ai in energy management

AI in Energy Management: Revolutionizing Efficiency and Sustainability

Author: Dr. Anya Sharma, PhD, Senior Research Scientist at the National Renewable Energy Laboratory (NREL) and Adjunct Professor of Electrical Engineering at Colorado School of Mines.

Publisher: IEEE Power & Energy Magazine – A leading publication in the field of power systems engineering and a trusted source for information on advancements in AI and energy.

Editor: Dr. David Lee, Editor-in-Chief of IEEE Power & Energy Magazine, with over 25 years of experience in power systems and smart grid technologies.

Keywords: AI in energy management, artificial intelligence, energy efficiency, renewable energy integration, smart grids, predictive maintenance, demand-side management, energy optimization, machine learning, deep learning, power system optimization, energy forecasting, sustainability.

Abstract: This article explores the transformative impact of AI in energy management, examining its applications across diverse sectors and highlighting the key benefits and challenges involved. We delve into the various AI techniques employed, their practical implementations, and future prospects for this rapidly evolving field.

1. Introduction: The Rise of AI in Energy Management

The global energy landscape is undergoing a dramatic shift. The increasing demand for energy, coupled with the urgent need for decarbonization, necessitates innovative solutions for efficient and sustainable energy management. Artificial intelligence (AI), with its ability to analyze vast datasets, identify patterns, and make predictions, is emerging as a crucial technology in addressing these challenges. AI in energy management is no longer a futuristic concept; it's a rapidly developing reality transforming how we generate, distribute, and consume energy. This article provides a comprehensive overview of AI's role in energy management, covering its diverse applications, benefits, and future implications.

2. AI Techniques in Energy Management

Several AI techniques are being leveraged to optimize energy systems. These include:

Machine Learning (ML): ML algorithms, particularly supervised and unsupervised learning, are widely used for predictive maintenance, energy forecasting, and demand-side management. Supervised learning models predict energy consumption based on historical data, while unsupervised learning identifies patterns and anomalies in energy usage.

Deep Learning (DL): DL, a subset of ML, excels at handling complex, high-dimensional datasets. It's particularly useful for analyzing sensor data from smart grids, improving energy forecasting accuracy, and optimizing renewable energy integration. Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are commonly employed in these applications.

Reinforcement Learning (RL): RL allows AI agents to learn optimal control strategies through trial and error. It's proving valuable in optimizing energy distribution in smart grids, managing energy storage systems, and controlling microgrids.

Natural Language Processing (NLP): NLP is increasingly used to analyze textual data related to energy markets, customer feedback, and regulatory documents, providing valuable insights for decision-making in energy management.

3. Applications of AI in Energy Management

AI in energy management finds applications across various stages of the energy lifecycle:

Energy Generation: AI optimizes power plant operations, predicting equipment failures and maximizing generation efficiency. For renewable energy sources like solar and wind, AI improves forecasting accuracy, enabling better grid integration and reducing reliance on fossil fuels.

Energy Transmission and Distribution: AI-powered smart grids enhance grid stability, improve fault detection and localization, and enable better load balancing. This leads to reduced energy losses and improved grid resilience. AI in energy management also helps optimize the placement and sizing of new infrastructure.

Energy Consumption: AI-driven demand-side management (DSM) programs personalize energy consumption feedback, allowing customers to reduce their energy usage and lower their bills. AI also optimizes building energy management systems (BEMS), minimizing energy waste in commercial and residential buildings.

Predictive Maintenance: AI algorithms analyze sensor data from energy infrastructure to predict potential equipment failures, enabling proactive maintenance and minimizing downtime. This reduces operational costs and improves system reliability. AI in energy management is crucial for maintaining the integrity of the entire energy supply chain.

4. Benefits of AI in Energy Management

The adoption of AI in energy management offers numerous benefits:

Improved Efficiency: AI optimizes energy generation, transmission, and consumption, leading to significant energy savings and reduced operational costs.

Enhanced Reliability: AI improves grid stability, reduces outages, and enables proactive maintenance, leading to a more reliable energy supply.

Increased Sustainability: AI facilitates the integration of renewable energy sources, reducing reliance on fossil fuels and minimizing carbon emissions. AI in energy management contributes significantly to environmental protection goals.

Better Grid Management: AI enables a more intelligent and responsive grid, capable of adapting to fluctuating energy demand and integrating diverse energy sources.

Reduced Costs: AI-driven automation and optimization reduce operational costs, maintenance expenses, and energy waste.

5. Challenges and Considerations in AI Adoption

Despite its significant potential, the widespread adoption of AI in energy management faces several challenges:

Data Availability and Quality: AI algorithms require large, high-quality datasets for effective training. Acquiring and processing this data can be challenging and expensive.

Cybersecurity Risks: The increased reliance on interconnected systems and data sharing raises concerns about cybersecurity vulnerabilities.

Computational Resources: Training and deploying advanced AI models require substantial computational resources.

Integration Complexity: Integrating AI into existing energy management systems can be complex and require significant expertise.

Regulatory Frameworks: The development of appropriate regulatory frameworks to govern the use of AI in energy management is crucial.

6. Future Trends in AI in Energy Management

The future of AI in energy management is bright. We can expect to see:

Increased use of edge computing: Processing data closer to the source will improve responsiveness and reduce latency.

More sophisticated AI models: The development of more advanced algorithms will further enhance forecasting accuracy and optimization capabilities.

Greater integration of AI across the energy value chain: AI will be used to coordinate various aspects of energy management, from generation to consumption.

Wider adoption of digital twins: Virtual representations of energy systems will facilitate testing and optimization of AI-driven strategies.

7. Conclusion

AI in energy management is rapidly transforming the energy sector, offering unprecedented opportunities to improve efficiency, enhance reliability, and achieve sustainability goals. While challenges remain, the benefits of AI are undeniable. As AI technologies continue to advance and costs decrease, we can expect to see even wider adoption across the energy industry, leading to a more sustainable and resilient energy future. The ongoing development and implementation of AI in energy management are pivotal in addressing the global energy challenges of today and tomorrow.

FAQs

What is the most important application of AI in energy management? While all applications are crucial, predictive maintenance and improved energy forecasting stand out due to their direct impact on cost savings, reliability, and sustainability.

How secure is AI in energy management systems? Cybersecurity is a major concern. Robust security protocols, including encryption and intrusion detection systems, are crucial to mitigate risks.

