Forecasting Methods for Renewable Power Generation E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 525

Veröffentlichungsjahr: 2025

Beschreibung

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.

Leseprobe

Table of Contents

Cover

Table of Contents

Series Page

Title Page

Copyright Page

Preface

1 Solar Power Forecasting Using Hybrid Deep Learning Networks Combined with Variational Mode Decomposition

1.1 Introduction

1.2 Methodology

1.3 Proposed Methodology for Solar Power Forecasting

1.4 Experimental Results and Discussion

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

2.1 Introduction

2.2 Observations

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

3.1 Introduction

3.2 Fundamentals of Ontologies

3.3 Wind Turbine Life Cycle Overview

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

3.7 Conclusion and Future Work

References

4 Statistical Forecasting Model for Solar Power Generation Under Different Environmental Conditions

4.1 Introduction

4.2 Fundamentals of Solar Power

4.3 Statistical Forecasting Techniques

4.4 Environmental Impacts on Solar Power Generation

4.5 Future Directions and Innovations

4.6 Conclusion

References

5 Understanding Forecasting Models for Renewable Energy Generation and Market Operation

5.1 Introduction to Renewable Energy Forecasting

5.2 Types of Forecasting Models for Renewable Energy

5.3 Forecasting Wind and Solar Energy Generation

5.4 Application of Forecasting in Renewable Energy Market Operations

5.5 Advanced Topics in Renewable Energy Forecasting

5.6 Challenges and Future Directions

5.7 Future Directions

References

6 Machine Learning Techniques for Demand Forecasting in the Electricity Sector

6.1 Introduction

6.2 Overview of Demand Forecasting

6.3 Overview of Machine Learning in Demand Forecasting

6.4 Machine Learning–Based Demand Forecasting in Thailand’s Metropolitan Areas: An In-Depth Case Study

6.5 Conclusion

References

7 Evaluation and Performance Metrics for Forecasting Renewable Power Generation, Demand, and Electricity Price

7.1 Introduction

7.2 Understanding Power Generation, Demand, and Price Forecasting

7.3 Significance of Accuracy and Reliability in Forecasting Electric Power, Demand, and Price

7.4 Strategic Framework for Enhanced Forecast Evaluation

7.5 Performance Metrics for Forecasting Accuracy in Generation, Demand, and Price of Electricity

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

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

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

9.5 Design of a Predictive ML Model

9.6 Data Processing for ML Model

9.7 MetaLearner Model

9.8 Result and Discussion

9.9 Conclusion

References

10 Scenario Analysis and Practical Approach of Deep Learning and Machine Learning Techniques in the Renewable Energy Sector

10.1 Introduction

10.2 Building an Intelligent System for Solar PV Analyzer

10.3 Popular Machine Learning and Deep Learning Techniques for Solar PV Classifications

10.4 Convolutional Neural Network

10.5 Case Study

10.6 Conclusion and Future Scope

Appendix: Pseudocode of Algorithms

References

11 Application of Artificial Intelligence and Machine Learning in Assessing Solar Energy Potential

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

11.4 Machine Learning Techniques in Solar Energy Conservation and Management

11.5 Conclusion and Future Perspectives

References

12 Revolutionizing Solar PV Forecasting with Machine Learning Techniques

12.1 Introduction

12.2 Related Work

12.3 Smart System for Solar PV Forecasting

12.4 Prominent Machine Learning Techniques for Forecasting

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

13.1 Introduction

13.2 Previous Work

13.3 Significance of Work

13.4 Methodology

13.5 Comparative Analysis

13.6 Conclusion

References

14 Recent Advancement in Renewable Energy with Artificial Intelligence and Machine Learning

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

14.5 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

Also of Interest

End User License Agreement

List of Tables

Chapter 1

Table 1.1 Values of hyperparameters used.

Table 1.2 Performance metrics of the proposed hybrid model with VMD decomposit...

Chapter 2

Table 2.1 Wind speed and wind direction of latitude 30.06675 and longitude 79....

