Buch, Englisch, 416 Seiten, Gewicht: 794 g
Buch, Englisch, 416 Seiten, Gewicht: 794 g
ISBN: 978-1-394-24943-5
Verlag: Wiley
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.
Autoren/Hrsg.
Fachgebiete
- Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Leistungselektronik
- Technische Wissenschaften Energietechnik | Elektrotechnik Alternative und erneuerbare Energien
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
Weitere Infos & Material
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
1.2.1 Variational Mode Decomposition 3
1.2.2 Long Short-Term Memory 5
1.2.3 Gated Recurrent Units 8
1.3 Proposed Methodology for Solar Power Forecasting 9
1.4 Experimental Results and Discussion 10
1.4.1 Solar PV Dataset 10
1.4.2 Experimental Setup and Model Training 12
1.4.3 Experimental Results 15
1.5 Conclusion 18
References 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
2.1.1 Brief Introduction to the State of Uttarakhand 23
2.1.2 Solar and Wind Energy Availability in Uttarakhand State 24
2.1.2.1 The Solar Statistics of the Uttarakhand State 24
2.1.2.2 The Wind Statistics of an Uttarakhand State 29
2.2 Observations 31
2.2.1 Satellite Image of Pithoragarh District 31
2.2.2 Solar Insolation of the District Pithoragarh Region in Uttarakhand 33
2.2.3 Wind Statistics of the District Pithoragarh Region in Uttarakhand 33
2.2.4 Monthly Speed Pattern of the Wind in the Study Area and Its Forecasting 35
2.3 Imperative of Machine Learning for Present Study 37
2.4 Conclusion 42
References 43
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
3.3.1 Technological Progress 55
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
3.6.1 Dynamic and Evolving Environments 61
3.6.2 Semantic Interoperability 61
3.6.3 Scale and Complexity 61
3.6.4 Human-Computer Interaction 62
3.6.5 Real-Time Decision Support 62
3.6.6 Security and Privacy Problems 62
3.6.7 Future Research Opportunities 62
3.7 Conclusion and Future Work 63
References 64
4 Statistical Forecasting Model for Solar Power Generation Under Different Environmental Conditions 67
Varun Pratap Singh and Bharti Sharma
4.1 Introduction 68
4.1.1 Overview of Solar Power Forecasting 68
4.1.2 Importance and Challenges of Accurate Forecasting 69
4.2 Fundamentals of Solar Power 69
4.2.1 Key Factors Influencing Solar Power Output 70
4.3 Statistical Forecasting Techniques 71
4.3.1 Time Series Forecasting Methods 71
4.3.1.1 Autoregressive Models (AR) 71
4.3.1.2 Moving Average Models (MA) 72
4.3.1.3 Autoregressive Integrated Moving Average (ARIMA) Models 73
4.3.1.4 Exponential Smoothing Models 73
4.3.2 Machine Learning Approaches in Solar Forecasting 74
4.3.2.1 Neural Networks 74
4.3.2.2 Support Vector Machines (SVMs) 77
4.3.3 Ensemble Methods 80
4.4 Environmental Impacts on Solar Power Generation 83
4.4.1 Influence of Weather Variabilities 83
4.4.2 Geographical Impact on Solar Radiation 84
4.5 Future Directions and Innovations 85
4.5.1 New Technologies and Methodologies Improving Forecasting Accuracy 85
4.5.2 Integrating AI and Big Data into Solar Energy Systems 86
4.6 Conclusion 86
References 88
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
5.1.1 Importance of Renewable Energy Forecasting 96
5.1.2 Overview of Forecasting Models 98
5.1.3 Challenges in Renewable Energy Forecasting 99
5.2 Types of Forecasting Models for Renewable Energy 101
5.2.1 Physical Models 101
5.2.1.1 Mesoscale Models 101
5.2.1.2 Microscale Models 102
5.2.1.3 Satellite-Based Models 102
5.2.2 Statistical Models 102
5.2.2.1 Time-Series Forecasting 102
5.2.2.2 Regression Models 102