What are the ethical implications of AI in energy management? Ethical considerations include data privacy, algorithmic bias, and job displacement. Careful planning and responsible implementation are essential.

What are the main obstacles to wider AI adoption in energy management? High upfront costs, data scarcity, and integration complexities are primary barriers.

How does AI contribute to renewable energy integration? AI improves forecasting of renewable energy generation, enabling better grid management and reducing the need for backup power from fossil fuels.

Can AI help reduce energy consumption in buildings? Yes, AI-powered building management systems (BEMS) optimize heating, cooling, and lighting, significantly reducing energy waste.

What type of data is most valuable for AI in energy management? Sensor data from smart meters, power plants, and other infrastructure is essential, as are historical energy consumption and weather data.

What is the future of jobs in the energy sector with the rise of AI? While some jobs may be automated, new roles focused on AI development, implementation, and management will emerge. Upskilling and reskilling initiatives will be crucial.

How can governments promote the adoption of AI in energy management? Governments can incentivize investment in AI technologies, support research and development, and establish clear regulatory frameworks.

AI-Driven Predictive Maintenance in Power Plants: This article focuses on the application of AI for predicting equipment failures in power plants, reducing downtime and maintenance costs.

Optimizing Renewable Energy Integration with AI: Explores how AI algorithms enhance the integration of solar and wind power into the electricity grid, improving grid stability and reliability.

AI for Demand-Side Management in Smart Homes: Discusses the use of AI to personalize energy consumption feedback to homeowners, helping them reduce energy usage and save money.

The Role of Machine Learning in Energy Forecasting: Examines different machine learning techniques employed for accurate energy demand and renewable energy generation forecasting.

Cybersecurity Challenges in AI-Enabled Energy Systems: Addresses the critical security concerns associated with the increased reliance on interconnected systems in AI-powered energy management.

Economic Impacts of AI Adoption in the Energy Sector: Analyzes the potential economic benefits of AI implementation in the energy sector, including cost savings and increased efficiency.

AI and the Future of Energy Storage Management: Investigates the use of AI for optimizing the performance and management of energy storage systems, improving grid stability and reliability.

Ethical Considerations in the Development and Deployment of AI in Energy: Discusses ethical dilemmas and challenges related to data privacy, algorithmic bias, and job displacement in the context of AI in energy management.

Case Studies: Successful Implementations of AI in Energy Management: Presents real-world examples of AI applications in various energy sectors, highlighting successful implementations and lessons learned.

