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Forecasting Methods for Renewable Power Generation

Edited by Jai Govind Singh, Rupendra Kumar Pachauri, and Sasidharan Sreedharan
Copyright: 2025   |   Expected Pub Date:2025//
ISBN: 9781394249435  |  Hardcover  |  
408 pages
Price: $225 USD
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One Line Description
Forecasting Methods for Renewable Power Generation is an essential resource for professionals and students alike, providing in-depth insights into vital forecasting techniques that enhance grid stability, optimize resource management, and enable effective electricity pricing strategies, making it a must-have reference for anyone involved in the clean energy sector.

Audience
Researchers, professors, students, electrical and data science engineers, and industry professionals working in the field of clean and renewable energy production and how to estimate demand response with electric market pricing

Description
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. It enables 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 the expected demand and availability of generation resources, market operators can optimize electricity pricing strategies. This allows them to align prices with the anticipated supply-demand balance, incentivize efficient use of electricity, and promote market efficiency. Accurate forecasting helps prevent price spikes, reduces market uncertainties, and supports the development of effective energy trading strategies.
Forecasting Methods for Renewable Power Generation presents these topics and trends in an encyclopedic format, serving as a go-to reference for the engineer, scientist, or student who is interested in the subject. The book is presented in three easy-to-navigate sections that thoroughly examine the AI and machine learning-based algorithms and pseudo code considered in this study. This is the most comprehensive and up-to-date encyclopedia of forecasting of renewable power generation, demand response, and electricity pricing ever written and is a must-have for any library.

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Author / Editor Details
Jai Govind Singh, PhD is an associate professor in the Department of Energy, Environment and Climate Change, School of Environment, Resources and Development at the Asian Institute of Technology, Bangkok, Thailand. He has completed nineteen sponsored research projects various international organizations and has published over 110 research papers in reputed journals and conferences. He has a wide net of research areas including 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 published and granted patents. His primary areas of research include solar energy, fuel cell technology, and smart grid operations.

Sasidharan Sreedharan, PhD is an assistant professor in the College of Applied Sciences, Ministry of Higher Education, Sultanate of Oman. He has completed more than fifteen 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 cyber security, smart grid operations, electrical supply restructuring, and energy storage.

