Forecasting Methods for Renewable Power Generation is an essential resource for both professionals and students, providing in-depth insights into vital forecasting techniques that enhance grid stability, optimize resource management, and enable effective electricity pricing strategies. It is a must-have reference for anyone involved in the clean energy sector.
Forecasting techniques in renewable power generation, demand response, and electricity pricing are vital for grid stability, optimal resource allocation, efficient energy management, and cost-effective electricity supply. They enable grid operators and market participants to make informed decisions, mitigate risks, and enhance the overall reliability and sustainability of the electrical grid. Electricity prices can vary significantly based on supply and demand dynamics. By forecasting expected demand and the availability of generation resources, market operators can optimize electricity pricing strategies. This alignment of prices with anticipated supply-demand balance incentivizes the efficient use of electricity and promotes market efficiency. Accurate forecasting helps prevent price spikes, reduces market uncertainties, and supports the development of effective energy trading strategies.
This book presents these topics and trends in an encyclopedic format, serving as a go-to reference for engineers, scientists, or students interested in the subject. The book is divided into three easy-to-navigate sections that thoroughly examine the AI and machine learning-based algorithms and pseudocode considered in this study. This is the most comprehensive and up-to-date encyclopedia of forecasting in renewable power generation, demand response, and electricity pricing ever written, and is a must-have for any library.
Preface xv 1 Solar Power Forecasting Using Hybrid Deep Learning Networks Combined with Variational Mode Decomposition 1 Krishna Prakash Natarajan and Jai Govind Singh 1.1 Introduction 1 1.2 Methodology 3 1.3 Proposed Methodology for Solar Power Forecasting 9 1.4 Experimental Results and Discussion 10 1.5 Conclusion 18 2 Location Analysis and Environmental Validation for Installation of Hybrid Solar-Wind Energy Generation System in Hilly Areas of Uttarakhand: Study Toward Forecasting 21 Paramjeet Singh Paliyal, Shyam Kumar Menon, Surajit Mondal and Vikas Thapa 2.1 Introduction 22 2.2 Observations 31 2.3 Imperative of Machine Learning for Present Study 37 2.4 Conclusion 42 3 Harnessing Wind Energy: Ontological Frameworks for Optimizing Wind Turbine Lifecycle Management and Performance 49 Gaurav Jaglan, Aman Jolly, Vikas Pandey, Shashikant and Priyanka Sharma 3.1 Introduction 50 3.2 Fundamentals of Ontologies 51 3.3 Wind Turbine Life Cycle Overview 53 3.4 Ontologies in Wind Turbine Design and Development 55 3.5 Different Ontologies Used for Wind Energy and Wind Turbine 57 3.6 Challenges and Opportunities 61 3.7 Conclusion and Future Work 63 4 Statistical Forecasting Model for Solar Power Generation Under Different Environmental Conditions 67 Varun Pratap Singh and Bharti Sharma 4.1 Introduction 68 4.2 Fundamentals of Solar Power 69 4.3 Statistical Forecasting Techniques 71 4.4 Environmental Impacts on Solar Power Generation 83 4.5 Future Directions and Innovations 85 4.6 Conclusion 86 5 Understanding Forecasting Models for Renewable Energy Generation and Market Operation 95 Varun Pratap Singh, Ashwani Kumar, Chandan Swaroop Meena and Nitesh Dutt 5.1 Introduction to Renewable Energy Forecasting 96 5.2 Types of Forecasting Models for Renewable Energy 101 5.3 Forecasting Wind and Solar Energy Generation 109 5.4 Application of Forecasting in Renewable Energy Market Operations 114 5.5 Advanced Topics in Renewable Energy Forecasting 118 5.6 Challenges and Future Directions 120 5.7 Future Directions 122 6 Machine Learning Techniques for Demand Forecasting in the Electricity Sector 131 Firuz Ahamed Nahid, Hussain Mahmud Chowdhury and Mohammad Nayeem Jahangir 6.1 Introduction 132 6.2 Overview of Demand Forecasting 134 6.3 Overview of Machine Learning in Demand Forecasting 142 6.4 Machine Learning--Based Demand Forecasting in Thailand’s Metropolitan Areas: An In-Depth Case Study 160 6.5 Conclusion 165 7 Evaluation and Performance Metrics for Forecasting Renewable Power Generation, Demand, and Electricity Price 173 Firuz Ahamed Nahid, Mohammad Nayeem Jahangir, Hussain Mahmud Chowdhury and Khadiza Akter 7.1 Introduction 174 7.2 Understanding Power Generation, Demand, and Price Forecasting 176 7.3 Significance of Accuracy and Reliability in Forecasting Electric Power, Demand, and Price 180 7.4 Strategic Framework for Enhanced Forecast Evaluation 181 7.5 Performance Metrics for Forecasting Accuracy in Generation, Demand, and