Table 2.2 Wind speed and wind direction of latitude 30.14607 and longitude 80....

Table 2.3 Imperative of machine learning for the present study.

Chapter 3

Table 3.1 Comparison of ontologies supporting wind turbine life cycle manageme...

Chapter 5

Table 5.1 Factors involved in wind energy forecasting.

Table 5.2 Factors involved in solar energy forecasting.

Chapter 6

Table 6.1 Comparison of traditional and machine learning models in demand fore...

Table 6.2 Strength and limitations of machine learning models in electric dema...

Table 6.3 Summary of the reviewed literatures in machine learning approaches i...

Table 6.4 Dataset statistics.

Table 6.5 Error index of the forecasting models.

Chapter 7

Table 7.1 Summary of statistical metrics in power generation, electricity dema...

Table 7.2 Summary of variability estimation metrics in power generation, elect...

Table 7.3 Summary of ramping characterization metrics in power generation, ele...

Table 7.4 Comparative analysis of forecasting models in energy sector.

Chapter 8

Table 8.1 Advantages and disadvantages of short-term and long-term power contr...

Table 8.2 Descriptive statistics for bid area’s electricity prices [8, 13, 14]...

Table 8.3 Results of NNAR(30,1,16)

[365]

model.

Table 8.4 Twenty-five days of forecasted values of NNAR(30,1,16)

[365]

model fo...

Table 8.5 Twenty-five days of forecasted values of NNAR(30,1,16)

[365]

model fo...

Table 8.6 Twenty-five days of forecasted values of NNAR(30,1,16)

[365]

model fo...

Table 8.7 Twenty-five days of forecasted values of NNAR(30,1,16)

[365]

model fo...

Table 8.8 Twenty-five days of forecasted values of NNAR(30,1,16)

[365]

model fo...

Table 8.9 Twenty-five days of forecasted values of NNAR(30,1,16)

[365]

model fo...

Table 8.10 Twenty-five days of forecasted values of NNAR(30,1,16)

[365]

model f...

Table 8.11 Twenty-five days of forecasted values of NNAR(30,1,16)

[365]

model f...

Chapter 9

Table 9.1 Different weather-related factors influence SPV-based generation.

Table 9.2 Dataset used for training and testing for ML model in this project (...

Chapter 10

Table 10.1 Taxonomy of the available literature.

Table 10.2 Dataset extracted through GLCM.

Table 10.3 Performance evaluation of ML models.

Chapter 12

Table 12.1 Few well-known kernels.

Table 12.2 Few well-known activation functions.

Table 12.3 Performance comparison of the model used.

Chapter 13

Table 13.1 Performance evaluation of ANN predictive model of load forecasting....

Table 13.2 Performance evaluation of random forest

Table 13.3 Comparative analysis of ANN and RF models for three seasons.

Chapter 14

Table 14.1 Energy consumption across various nations from 2005 to 2024.

Table 14.2 ML and deep learning techniques including advantages and disadvanta...

List of Illustrations

Chapter 1

Figure 1.1 Traditional RNN.

Figure 1.2 Structure of an LSTM cell.

Figure 1.3 The framework of the proposed prediction method.

Figure 1.4 Solar power generation on typical days.

Figure 1.5 Mean solar power generated by hour for (a) summer and (b) winter.

Figure 1.6 Mean solar radiation by months.

Figure 1.7 Correlation between mean solar power, irradiation, and temperature....

Figure 1.8 Correlation of the features plotted against the solar power.

Figure 1.9 Solar power dataset decomposed with VMD.

Figure 1.10 Solar power prediction for a typical day.

Figure 1.11 Scatter plot of solar power prediction versus actual values.

Figure 1.12 Solar power prediction for a typical cloudy day.

Chapter 2

Figure 2.1 GIS Map of the Uttarakhand state.

Figure 2.2 Monthly solar insolation from 2009 to 2023 in kilowatt hours per me...

Figure 2.3 Long-term monthly solar insolation from year with minimum, maximum,...