5.2.2.3 Machine Learning Algorithms 103
5.2.3 Hybrid Models 105
5.2.3.1 Ensemble Methods 105
5.2.3.2 Integrated Physical-Statistical Models 106
5.2.3.3 Multi-Model Fusion 106
5.2.4 Specialized Models 107
5.2.4.1 Persistence Models 107
5.2.4.2 Probabilistic Models 107
5.2.4.3 Seasonal and Cyclical Models 107
5.3 Forecasting Wind and Solar Energy Generation 109
5.3.1 Wind Energy Forecasting Techniques 109
5.3.1.1 Wind Speed and Direction Forecasting 109
5.3.1.2 Turbine Output Prediction 110
5.3.2 Solar Energy Forecasting Techniques 112
5.3.2.1 Solar Irradiance Models 112
5.3.2.2 Photovoltaic Output Prediction 112
5.4 Application of Forecasting in Renewable Energy Market Operations 114
5.4.1 Impact on Energy Pricing 115
5.4.2 Renewable Energy Trading 115
5.4.3 Managing Supply and Demand Balance 115
5.4.4 Enhancing Grid Stability and Reliability 116
5.4.5 Investment and Financial Planning 116
5.4.6 Maintenance Scheduling 116
5.4.7 Energy Storage Optimization 117
5.4.8 Demand Response Programs 117
5.5 Advanced Topics in Renewable Energy Forecasting 118
5.5.1 Incorporating Climate Change Projections 118
5.5.2 Forecasting for Offshore Renewable Energy Sources 119
5.5.3 Role of Big Data and IoT in Forecasting 119
5.6 Challenges and Future Directions 120
5.6.1 Addressing Variability and Uncertainty 120
5.6.2 Integrating Emerging Technologies 121
5.6.3 Policy and Regulatory Considerations 122
5.7 Future Directions 122
References 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
6.1.1 Motivation and Contribution 133
6.2 Overview of Demand Forecasting 134
6.2.1 Classification of Demand Forecasting 134
6.2.2 Benefits of Load Forecasting 136
6.2.2.1 Efficient Resource Utilization 136
6.2.2.2 Operational Efficiency for Energy Producers 137
6.2.2.3 Enhanced Grid Operations and Reliability 137
6.2.2.4 Consumer Benefits and Renewable Energy Integration 137
6.2.2.5 Strategic Planning and Policy Support 137
6.2.3 Factors Affecting Electricity Demand Forecasting 138
6.2.3.1 Temporal Factors 138
6.2.3.2 Meteorological Effects 138
6.2.3.3 Economic Indicators 138
6.2.3.4 Societal Changes 139
6.2.3.5 Regulatory and Policy Influences 139
6.2.3.6 Complex Interactions 139
6.2.4 Challenges of Demand Forecasting in the Electricity Sector 139
6.2.5 Demand Forecasting Model Generation Framework 140
6.3 Overview of Machine Learning in Demand Forecasting 142
6.3.1 Defining Machine Learning 142
6.3.2 Categorizing Machine Learning Methods 143
6.3.3 Traditional vs. Machine Learning Approaches 143
6.3.4 Machine Learning Techniques in Demand Forecasting 143
6.3.5 Summary of the Reviewed Papers 144
6.4 Machine Learning–Based Demand Forecasting in Thailand’s Metropolitan Areas: An In-Depth Case Study 160
6.4.1 Overview 160
6.4.2 Model Design and Validation 161
6.4.3 Data Management 161
6.4.4 Training Process 162
6.4.5 Model Performance Evaluation 162
6.4.6 Model Performance Evaluation 163
6.4.7 Discussion 164
6.5 Conclusion 165
References 166
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
7.1.1 Power Generation Forecasting 174
7.1.2 Demand Forecasting 175
7.1.3 Electricity Price Forecasting 175
7.2 Understanding Power Generation, Demand, and Price Forecasting 176
7.2.1 Challenges and Uncertainties in Forecasting Electric Power, Demand, and Price 176
7.2.1.1 Power Generation Forecasting Challenges 177
7.2.1.2 Demand Forecasting Uncertainties 177
7.2.1.3 Price Forecasting Complexities 178
7.2.2 The Advantages of Ongoing Forecasting Evaluation in Power Generation, Demand, and Price Forecasting 178