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Table of Contents
Preface
1. Solar Power Forecasting Using Hybrid Deep Learning Networks Combined with Variational Mode Decomposition
Krishna Prakash Natarajan and Jai Govind Singh
1.1 Introduction
1.2 Methodology
1.2.1 Variational Mode Decomposition
1.2.2 Long Short-Term Memory
1.2.3 Gated Recurrent Units
1.3 Proposed Methodology for Solar Power Forecasting
1.4 Experimental Results and Discussion
1.4.1 Solar PV Dataset
1.4.2 Experimental Setup and Model Training
1.4.3 Experimental Results
1.5 Conclusion
References
2. Location Analysis and Environmental Validation for Installation of Hybrid Solar-Wind Energy Generation System in Hilly Areas of Uttarakhand: Study Toward Forecasting
Paramjeet Singh Paliyal, Shyam Kumar Menon, Surajit Mondal and Vikas Thapa
2.1 Introduction
2.1.1 Brief Introduction to the State of Uttarakhand
2.1.2 Solar and Wind Energy Availability in Uttarakhand State
2.1.2.1 The Solar Statistics of the Uttarakhand State
2.1.2.2 The Wind Statistics of an Uttarakhand State
2.2 Observations
2.2.1 Satellite Image of Pithoragarh District
2.2.2 Solar Insolation of the District Pithoragarh Region in Uttarakhand
2.2.3 Wind Statistics of the District Pithoragarh Region in Uttarakhand
2.2.4 Monthly Speed Pattern of the Wind in the Study Area and Its Forecasting
2.3 Imperative of Machine Learning for Present Study
2.4 Conclusion
References
3. Harnessing Wind Energy: Ontological Frameworks for Optimizing Wind Turbine Lifecycle Management and Performance
Gaurav Jaglan, Aman Jolly, Vikas Pandey, Shashikant and Priyanka Sharma
3.1 Introduction
3.2 Fundamentals of Ontologies
3.3 Wind Turbine Life Cycle Overview
3.3.1 Technological Progress
3.4 Ontologies in Wind Turbine Design and Development
3.5 Different Ontologies Used for Wind Energy and Wind Turbine
3.6 Challenges and Opportunities
3.6.1 Dynamic and Evolving Environments
3.6.2 Semantic Interoperability
3.6.3 Scale and Complexity
3.6.4 Human-Computer Interaction
3.6.5 Real-Time Decision Support
3.6.6 Security and Privacy Problems
3.6.7 Future Research Opportunities
3.7 Conclusion and Future Work
References
4. Statistical Forecasting Model for Solar Power Generation Under Different Environmental Conditions
Varun Pratap Singh and Bharti Sharma
4.1 Introduction
4.1.1 Overview of Solar Power Forecasting
4.1.2 Importance and Challenges of Accurate Forecasting
4.2 Fundamentals of Solar Power
4.2.1 Key Factors Influencing Solar Power Output
4.3 Statistical Forecasting Techniques
4.3.1 Time Series Forecasting Methods
4.3.1.1 Autoregressive Models (AR)
4.3.1.2 Moving Average Models (MA)
4.3.1.3 Autoregressive Integrated Moving Average (ARIMA) Models
4.3.1.4 Exponential Smoothing Models
4.3.2 Machine Learning Approaches in Solar Forecasting
4.3.2.1 Neural Networks
4.3.2.2 Support Vector Machines (SVMs)
4.3.3 Ensemble Methods
4.4 Environmental Impacts on Solar Power Generation
4.4.1 Influence of Weather Variabilities
4.4.2 Geographical Impact on Solar Radiation
4.5 Future Directions and Innovations
4.5.1 New Technologies and Methodologies Improving Forecasting Accuracy
4.5.2 Integrating AI and Big Data into Solar Energy Systems
4.6 Conclusion
References
5. Understanding Forecasting Models for Renewable Energy Generation and Market Operation
Varun Pratap Singh, Ashwani Kumar, Chandan Swaroop Meena and Nitesh Dutt
5.1 Introduction to Renewable Energy Forecasting
5.1.1 Importance of Renewable Energy Forecasting
5.1.2 Overview of Forecasting Models
5.1.3 Challenges in Renewable Energy Forecasting
5.2 Types of Forecasting Models for Renewable Energy
5.2.1 Physical Models
5.2.1.1 Mesoscale Models
5.2.1.2 Microscale Models
5.2.1.3 Satellite-Based Models
5.2.2 Statistical Models
5.2.2.1 Time-Series Forecasting
5.2.2.2 Regression Models
5.2.2.3 Machine Learning Algorithms
5.2.3 Hybrid Models
5.2.3.1 Ensemble Methods
5.2.3.2 Integrated Physical-Statistical Models
5.2.3.3 Multi-Model Fusion
5.2.4 Specialized Models
5.2.4.1 Persistence Models
5.2.4.2 Probabilistic Models
5.2.4.3 Seasonal and Cyclical Models
5.3 Forecasting Wind and Solar Energy Generation
5.3.1 Wind Energy Forecasting Techniques
5.3.1.1 Wind Speed and Direction Forecasting