ai in energy management: Applications of AI and IOT in Renewable Energy Rabindra Nath Shaw, Ankush Ghosh, Saad Mekhilef, Valentina Emilia Balas, 2022-02-09 Applications of AI and IOT in Renewable Energy provides a future vision of unexplored areas and applications for Artificial Intelligence and Internet of Things in sustainable energy systems. The ideas presented in this book are backed up by original, unpublished technical research results covering topics like smart solar energy systems, intelligent dc motors and energy efficiency study of electric vehicles. In all these areas and more, applications of artificial intelligence methods, including artificial neural networks, genetic algorithms, fuzzy logic and a combination of the above in hybrid systems are included. This book is designed to assist with developing low cost, smart and efficient solutions for renewable energy systems and is intended for researchers, academics and industrial communities engaged in the study and performance prediction of renewable energy systems. - Includes future applications of AI and IOT in renewable energy - Based on case studies to give each chapter real-life context - Provides advances in renewable energy using AI and IOT with technical detail and data
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ai in energy management: Predictive Modelling for Energy Management and Power Systems Engineering Ravinesh Deo, Pijush Samui, Sanjiban Sekhar Roy, 2020-09-30 Predictive Modeling for Energy Management and Power Systems Engineering introduces readers to the cutting-edge use of big data and large computational infrastructures in energy demand estimation and power management systems. The book supports engineers and scientists who seek to become familiar with advanced optimization techniques for power systems designs, optimization techniques and algorithms for consumer power management, and potential applications of machine learning and artificial intelligence in this field. The book provides modeling theory in an easy-to-read format, verified with on-site models and case studies for specific geographic regions and complex consumer markets. - Presents advanced optimization techniques to improve existing energy demand system - Provides data-analytic models and their practical relevance in proven case studies - Explores novel developments in machine-learning and artificial intelligence applied in energy management - Provides modeling theory in an easy-to-read format
ai in energy management: Intelligent Renewable Energy Systems Neeraj Priyadarshi, Akash Kumar Bhoi, Sanjeevikumar Padmanaban, S. Balamurugan, Jens Bo Holm-Nielsen, 2022-01-19 INTELLIGENT RENEWABLE ENERGY SYSTEMS This collection of papers on artificial intelligence and other methods for improving renewable energy systems, written by industry experts, is a reflection of the state of the art, a must-have for engineers, maintenance personnel, students, and anyone else wanting to stay abreast with current energy systems concepts and technology. Renewable energy is one of the most important subjects being studied, researched, and advanced in today’s world. From a macro level, like the stabilization of the entire world’s economy, to the micro level, like how you are going to heat or cool your home tonight, energy, specifically renewable energy, is on the forefront of the discussion. This book illustrates modelling, simulation, design and control of renewable energy systems employed with recent artificial intelligence (AI) and optimization techniques for performance enhancement. Current renewable energy sources have less power conversion efficiency because of its intermittent and fluctuating behavior. Therefore, in this regard, the recent AI and optimization techniques are able to deal with data ambiguity, noise, imprecision, and nonlinear behavior of renewable energy sources more efficiently compared to classical soft computing techniques. This book provides an extensive analysis of recent state of the art AI and optimization techniques applied to green energy systems. Subsequently, researchers, industry persons, undergraduate and graduate students involved in green energy will greatly benefit from this comprehensive volume, a must-have for any library. Audience Engineers, scientists, managers, researchers, students, and other professionals working in the field of renewable energy.
ai in energy management: Artificial Intelligence for Renewable Energy Systems Ajay Kumar Vyas, S. Balamurugan, Kamal Kant Hiran, Harsh S. Dhiman, 2022-03-02 ARTIFICIAL INTELLIGENCE FOR RENEWABLE ENERGY SYSTEMS Renewable energy systems, including solar, wind, biodiesel, hybrid energy, and other relevant types, have numerous advantages compared to their conventional counterparts. This book presents the application of machine learning and deep learning techniques for renewable energy system modeling, forecasting, and optimization for efficient system design. Due to the importance of renewable energy in today’s world, this book was designed to enhance the reader’s knowledge based on current developments in the field. For instance, the extraction and selection of machine learning algorithms for renewable energy systems, forecasting of wind and solar radiation are featured in the book. Also highlighted are intelligent data, renewable energy informatics systems based on supervisory control and data acquisition (SCADA); and intelligent condition monitoring of solar and wind energy systems. Moreover, an AI-based system for real-time decision-making for renewable energy systems is presented; and also demonstrated is the prediction of energy consumption in green buildings using machine learning. The chapter authors also provide both experimental and real datasets with great potential in the renewable energy sector, which apply machine learning (ML) and deep learning (DL) algorithms that will be helpful for economic and environmental forecasting of the renewable energy business. Audience The primary target audience includes research scholars, industry engineers, and graduate students working in renewable energy, electrical engineering, machine learning, information & communication technology.
ai in energy management: Artificial Intelligence and Heuristics for Smart Energy Efficiency in Smart Cities Mustapha Hatti, 2021-11-24 This book emphasizes the role of micro-grid systems and connected networks for the strategic storage of energy through the use of information and communication techniques, big data, the cloud, and meta-heuristics to support the greed for artificial intelligence techniques in data and the implementation of global strategies to meet the challenges of the city in the broad sense. The intelligent management of renewable energy in the context of the energy transition requires the use of techniques and tools based on artificial intelligence (AI) to overcome the challenges of the intermittence of resources and the cost of energy. The advent of the smart city makes an increased call for the integration of artificial intelligence and heuristics to meet the challenge of the increasing migration of populations to the city, in order to ensure food, energy, and environmental security of the citizen of the city and his well-being. This book is intended for policymakers, academics, practitioners, and students. Several real cases are exposed throughout the book to illustrate the concepts and methods of the networks and systems presented. This book proposes the development of new technological innovations—mainly ICT—the concept of “Smart City” appears as a means of achieving more efficient and sustainable cities. The overall goal of the book is to develop a comprehensive framework to help public and private stakeholders make informed decisions on smart city investment strategies and develop skills for assessment and prioritization, including resolution of difficulties with deployment and reproducibility.
ai in energy management: Artificial Intelligence-Based Energy Management Systems for Smart Microgrids Baseem Khan, Sanjeevikumar Padmanaban, Hassan Haes Alhelou, Om Prakash Mahela, S. Rajkumar, 2022-06-07 Modeling and optimization of energy management systems for micro- and mini-grids play an important role in the fields of energy generation dispatch, system operation, protection coordination, power quality issues, and peak demand conflict with grid security. This comprehensive reference text provides an in-depth insight into these topics. This text discusses the use of meta-heuristic and artificial intelligence algorithms for developing energy management systems with energy use prediction for mini- and microgrid systems. It covers important concepts including modeling of microgrid and energy management systems, optimal protection coordination-based microgrid energy management, optimal energy dispatch with energy management systems, and peak demand management with energy management systems. Key Features: Presents a comprehensive discussion of mini- and microgrid concepts Discusses AC and DC microgrid modeling in detail Covers optimization of mini- and microgrid systems using AI and meta-heuristic techniques Provides MATLAB®-based simulations on a mini- and microgrid Comprehensively discussing concepts of microgrids with the help of software-based simulations, this text will be useful as a reference text for graduate students and professionals in the fields of electrical engineering, electronics and communication engineering, renewable energy, and clean technology.