7.3 Significance of Accuracy and Reliability in Forecasting Electric Power, Demand, and Price 180
7.3.1 For Energy Producers 180
7.3.2 For Consumers 181
7.3.3 For Energy Markets 181
7.4 Strategic Framework for Enhanced Forecast Evaluation 181
7.5 Performance Metrics for Forecasting Accuracy in Generation, Demand, and Price of Electricity 183
7.5.1 Criteria for Assessing Accuracy 184
7.5.2 Category of Forecasting in in Forecasting Electric Power, Demand, and Price 188
7.5.2.1 Statistical Metrics 188
7.5.2.2 Variability Estimation Metrics 189
7.5.2.3 Ramping Characterization Metrics 198
7.6 Comparative Analysis of Forecasting Methods in Energy Sector 203
7.7 Future Directions 209
7.8 Conclusion 210
References 211
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
References 239
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
9.4.1 Importance of Solar PV in Modern Electrification System 249
9.4.2 Working of Solar PV 249
9.4.3 Factors Affecting PV Power Generation 250
9.5 Design of a Predictive ML Model 254
9.5.1 Kth-Nearest Neighbor (KNN) Algorithm 255
9.5.2 The Random Forest Regressor 256
9.6 Data Processing for ML Model 258
9.6.1 Dataset Preparation 258
9.6.2 The Importance of Data Processing in Machine Learning 258
9.6.3 Steps Involved in Data Preprocessing 259
9.6.4 Visualizing the Dataset 261
9.7 MetaLearner Model 263
9.7.1 Dataset Preparation 263
9.7.2 The MetaLearner’s Operation 264
9.7.3 Holding the Model in Place 265
9.8 Result and Discussion 266
9.8.1 KNN Model 266
9.8.2 Feedforward Neural Network (FNN) Model 268
9.8.3 Random Forest Model 269
9.8.4 MetaLearner Model 269
9.9 Conclusion 271
References 272
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
10.1.1 Literature Survey 284
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
10.3.1 Support Vector Machines 294
10.3.2 Random Forest Algorithm 296
10.4 Convolutional Neural Network 297
10.5 Case Study 299
10.6 Conclusion and Future Scope 305
Appendix: Pseudocode of Algorithms 306
Appendix- A: Support Vector Machine 306
Appendix- B: Random Forest 306
Appendix-C: Convolutional Neural Network 307
References 307
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
11.3.1 Predictive Modeling and Evaluation of Solar Systems 313
11.3.2 Selection of Optimal Locations for Solar Installations 313
11.3.3 Design and Fabrication of Solar Cells 313
11.3.4 Optimizing the Efficiency of Solar Panels 314
11.4 Machine Learning Techniques in Solar Energy Conservation and Management 314
11.4.1 Artificial Neural Networks (ANNs) 315
11.4.2 Genetic Algorithm 316
11.4.3 Particle Swarm Optimization (PSO) 316
11.4.4 Simulated Annealing (SA) 317
11.4.5 Random Forest (RF) 317
11.4.6 Hybrid Algorithm 317
11.5 Conclusion and Future Perspectives 318
References 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
12.4.1 Support Vector Regression 328
12.4.2 Artificial Neural Network 332
12.5 Case Study: Forecasting Power Generation of a Solar PV System 336
12.6 Conclusion and Future Scope 342
Appendix: Pseudo Code of Suggested Algorithms 342
References 343
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
13.4.1 Input Data 351
13.4.2 Data Preprocessing 354
13.4.3 Electrical Load Forecasting Algorithms 355
13.4.3.1 ANN Model for Electrical Load Forecasting 355
13.4.3.2 Random Forest Model for Electrical Load Forecasting 358
13.5 Comparative Analysis 360
13.6 Conclusion 361
References 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
14.4.1 Forecasting the Power of Photovoltaic System 377
14.4.2 Forecasting the Power of Wind Energy System 378
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