5.3.1.2 Turbine Output Prediction
5.3.2 Solar Energy Forecasting Techniques
5.3.2.1 Solar Irradiance Models
5.3.2.2 Photovoltaic Output Prediction
5.4 Application of Forecasting in Renewable Energy Market Operations
5.4.1 Impact on Energy Pricing
5.4.2 Renewable Energy Trading
5.4.3 Managing Supply and Demand Balance
5.4.4 Enhancing Grid Stability and Reliability
5.4.5 Investment and Financial Planning
5.4.6 Maintenance Scheduling
5.4.7 Energy Storage Optimization
5.4.8 Demand Response Programs
5.5 Advanced Topics in Renewable Energy Forecasting
5.5.1 Incorporating Climate Change Projections
5.5.2 Forecasting for Offshore Renewable Energy Sources
5.5.3 Role of Big Data and IoT in Forecasting
5.6 Challenges and Future Directions
5.6.1 Addressing Variability and Uncertainty
5.6.2 Integrating Emerging Technologies
5.6.3 Policy and Regulatory Considerations
5.7 Future Directions
References
6. Machine Learning Techniques for Demand Forecasting in the Electricity Sector
Firuz Ahamed Nahid, Hussain Mahmud Chowdhury and Mohammad Nayeem Jahangir
6.1 Introduction
6.1.1 Motivation and Contribution
6.2 Overview of Demand Forecasting
6.2.1 Classification of Demand Forecasting
6.2.2 Benefits of Load Forecasting
6.2.2.1 Efficient Resource Utilization
6.2.2.2 Operational Efficiency for Energy Producers
6.2.2.3 Enhanced Grid Operations and Reliability
6.2.2.4 Consumer Benefits and Renewable Energy Integration
6.2.2.5 Strategic Planning and Policy Support
6.2.3 Factors Affecting Electricity Demand Forecasting
6.2.3.1 Temporal Factors
6.2.3.2 Meteorological Effects
6.2.3.3 Economic Indicators
6.2.3.4 Societal Changes
6.2.3.5 Regulatory and Policy Influences
6.2.3.6 Complex Interactions
6.2.4 Challenges of Demand Forecasting in the Electricity Sector
6.2.5 Demand Forecasting Model Generation Framework
6.3 Overview of Machine Learning in Demand Forecasting
6.3.1 Defining Machine Learning
6.3.2 Categorizing Machine Learning Methods
6.3.3 Traditional vs. Machine Learning Approaches
6.3.4 Machine Learning Techniques in Demand Forecasting
6.3.5 Summary of the Reviewed Papers
6.4 Machine Learning–Based Demand Forecasting in Thailand’s Metropolitan Areas: An In-Depth Case Study
6.4.1 Overview
6.4.2 Model Design and Validation
6.4.3 Data Management
6.4.4 Training Process
6.4.5 Model Performance Evaluation
6.4.6 Model Performance Evaluation
6.4.7 Discussion
6.5 Conclusion
References
7. Evaluation and Performance Metrics for Forecasting Renewable Power Generation, Demand, and Electricity Price
Firuz Ahamed Nahid, Mohammad Nayeem Jahangir, Hussain Mahmud Chowdhury and Khadiza Akter
7.1 Introduction
7.1.1 Power Generation Forecasting
7.1.2 Demand Forecasting
7.1.3 Electricity Price Forecasting
7.2 Understanding Power Generation, Demand, and Price Forecasting
7.2.1 Challenges and Uncertainties in Forecasting Electric Power, Demand, and Price
7.2.1.1 Power Generation Forecasting Challenges
7.2.1.2 Demand Forecasting Uncertainties
7.2.1.3 Price Forecasting Complexities
7.2.2 The Advantages of Ongoing Forecasting Evaluation in Power Generation, Demand, and Price Forecasting
7.3 Significance of Accuracy and Reliability in Forecasting Electric Power, Demand, and Price
7.3.1 For Energy Producers
7.3.2 For Consumers
7.3.3 For Energy Markets
7.4 Strategic Framework for Enhanced Forecast Evaluation
7.5 Performance Metrics for Forecasting Accuracy in Generation, Demand, and Price of Electricity
7.5.1 Criteria for Assessing Accuracy
7.5.2 Category of Forecasting in in Forecasting Electric Power, Demand, and Price
7.5.2.1 Statistical Metrics
7.5.2.2 Variability Estimation Metrics
7.5.2.3 Ramping Characterization Metrics
7.6 Comparative Analysis of Forecasting Methods in Energy Sector
7.7 Future Directions
7.8 Conclusion
References
8. Forecasting Electricity Prices Using NNAR Approach: An Emerging Nation Experience
Sonal Gupta and Deepankar Chakrabarti
8.1 Introduction
8.2 Literature Review
8.3 Data and Methodology
8.4 Data Analysis
8.5 Conclusion
References
9. Machine Learning–Enabled Solar Photovoltaic Energy Forecasting for Modern-Day Grid Integration: A Virtual Power Plant Perspective