ai in energy management: Sustainable Developments by Artificial Intelligence and Machine Learning for Renewable Energies Krishna Kumar, Ram Shringar Rao, Omprakash Kaiwartya, Shamim Kaiser, Sanjeevikumar Padmanaban, 2022-03-18 Sustainable Developments by Artificial Intelligence and Machine Learning for Renewable Energies analyzes the changes in this energy generation shift, including issues of grid stability with variability in renewable energy vs. traditional baseload energy generation. Providing solutions to current critical environmental, economic and social issues, this book comprises various complex nonlinear interactions among different parameters to drive the integration of renewable energy into the grid. It considers how artificial intelligence and machine learning techniques are being developed to produce more reliable energy generation to optimize system performance and provide sustainable development. As the use of artificial intelligence to revolutionize the energy market and harness the potential of renewable energy is essential, this reference provides practical guidance on the application of renewable energy with AI, along with machine learning techniques and capabilities in design, modeling and for forecasting performance predictions for the optimization of renewable energy systems. It is targeted at researchers, academicians and industry professionals working in the field of renewable energy, AI, machine learning, grid Stability and energy generation. - Covers the best-performing methods and approaches for designing renewable energy systems with AI integration in a real-time environment - Gives advanced techniques for monitoring current technologies and how to efficiently utilize the energy grid spectrum - Addresses the advanced field of renewable generation, from research, impact and idea development of new applications
ai in energy management: Artificial Intelligence and Renewables Towards an Energy Transition Mustapha Hatti, 2020-12-18 This proceedings book emphasizes adopting artificial intelligence-based and sustainable energy efficiency integrated with clear objectives, to involve researchers, students, and specialists in their development and implementation adequately in achieving objectives. The integration of artificial intelligence into renewable energetic systems would allow the rapid development of a knowledge-based economy suitable to the energy transition, while fully integrating the renewables into the global economy. This is how artificial intelligence has hand in by conceptualizing this transition and above all by saving time. The knowledge economy is valuated within the smart cities, which are fast becoming the favorite places where the energy transition will take place efficiently and intelligently by implementing integrated approaches to energy saving and energy supply and integrated urban approaches that go beyond individual interventions in buildings or transport modes using information and communication technologies.
ai in energy management: Smart Grid Janaka B. Ekanayake, Nick Jenkins, Kithsiri M. Liyanage, Jianzhong Wu, Akihiko Yokoyama, 2012-02-23 Electric power systems worldwide face radical transformation with the need to decarbonise electricity supply, replace ageing assets and harness new information and communication technologies (ICT). The Smart Grid uses advanced ICT to control next generation power systems reliably and efficiently. This authoritative guide demonstrates the importance of the Smart Grid and shows how ICT will extend beyond transmission voltages to distribution networks and customer-level operation through Smart Meters and Smart Homes. Smart Grid Technology and Applications: Clearly unravels the evolving Smart Grid concept with extensive illustrations and practical examples. Describes the spectrum of key enabling technologies required for the realisation of the Smart Grid with worked examples to illustrate the applications. Enables readers to engage with the immediate development of the power system and take part in the debate over the future Smart Grid. Introduces the constituent topics from first principles, assuming only a basic knowledge of mathematics, circuits and power systems. Brings together the expertise of a highly experienced and international author team from the UK, Sri Lanka, China and Japan. Electrical, electronics and computer engineering researchers, practitioners and consultants working in inter-disciplinary Smart Grid RD&D will significantly enhance their knowledge through this reference. The tutorial style will greatly benefit final year undergraduate and master’s students as the curriculum increasing focuses on the breadth of technologies that contribute to Smart Grid realisation.
ai in energy management: Artificial Intelligence of Things for Smart Green Energy Management Sarah El Himer, Mariyam Ouaissa, Abdulrahman A. A. Emhemed, Mariya Ouaissa, Zakaria Boulouard, 2022-06-23 This book is intended to assist in the development of smart and efficient green energy solutions. It introduces energy systems, power generation, and power demands which able to minimise generation costs, power loss or environmental effects. It proposes cutting-edge solutions and approaches based on recent technologies such as intelligent renewable energy systems (wind and solar). These solutions, applied to different sectors, can provide a solid basis for meeting the needs of both developed and developing countries. The book provides a collection of contributions including new techniques, methods, algorithms, practical solutions and models based on applying artificial intelligence and the Internet of things into green energy management systems. It provides a comprehensive reference for researchers, scholars and industry in the field of green energy and computational intelligence.
ai in energy management: Renewable Energy for Smart and Sustainable Cities Mustapha Hatti, 2018-11-23 This book features cutting-edge research presented at the second international conference on Artificial Intelligence in Renewable Energetic Systems, IC-AIRES2018, held on 24–26 November 2018, at the High School of Commerce, ESC-Koléa in Tipaza, Algeria. Today, the fundamental challenge of integrating renewable energies into the design of smart cities is more relevant than ever. While based on the advent of big data and the use of information and communication technologies, smart cities must now respond to cross-cutting issues involving urban development, energy and environmental constraints; further, these cities must also explore how they can integrate more sustainable energies. Sustainable energies are a major determinant of smart cities’ longevity. From an environmental and technological standpoint, these energies offer an optimal power supply to the electric network while creating significantly less pollution. This requires flexibility, i.e., the availability of supply and demand. The end goal of any smart city is to improve the quality of life for all citizens (both in the city and in the countryside) in a way that is sustainable and respectful of the environment. This book encourages the reader to engage in the preservation of our environment, every moment, every day, so as to help build a clean and healthy future, and to think of the future generations who will one day inherit our planet. Further, it equips those whose work involves energy systems and those engaged in modelling artificial intelligence to combine their expertise for the benefit of the scientific community and humanity as a whole.
ai in energy management: Decision Making Using AI in Energy and Sustainability Gülgün Kayakutlu, M. Özgür Kayalica, 2023-10-16 Artificial intelligence (AI) has a huge impact on science and technology, including energy, where access to resources has been a source of geopolitical conflicts. AI can predict the demand and supply of renewable energy, optimize efficiency in energy systems, and improve the management of natural energy resources, among other things. This book explores the use of AI tools for improving the management of energy systems and providing sustainability with smart cities, smart facilities, smart buildings, smart transportation, and smart houses. Featuring research from International Federation for Information Processing's (IFIP) “AI in Energy and Sustainability” working group, this book provides new models and algorithms for AI applications in energy and sustainability fields. Any short-term, mid-term and long-term forecasting, optimization models, trend foresights and prescriptions based on scenarios are studied in the energy world and the smart systems for sustainability. The contents of this book are valuable for energy researchers, academics, scholars, practitioners and policy makers.