Price of Electricity 183 7.6 Comparative Analysis of Forecasting Methods in Energy Sector 203 7.7 Future Directions 209 7.8 Conclusion 210 8 Forecasting Electricity Prices Using NNAR Approach: An Emerging Nation Experience 219 Sonal Gupta and Deepankar Chakrabarti 8.1 Introduction 220 8.2 Literature Review 223 8.3 Data and Methodology 226 8.4 Data Analysis 228 8.5 Conclusion 238 9 Machine Learning--Enabled Solar Photovoltaic Energy Forecasting for Modern-Day Grid Integration: A Virtual Power Plant Perspective 243 Subhajit Roy, Smriti Jaiswal, Manav Sanghi, Mriganka Dhar, Arif Mohammed, Kothalanka K. Pavan, D. C. Das and Nidul Sinha 9.1 Introduction 244 9.2 Literature Review 245 9.3 Application of Machine Learning to Tackle Climatic Constraints 248 9.4 Application of ML in Solar PV--Based Generation 249 9.5 Design of a Predictive ML Model 254 9.6 Data Processing for ML Model 258 9.7 MetaLearner Model 263 9.8 Result and Discussion 266 9.9 Conclusion 271 10 Scenario Analysis and Practical Approach of Deep Learning and Machine Learning Techniques in the Renewable Energy Sector 279 Supriya, Ashutosh Shukla, Priyanka Sharma and Rupendra Kumar Pachauri 10.1 Introduction 280 10.2 Building an Intelligent System for Solar PV Analyzer 293 10.3 Popular Machine Learning and Deep Learning Techniques for Solar PV Classifications 294 10.4 Convolutional Neural Network 297 10.5 Case Study 299 10.6 Conclusion and Future Scope 305 11 Application of Artificial Intelligence and Machine Learning in Assessing Solar Energy Potential 311 Ajay Mittal 11.1 Introduction 311 11.2 Interconnections Between Deep Learning (DL), Machine Learning (ML), and Artificial Intelligence (AI) 312 11.3 Applications of Artificial Intelligence in Assessing Solar Energy Potential 313 11.4 Machine Learning Techniques in Solar Energy Conservation and Management 314 11.5 Conclusion and Future Perspectives 318 12 Revolutionizing Solar PV Forecasting with Machine Learning Techniques 321 Supriya, Ashutosh Shukla, Priyanka Sharma and Rupendra Kumar Pachauri 12.1 Introduction 322 12.2 Related Work 325 12.3 Smart System for Solar PV Forecasting 328 12.4 Prominent Machine Learning Techniques for Forecasting 328 12.5 Case Study: Forecasting Power Generation of a Solar PV System 336 12.6 Conclusion and Future Scope 342 13 Machine Learning--Based Prediction of Electrical Load in the Context of Variable Weather Conditions 347 Ashutosh Shukla, Supriya and Rupendra Kumar Pachauri 13.1 Introduction 348 13.2 Previous Work 349 13.3 Significance of Work 350 13.4 Methodology 350 13.5 Comparative Analysis 360 13.6 Conclusion 361 14 Recent Advancement in Renewable Energy with Artificial Intelligence and Machine Learning 365 Sakshi Chaudhary, Aakansha Simra and Gaurav Pandey 14.1 Introduction 365 14.2 The Growth and Intersection of AI and ML in the World of Renewable Power 369 14.3 Machine Learning--Based Forecasting System for Renewable Energy Production 371 14.4 AI and ML Applications for Renewable Energy 376 14.5 Approaches and Limitations in AI Application for Renewable Energy 379 14.6 Advances and Prospects in AI for Solar and Wind Power 380 14.7 Conclusion 381 References 381 About the Editors 387 Index 389
Jai Govind Singh, PhD, is an associate professor in the Department of Energy, Environment, and Climate Change at the School of Environment, Resources, and Development, Asian Institute of Technology, Bangkok, Thailand. He has completed 19 sponsored research projects with various international organizations and has published over 110 research papers in reputed journals and conferences. His wide net of research areas includes e-vehicle technologies, smart grid and micro-grid design and operation, power system operation and control, electricity market restructuring and power trading, and energy storage technologies. Rupendra Kumar Pachauri, PhD, is an assistant professor in the Electrical and Electronics Engineering Department at the University of Petroleum and Energy Studies, Dehradun, India. He has published over 130 research papers in internationally reputed journals and conferences, as well as several patents. His primary areas of research include solar energy, fuel cell technology, and smart grid operations. Sasidharan Sreedharan, PhD, is an assistant professor at the College of Applied Sciences, Ministry of Higher Education, Sultanate of Oman. He has completed more than 15 sponsored research projects for various international organizations and published over 80 research papers in reputed journals and conferences. His primary areas of research include high-performance computing, AI and machine learning, optimization and cybersecurity, smart grid operations, electrical supply restructuring, and energy storage.