Subhajit Roy, Smriti Jaiswal, Manav Sanghi, Mriganka Dhar, Arif Mohammed, Kothalanka K. Pavan, D. C. Das and Nidul Sinha
9.1 Introduction
9.2 Literature Review
9.3 Application of Machine Learning to Tackle Climatic Constraints
9.4 Application of ML in Solar PV–Based Generation
9.4.1 Importance of Solar PV in Modern Electrification System
9.4.2 Working of Solar PV
9.4.3 Factors Affecting PV Power Generation
9.5 Design of a Predictive ML Model
9.5.1 Kth-Nearest Neighbor (KNN) Algorithm
9.5.2 The Random Forest Regressor
9.6 Data Processing for ML Model
9.6.1 Dataset Preparation
9.6.2 The Importance of Data Processing in Machine Learning
9.6.3 Steps Involved in Data Preprocessing
9.6.4 Visualizing the Dataset
9.7 MetaLearner Model
9.7.1 Dataset Preparation
9.7.2 The MetaLearner’s Operation
9.7.3 Holding the Model in Place
9.8 Result and Discussion
9.8.1 KNN Model
9.8.2 Feedforward Neural Network (FNN) Model
9.8.3 Random Forest Model
9.8.4 MetaLearner Model
9.9 Conclusion
References
10. Scenario Analysis and Practical Approach of Deep Learning and Machine Learning Techniques in the Renewable Energy Sector
Supriya, Ashutosh Shukla, Priyanka Sharma and Rupendra Kumar Pachauri
10.1 Introduction
10.1.1 Literature Survey
10.2 Building an Intelligent System for Solar PV Analyzer
10.3 Popular Machine Learning and Deep Learning Techniques for Solar PV Classifications
10.3.1 Support Vector Machines
10.3.2 Random Forest Algorithm
10.4 Convolutional Neural Network
10.5 Case Study
10.6 Conclusion and Future Scope
Appendix: Pseudocode of Algorithms
Appendix- A: Support Vector Machine
Appendix- B: Random Forest
Appendix-C: Convolutional Neural Network
References
11. Application of Artificial Intelligence and Machine Learning in Assessing Solar Energy Potential
Ajay Mittal
11.1 Introduction
11.2 Interconnections Between Deep Learning (DL), Machine Learning (ML), and Artificial Intelligence (AI)
11.3 Applications of Artificial Intelligence in Assessing Solar Energy Potential
11.3.1 Predictive Modeling and Evaluation of Solar Systems
11.3.2 Selection of Optimal Locations for Solar Installations
11.3.3 Design and Fabrication of Solar Cells
11.3.4 Optimizing the Efficiency of Solar Panels
11.4 Machine Learning Techniques in Solar Energy Conservation and Management
11.4.1 Artificial Neural Networks (ANNs)
11.4.2 Genetic Algorithm
11.4.3 Particle Swarm Optimization (PSO)
11.4.4 Simulated Annealing (SA)
11.4.5 Random Forest (RF)
11.4.6 Hybrid Algorithm
11.5 Conclusion and Future Perspectives
References
12. Revolutionizing Solar PV Forecasting with Machine Learning Techniques
Supriya, Ashutosh Shukla, Priyanka Sharma and Rupendra Kumar Pachauri
12.1 Introduction
12.2 Related Work
12.3 Smart System for Solar PV Forecasting
12.4 Prominent Machine Learning Techniques for Forecasting
12.4.1 Support Vector Regression
12.4.2 Artificial Neural Network
12.5 Case Study: Forecasting Power Generation of a Solar PV System
12.6 Conclusion and Future Scope
Appendix: Pseudo Code of Suggested Algorithms
References
13. Machine Learning–Based Prediction of Electrical Load in the Context of Variable Weather Conditions
Ashutosh Shukla, Supriya and Rupendra Kumar Pachauri
13.1 Introduction
13.2 Previous Work
13.3 Significance of Work
13.4 Methodology
13.4.1 Input Data
13.4.2 Data Preprocessing
13.4.3 Electrical Load Forecasting Algorithms
13.4.3.1 ANN Model for Electrical Load Forecasting
13.4.3.2 Random Forest Model for Electrical Load Forecasting
13.5 Comparative Analysis
13.6 Conclusion
References
14. Recent Advancement in Renewable Energy with Artificial Intelligence and Machine Learning
Sakshi Chaudhary, Aakansha Simra and Gaurav Pandey
14.1 Introduction
14.2 The Growth and Intersection of AI and ML in the World of Renewable Power
14.3 Machine Learning–Based Forecasting System for Renewable Energy Production
14.4 AI and ML Applications for Renewable Energy
14.4.1 Forecasting the Power of Photovoltaic System
14.4.2 Forecasting the Power of Wind Energy System
14.5 Approaches and Limitations in AI Application for Renewable Energy
14.6 Advances and Prospects in AI for Solar and Wind Power
14.7 Conclusion
References
About the Editors
Index

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