ai in energy management: Machine Learning Applications for Intelligent Energy Management Haris Doukas, Vangelis Marinakis, Elissaios Sarmas, 2024-02-02 As carbon dioxide (CO2) emissions and other greenhouse gases constantly rise and constitute the main contributor to climate change, temperature rise and global warming, artificial intelligence, big data, Internet of things, and blockchain technologies are enlisted to help enforce energy transition and transform the entire energy sector. The book at hand presents state-of-the-art developments in artificial intelligence-empowered analytics of energy data and artificial intelligence-empowered application development. Topics covered include a presentation of the various stakeholders in the energy sector and their corresponding required analytic services, such as state-of-the-art machine learning, artificial intelligence, and optimization models and algorithms tailored for a series of demanding energy problems and aiming at providing optimal solutions under specific constraints. Professors, researchers, scientists, engineers, and students in energy sector-related disciplines are expected to be inspired and benefit from this book, along with readers from other disciplines wishing to learn more about this exciting new field of research.
ai in energy management: Trends and Challenges in Maritime Energy Management Aykut I. Ölçer, Momoko Kitada, Dimitrios Dalaklis, Fabio Ballini, 2018-05-03 This book provides an overview of contemporary trends and challenges in maritime energy management (MEM). Coordinated action is necessary to achieve a low carbon and energy-efficient maritime future, and MEM is the prevailing framework aimed at reducing greenhouse gas emissions resulting from maritime industry activities. The book familiarizes readers with the status quo in the field, and paves the way for finding solutions to perceived challenges. The 34 contributions cover six important aspects: regulatory framework; energy-efficient ship design; energy efficient ship and port operation; economic and social dimensions; alternative fuels and wind-assisted ship propulsion; and marine renewable energy. This pioneering work is intended for researchers and academics as well as practitioners and policymakers involved in this important field.
ai in energy management: Guide to Energy Management, Fifth Edition Barney L. Capehart, Wayne C. Turner, William J. Kennedy, 2006-01-18 Written by three of the most respected energy professionals in the industry, this fifth edition of a bestseller is an energy manager's guide to the most important areas of energy cost cutting. It examines the core objectives of energy management and illustrates the latest and most effective strategies, techniques, and tools for improving lighting efficiency, combustion processes, steam generation/distribution, and industrial waste reutilization. The book thoroughly brings up to date such topics as energy system management, energy auditing, rate structures, economic evaluation, HVAC optimization, control systems and computers, process energy, renewable energy, and industrial water management.
ai in energy management: Sustainable Smart Cities Marta Peris-Ortiz, Dag R. Bennett, Diana Pérez-Bustamante Yábar, 2016-10-05 This volume provides the most current research on smart cities. Specifically, it focuses on the economic development and sustainability of smart cities and examines how to transform older industrial cities into sustainable smart cities. It aims to identify the role of the following elements in the creation and management of smart cities:• Citizen participation and empowerment • Value creation mechanisms • Public administration• Quality of life and sustainability• Democracy• ICT• Private initiatives and entrepreneurship Regardless of their size, all cities are ultimately agglomerations of people and institutions. Agglomeration economies make it possible to attain minimum efficiencies of scale in the organization and delivery of services. However, the economic benefits do not constitute the main advantage of a city. A city’s status rests on three dimensions: (1) political impetus, which is the result of citizens’ participation and the public administration’s agenda; (2) applications derived from technological advances (especially in ICT); and (3) cooperation between public and private initiatives in business development and entrepreneurship. These three dimensions determine which resources are necessary to create smart cities. But a smart city, ideal in the way it channels and resolves technological, social and economic-growth issues, requires many additional elements to function at a high-performance level, such as culture (an environment that empowers and engages citizens) and physical infrastructure designed to foster competition and collaboration, encourage new ideas and actions, and set the stage for new business creation. Featuring contributions with models, tools and cases from around the world, this book will be a valuable resource for researchers, students, academics, professionals and policymakers interested in smart cities.
ai in energy management: Proceedings of ICRIC 2019 Pradeep Kumar Singh, Arpan Kumar Kar, Yashwant Singh, Maheshkumar H. Kolekar, Sudeep Tanwar, 2019-11-21 This book presents high-quality, original contributions (both theoretical and experimental) on software engineering, cloud computing, computer networks & internet technologies, artificial intelligence, information security, and database and distributed computing. It gathers papers presented at ICRIC 2019, the 2nd International Conference on Recent Innovations in Computing, which was held in Jammu, India, in March 2019. This conference series represents a targeted response to the growing need for research that reports on and assesses the practical implications of IoT and network technologies, AI and machine learning, cloud-based e-Learning and big data, security and privacy, image processing and computer vision, and next-generation computing technologies.
ai in energy management: Watershed Management and Applications of AI Sandeep Samantaray, Abinash Sahoo, Dillip K. Ghose, 2021-05-16 Land use and water resources are two major environmental issues which necessitate conservation, management, and maintenance practices through the use of various engineering techniques. Water scientists and environmental engineers must address the various aspects of flood control, soil conservation, rainfall-runoff processes, and groundwater hydrology. Watershed Management and Applications of AI provides the necessary principles of hydrology to provide practical strategies useful for the planning, design, and management of watersheds. The book also synthesizes novel new approaches, such as hydrological applications of machine learning using neural networks to predict runoff and using artificial intelligence for the prediction of groundwater fluctuations. Features: Presents hydrologic analysis and design along with soil conservation practices through proper watershed management techniques Provides analysis of land erosion and sediment transport in watersheds from small to large scale Includes estimations for runoff using different methodologies with systematic approaches for each Discusses water harvesting and development of water yield catchments This book will be a valuable resource for students in hydrology courses, environmental consultants, water resource engineers, and researchers in related water science and engineering fields.
ai in energy management: Energy Management Principles Craig B. Smith, Kelly E. Parmenter, 2015-11-06 Energy Management Principles: Applications, Benefits, Savings, Second Edition is a comprehensive guide to the fundamental principles and systematic processes of maintaining and improving energy efficiency and reducing waste. Fully revised and updated with analysis of world energy utilization, incentives and utility rates, and new content highlighting how energy efficiency can be achieved through 1 of 16 outlined principles and programs, the book presents cost effective analysis, case studies, global examples, and guidance on building and site auditing. This fully revised edition provides a theoretical basis for conservation, as well as the avenues for its application, and by doing so, outlines the potential for cost reductions through an analysis of inefficiencies. - Provides extensive coverage of all major fundamental energy management principles - Applies general principles to all major components of energy use, such as HVAC, electrical end use and lighting, and transportation - Describes how to initiate an energy management program for a building, a process, a farm or an industrial facility
ai in energy management: Distributed Energy Management of Electrical Power Systems Yinliang Xu, Wei Zhang, Wenxin Liu, Wen Yu, 2021-01-13 Go in-depth with this comprehensive discussion of distributed energy management Distributed Energy Management of Electrical Power Systems provides the most complete analysis of fully distributed control approaches and their applications for electric power systems available today. Authored by four respected leaders in the field, the book covers the technical aspects of control, operation management, and optimization of electric power systems. In each chapter, the book covers the foundations and fundamentals of the topic under discussion. It then moves on to more advanced applications. Topics reviewed in the book include: System-level coordinated control Optimization of active and reactive power in power grids The coordinated control of distributed generation, elastic load and energy storage systems Distributed Energy Management incorporates discussions of emerging and future technologies and their potential effects on electrical power systems. The increased impact of renewable energy sources is also covered. Perfect for industry practitioners and graduate students in the field of power systems, Distributed Energy Management remains the leading reference for anyone with an interest in its fascinating subject matter.
ai in energy management: Energy Management in Plastics Processing Robin Kent, 2018-07-06 Energy Management in Plastics Processing: Strategies, Targets, Techniques, and Tools, Third Edition, addresses energy benchmarking and site surveys, how to understand energy supplies and bills, and how to measure and manage energy usage and carbon footprinting. The book's approach highlights the need to reduce the kWh/kg of materials processed and the resulting permanent reductions in consumption and costs. Every topic is covered in a 2-page spread, providing the reader with clear actions and key tips for success. This revised third edition covers new developments in energy management, power supply considerations, automation, assembly operations, water footprinting, and transport considerations, and more. Users will find a practical workbook that not only shows how to reduce energy consumption in all the major plastics shaping processes (moulding, extrusion, forming), but also provides tactics that will benefit other locations in plants (e.g. in factory services and nonmanufacturing areas). - Enables plastics processors in their desire to institute an effective energy management system, both in processing and elsewhere in the plant - Provides a holistic perspective, shining a light on areas where energy management methods may have not been previously considered - Acts as a roadmap to help companies move towards improved sustainability and cost savings
ai in energy management: HPC, Big Data, and AI Convergence Towards Exascale Olivier Terzo, Jan Martinovič, 2022-01-13 HPC, Big Data, AI Convergence Towards Exascale provides an updated vision on the most advanced computing, storage, and interconnection technologies, that are at basis of convergence among the HPC, Cloud, Big Data, and artificial intelligence (AI) domains. Through the presentation of the solutions devised within recently founded H2020 European projects, this book provides an insight on challenges faced by integrating such technologies and in achieving performance and energy efficiency targets towards the exascale level. Emphasis is given to innovative ways of provisioning and managing resources, as well as monitoring their usage. Industrial and scientific use cases give to the reader practical examples of the needs for a cross-domain convergence. All the chapters in this book pave the road to new generation of technologies, support their development and, in addition, verify them on real-world problems. The readers will find this book useful because it provides an overview of currently available technologies that fit with the concept of unified Cloud-HPC-Big Data-AI applications and presents examples of their actual use in scientific and industrial applications.
ai in energy management: Data Center Handbook Hwaiyu Geng, 2014-12-22 Provides the fundamentals, technologies, and best practices in designing, constructing and managing mission critical, energy efficient data centers Organizations in need of high-speed connectivity and nonstop systems operations depend upon data centers for a range of deployment solutions. A data center is a facility used to house computer systems and associated components, such as telecommunications and storage systems. It generally includes multiple power sources, redundant data communications connections, environmental controls (e.g., air conditioning, fire suppression) and security devices. With contributions from an international list of experts, The Data Center Handbook instructs readers to: Prepare strategic plan that includes location plan, site selection, roadmap and capacity planning Design and build green data centers, with mission critical and energy-efficient infrastructure Apply best practices to reduce energy consumption and carbon emissions Apply IT technologies such as cloud and virtualization Manage data centers in order to sustain operations with minimum costs Prepare and practice disaster reovery and business continuity plan The book imparts essential knowledge needed to implement data center design and construction, apply IT technologies, and continually improve data center operations.
ai in energy management: AI in Manufacturing and Green Technology Sambit Kumar Mishra, Zdzislaw Polkowski, Samarjeet Borah, Ritesh Dash, 2020-10-20 This book focuses on environmental sustainability by employing elements of engineering and green computing through modern educational concepts and solutions. It visualizes the potential of artificial intelligence, enhanced by business activities and strategies for rapid implementation, in manufacturing and green technology. This book covers utilization of renewable resources and implementation of the latest energy-generation technologies. It discusses how to save natural resources from depletion and illustrates facilitation of green technology in industry through usage of advanced materials. The book also covers environmental sustainability and current trends in manufacturing. The book provides the basic concepts of green technology, along with the technology aspects, for researchers, faculty, and students.
ai in energy management: Hybrid Electric Vehicles Simona Onori, Lorenzo Serrao, Giorgio Rizzoni, 2015-12-16 This SpringerBrief deals with the control and optimization problem in hybrid electric vehicles. Given that there are two (or more) energy sources (i.e., battery and fuel) in hybrid vehicles, it shows the reader how to implement an energy-management strategy that decides how much of the vehicle’s power is provided by each source instant by instant. Hybrid Electric Vehicles: •introduces methods for modeling energy flow in hybrid electric vehicles; •presents a standard mathematical formulation of the optimal control problem; •discusses different optimization and control strategies for energy management, integrating the most recent research results; and •carries out an overall comparison of the different control strategies presented. Chapter by chapter, a case study is thoroughly developed, providing illustrative numerical examples that show the basic principles applied to real-world situations. The brief is intended as a straightforward tool for learning quickly about state-of-the-art energy-management strategies. It is particularly well-suited to the needs of graduate students and engineers already familiar with the basics of hybrid vehicles but who wish to learn more about their control strategies.
ai in energy management: Regulating Artificial Intelligence in Industry Damian M. Bielicki, 2021-12-24 Artificial Intelligence (AI) has augmented human activities and unlocked opportunities for many sectors of the economy. It is used for data management and analysis, decision making, and many other aspects. As with most rapidly advancing technologies, law is often playing a catch up role so the study of how law interacts with AI is more critical now than ever before. This book provides a detailed qualitative exploration into regulatory aspects of AI in industry. Offering a unique focus on current practice and existing trends in a wide range of industries where AI plays an increasingly important role, the work contains legal and technical analysis performed by 15 researchers and practitioners from different institutions around the world to provide an overview of how AI is being used and regulated across a wide range of sectors, including aviation, energy, government, healthcare, legal, maritime, military, music, and others. It addresses the broad range of aspects, including privacy, liability, transparency, justice, and others, from the perspective of different jurisdictions. Including a discussion of the role of AI in industry during the Covid-19 pandemic, the chapters also offer a set of recommendations for optimal regulatory interventions. Therefore, this book will be of interest to academics, students and practitioners interested in technological and regulatory aspects of AI.
ai in energy management: Enterprise Artificial Intelligence Transformation Rashed Haq, 2020-06-10 Enterprise Artificial Intelligence Transformation AI is everywhere. From doctor's offices to cars and even refrigerators, AI technology is quickly infiltrating our daily lives. AI has the ability to transform simple tasks into technological feats at a human level. This will change the world, plain and simple. That's why AI mastery is such a sought-after skill for tech professionals. Author Rashed Haq is a subject matter expert on AI, having developed AI and data science strategies, platforms, and applications for Publicis Sapient's clients for over 10 years. He shares that expertise in the new book, Enterprise Artificial Intelligence Transformation. The first of its kind, this book grants technology leaders the insight to create and scale their AI capabilities and bring their companies into the new generation of technology. As AI continues to grow into a necessary feature for many businesses, more and more leaders are interested in harnessing the technology within their own organizations. In this new book, leaders will learn to master AI fundamentals, grow their career opportunities, and gain confidence in machine learning. Enterprise Artificial Intelligence Transformation covers a wide range of topics, including: Real-world AI use cases and examples Machine learning, deep learning, and slimantic modeling Risk management of AI models AI strategies for development and expansion AI Center of Excellence creating and management If you're an industry, business, or technology professional that wants to attain the skills needed to grow your machine learning capabilities and effectively scale the work you're already doing, you'll find what you need in Enterprise Artificial Intelligence Transformation.
ai in energy management: Blockchain-Based Smart Grids Miadreza Shafie-khah, 2020-04-30 Blockchain-Based Smart Grids presents emerging applications of blockchain in electrical system and looks to future developments in the use of blockchain technology in the energy market. Rapid growth of renewable energy resources in power systems and significant developments in the telecommunication systems has resulted in new market designs being employed to cover unpredictable and distributed generation of electricity. This book considers the marriage of blockchain and grid modernization, and discusses the transaction shifts in smart grids, from centralized to peer-to-peer structures. In addition, it addresses the effective application of these structures to speed up processes, resulting in more flexible electricity systems. Aimed at moving towards blockchain-based smart grids with renewable applications, this book is useful to researchers and practitioners in all sectors of smart grids, including renewable energy providers, manufacturers and professionals involved in electricity generation from renewable sources, grid modernization and smart grid applications.
ai in energy management: Handbook of Water and Energy Management in Food Processing Jiri Klemes, Robin Smith, Jin-Kuk Kim, 2008-06-30 Effective water and energy use in food processing is essential, not least for legislative compliance and cost reduction. This major volume reviews techniques for improvements in the efficiency of water and energy use as well as wastewater treatment in the food industry.Opening chapters provide an overview of key drivers for better management. Part two is concerned with assessing water and energy consumption and designing strategies for their reduction. These include auditing energy and water use, and modelling and optimisation tools for water minimisation. Part three reviews good housekeeping procedures, measurement and process control, and monitoring and intelligent support systems. Part four discusses methods to minimise energy consumption. Chapters focus on improvements in specific processes such as refrigeration, drying and heat recovery. Part five discusses water reuse and wastewater treatment in the food industry. Chapters cover water recycling, disinfection techniques, aerobic and anaerobic systems for treatment of wastewater. The final section concentrates on particular industry sectors including fresh meat and poultry, cereals, sugar, soft drinks, brewing and winemaking.With its distinguished editors and international team of contributors, Handbook of water and energy management in food processing is a standard reference for the food industry. - Provides an overview of key drivers for better management - Reviews techniques for improvements in efficiency of water and energy use and waste water treatment - Examines house keeping proceedures and measurement and process control
ai in energy management: Is AI Good for the Planet? Benedetta Brevini, 2021-10-14 Artificial intelligence (AI) is presented as a solution to the greatest challenges of our time, from global pandemics and chronic diseases to cybersecurity threats and the climate crisis. But AI also contributes to the climate crisis by running on technology that depletes scarce resources and by relying on data centres that demand excessive energy use. Is AI Good for the Planet? brings the climate crisis to the centre of debates around AI, exposing its environmental costs and forcing us to reconsider our understanding of the technology. It reveals why we should no longer ignore the environmental problems generated by AI. Embracing a green agenda for AI that puts the climate crisis at centre stage is our urgent priority. Engaging and passionately written, this book is essential reading for scholars and students of AI, environmental studies, politics, and media studies and for anyone interested in the connections between technology and the environment.
ai in energy management: Data Analytics and Artificial Intelligence for Predictive Maintenance in Smart Manufacturing Amit Kumar Tyagi, Shrikant Tiwari, Gulshan Soni, 2024-10-23 Today, in this smart era, data analytics and artificial intelligence (AI) play an important role in predictive maintenance (PdM) within the manufacturing industry. This innovative approach aims to optimize maintenance strategies by predicting when equipment or machinery is likely to fail so that maintenance can be performed just in time to prevent costly breakdowns. This book contains up-to-date information on predictive maintenance and the latest advancements, trends, and tools required to reduce costs and save time for manufacturers and industries. Data Analytics and Artificial Intelligence for Predictive Maintenance in Smart Manufacturing provides an extensive and in-depth exploration of the intersection of data analytics, artificial intelligence, and predictive maintenance in the manufacturing industry and covers fundamental concepts, advanced techniques, case studies, and practical applications. Using a multidisciplinary approach, this book recognizes that predictive maintenance in manufacturing requires collaboration among engineers, data scientists, and business professionals and includes case studies from various manufacturing sectors showcasing successful applications of predictive maintenance. The real-world examples explain the useful benefits and ROI achieved by organizations. The emphasis is on scalability, making it suitable for both small and large manufacturing operations, and readers will learn how to adapt predictive maintenance strategies to different scales and industries. This book presents resources and references to keep readers updated on the latest advancements, tools, and trends, ensuring continuous learning. Serving as a reference guide, this book focuses on the latest advancements, trends, and tools relevant to predictive maintenance and can also serve as an educational resource for students studying manufacturing, data science, or related fields.
ai in energy management: ISO 50001 Alan Field, 2019-11-28 ISO 50001 – A strategic guide to establishing an energy management system provides a practical but strategic overview for leadership teams of what an EnMS (energy management system) is and how implementing one can bring added value to an organisation.
ai in energy management: Ask a Manager Alison Green, 2018-05-01 From the creator of the popular website Ask a Manager and New York’s work-advice columnist comes a witty, practical guide to 200 difficult professional conversations—featuring all-new advice! There’s a reason Alison Green has been called “the Dear Abby of the work world.” Ten years as a workplace-advice columnist have taught her that people avoid awkward conversations in the office because they simply don’t know what to say. Thankfully, Green does—and in this incredibly helpful book, she tackles the tough discussions you may need to have during your career. You’ll learn what to say when • coworkers push their work on you—then take credit for it • you accidentally trash-talk someone in an email then hit “reply all” • you’re being micromanaged—or not being managed at all • you catch a colleague in a lie • your boss seems unhappy with your work • your cubemate’s loud speakerphone is making you homicidal • you got drunk at the holiday party Praise for Ask a Manager “A must-read for anyone who works . . . [Alison Green’s] advice boils down to the idea that you should be professional (even when others are not) and that communicating in a straightforward manner with candor and kindness will get you far, no matter where you work.”—Booklist (starred review) “The author’s friendly, warm, no-nonsense writing is a pleasure to read, and her advice can be widely applied to relationships in all areas of readers’ lives. Ideal for anyone new to the job market or new to management, or anyone hoping to improve their work experience.”—Library Journal (starred review) “I am a huge fan of Alison Green’s Ask a Manager column. This book is even better. It teaches us how to deal with many of the most vexing big and little problems in our workplaces—and to do so with grace, confidence, and a sense of humor.”—Robert Sutton, Stanford professor and author of The No Asshole Rule and The Asshole Survival Guide “Ask a Manager is the ultimate playbook for navigating the traditional workforce in a diplomatic but firm way.”—Erin Lowry, author of Broke Millennial: Stop Scraping By and Get Your Financial Life Together
ai in energy management: Introduction to Industrial Energy Efficiency Patrik Thollander, Magnus Karlsson, Patrik Rohdin, Johan Wollin, Jakob Rosenqvist, 2020-01-29 Introduction to Industrial Energy Efficiency: Energy Auditing, Energy Management, and Policy Issues offers a systemic overview of all key-aspects involved in improving industrial energy efficiency in various industry sectors. It is organized in three parts, each dealing with a particular perspective needed to form a complete view of related issues. Sections focus on energy auditing and improved energy efficiency of companies from a predominantly technical perspective, shed light on energy management and factors that hinder or drive the adoption of energy efficiency practices in the manufacturing industry, and explore energy efficiency policy instruments and how they are designed, implemented and evaluated. Practicing engineers in the field of energy efficiency, engineering and energy researchers coming into the field, and graduate students will find this book to be an invaluable reference on the fundamental knowledge they need to get started in this area. - Provides, in one volume, a comprehensive overview of energy systems efficiency and management that is applied to various industrial processes - Explores operational measures for improvement, including case studies from varying countries and sectors - Discusses the barriers to, and driving forces for, improving energy efficiency in industrial settings, including technical, behavioral, organizational and policy aspects
ai in energy management: Advanced Energy Management Yuekuan Zhou, 2025-10-01 Advanced Energy Management: Digitalization and AI for the Internet of Energy presents the latest advances in the fields of distributed renewable systems, advanced controls, and energy management of nonlinear energy behaviours. The book addresses the problems of energy resilience under extreme climate and extreme events and presents new applications for energy-efficient, low-carbon and energy-reliable cities. The book explains how energy management is used in multi-energy systems for improved power dispatch, fast response, dynamic ageing, techno-economic performance. The introduction provides a comprehensive overview of artificial intelligence applications for renewable energy systems. The subsequent sections delve into various aspects, such as the interconnection between climate change and multi-energy systems, the role of big data and digitalization in sustainable energy supply, the integration of renewable energy sources, and the application of machine learning in power forecasting, demand predictions, and energy storage. Following this, the book explains demand-side management and grid-response controls in integrated energy management systems (IEMSs), and energy management systems (EMSs) in integrated energy systems and power grid with artificial intelligence. Dedicated chapters review peer-to-peer and blockchain-based energy sharing and trading, dynamic pricing and decision making in distributed energy markets, as well as city-scale energy resilience and robustness with distributed energy systems. Finally, the ethical, regulatory, and policy considerations of applying artificial intelligence in energy systems and energy management are discussed, and detailed Sustainability and Environmental Impact Analyses are presented. Advanced Energy Management: Digitalization and AI for the Internet of Energy is an invaluable reference for students, researchers, and industry professionals seeking advanced energy management strategies, and will be of interest to anyone involved in carbon neutrality solutions.• Presents the latest developments and future trends in advanced energy management following sustainable energy supply-transmission-distribution with energy storage• Explains the fundamentals of how AI can facilitate a low-carbon transition through sustainable energy and storage• Includes case studies and real-world applications of advanced energy management with AI and digitalization
ai in energy management: Artificial Intelligence for Intelligent Systems Inam Ullah Khan, Mariya Ouaissa, Mariyam Ouaissa, Muhammad Fayaz, Rehmat Ullah, 2024-07-31 The aim of this book is to highlight the most promising lines of research, using new enabling technologies and methods based on AI/ML techniques to solve issues and challenges related to intelligent and computing systems. Intelligent computing easily collects data using smart technological applications like IoT-based wireless networks, digital healthcare, transportation, blockchain, 5.0 industry and deep learning for better decision making. AI enabled networks will be integrated in smart cities' concept for interconnectivity. Wireless networks will play an important role. The digital era of computational intelligence will change the dynamics and lifestyle of human beings. Future networks will be introduced with the help of AI technology to implement cognition in real-world applications. Cyber threats are dangerous to encode information from network. Therefore, AI-Intrusion detection systems need to be designed for identification of unwanted data traffic. This book: Provides a better understanding of artificial intelligence-based applications for future smart cities Presents a detailed understanding of artificial intelligence tools for intelligent technologies Showcases intelligent computing technologies in obtaining optimal solutions using artificial intelligence Discusses energy-efficient routing protocols using artificial intelligence for Flying ad-hoc networks (FANETs) Covers machine learning-based Intrusion detection system (IDS) for smart grid It is primarily written for senior undergraduate, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, and computer engineering.
ai in energy management: Willpower Roy F. Baumeister, John Tierney, 2011-09-01 One of the world's most esteemed and influential psychologists, Roy F. Baumeister, teams with New York Times science writer John Tierney to reveal the secrets of self-control and how to master it. Deep and provocative analysis of people's battle with temptation and masterful insights into understanding willpower: why we have it, why we don't, and how to build it. A terrific read. —Ravi Dhar, Yale School of Management, Director of Center for Customer Insights Pioneering research psychologist Roy F. Baumeister collaborates with New York Times science writer John Tierney to revolutionize our understanding of the most coveted human virtue: self-control. Drawing on cutting-edge research and the wisdom of real-life experts, Willpower shares lessons on how to focus our strength, resist temptation, and redirect our lives. It shows readers how to be realistic when setting goals, monitor their progress, and how to keep faith when they falter. By blending practical wisdom with the best of recent research science, Willpower makes it clear that whatever we seek—from happiness to good health to financial security—we won’t reach our goals without first learning to harness self-control.
ai in energy management: AI-Based Services for Smart Cities and Urban Infrastructure Lyu, Kangjuan, Hu, Min, Du, Juan, Sugumaran, Vijayan, 2020-09-04 Cities are the next frontier for artificial intelligence to permeate. As smart urban environments become possible, probable, and even preferred, artificial intelligence offers the chance for even further advancement through infrastructure and industry boosting. Opportunity overflows, but without thorough research to guide a complicated development and implementation process, urban environments can become disorganized and outright dangerous for citizens. AI-Based Services for Smart Cities and Urban Infrastructure is a collection of innovative research that explores artificial intelligence (AI) applications in urban planning. In addition, the book looks at how the internet of things and AI can work together to enable a real smart city and discusses state-of-the-art techniques in urban infrastructure design, construction, operation, maintenance, and management. While highlighting a broad range of topics including construction management, public transportation, and smart agriculture, this book is ideally designed for engineers, entrepreneurs, urban planners, architects, policymakers, researchers, academicians, and students.

ai in energy management