Figure 2.4 Long-term monthly temperature pattern in degrees celsius.

Figure 2.5 Tracing of the sun path to get the azimuth angle and day length by ...

Figure 2.6 Seventy-two hours of forecasting of the solar isolation and tempera...

Figure 2.7 Satellite image of Pithoragarh district of Uttarakhand [21].

Figure 2.8 Bird-eye view of Pithoragarh district of Uttarakhand [21].

Figure 2.9 Solar insolation and temperature pattern of the Pithoragarh distric...

Figure 2.10 Wind pattern for the latitude 30.53 and longitude 80.29 showing th...

Figure 2.11 Seventy-two hours of forecasting of wind speed and wind direction....

Chapter 3

Figure 3.1 Use cases for ontology-based wind turbine lifecycle management.

Figure 3.2 W3C standard technologies encompassing semantic web stack.

Chapter 4

Figure 4.1 Basic ANN structures.

Figure 4.2 The modeling process flowchart for ANN.

Figure 4.3 SVM architecture.

Figure 4.4 Model classification based on temporal and spatial resolutions [61]...

Chapter 5

Figure 5.1 Classification of the renewable energy forecasting model.

Figure 5.2 Application of forecasting in renewable energy market operations.

Chapter 6

Figure 6.1 Electric demand forecasting classification.

Figure 6.2 Demand forecasting modeling approach.

Figure 6.3 Monthly average load curve of metropolitan area, Thailand.

Figure 6.4 Proposed model architecture.

Figure 6.5 Forecasting performance of the models: (a) proposed model, (b) LSTM...

Chapter 7

Figure 7.1 Challenges of power generation, demand, and price forecasting.

Figure 7.2 Significance of accuracy and reliability in forecasting electric po...

Figure 7.3 Framework for enhanced forecast evaluation.

Figure 7.4 Forecasting evaluation criteria.

Chapter 8

Figure 8.1 IEX bid areas [10].

Figure 8.2 Detailed process of neural network autoregressive method.

Chapter 9

Figure 9.1 Illustration of a solar cell’s operation, showcasing the movement o...

Figure 9.2 Variation in sky cover (oktas) graded on a scale of 1–10.

Figure 9.3 Correlation between temperature and voltage output illustrated in a...

Figure 9.4 Graph displaying the relationship between relative humidity and pow...

Figure 9.5 Shots in solar luminance (Lux) in relation to changes in air pressu...

Figure 9.6 Functionality of the KNN algorithm illustrated.

Figure 9.7 Representation of a feedforward neural network.

Figure 9.8 Visual depiction of the random forest [58].

Figure 9.9 A representation of the missing data for each characteristic.

Figure 9.10 Distribution of solar PV output frequencies across various solar p...

Figure 9.11 Heat map illustrating correlations.

Figure 9.12 Operational process of the MetaLearner model.

Figure 9.13 Diagram displaying the workflow of the stacking regressor.

Figure 9.14 Comparison between actual and predicted values using KNN regressor...

Figure 9.15 Evaluation metrics for the feedforward neural network’s performanc...

Figure 9.16 Evaluation metrics for the random forest regressor’s performance....

Figure 9.17 Evaluation metrics for the MetaLearner’s performance.

Chapter 10

Figure 10.1 Potential of renewable power in various states of India.

Figure 10.2 Various renewable energy sources in India.

Figure 10.3 Scenario analysis of deep learning and machine learning techniques...

Figure 10.4 Stages for building an intelligent system for solar PV analyzer.

Figure 10.5 Decision boundary through support vectors.

Figure 10.6 Random forest classifier.

Figure 10.7 Stacks of layer in CNN.

Figure 10.8 Workflow to the solar PV identification system.

Figure 10.9 (a) Dusty solar PV module. (b) Clean solar PV module.

Chapter 11

Figure 11.1 Relationships between AI, machine learning, and deep learning [9]....

Figure 11.2 Schematic showing the most commonly used ML techniques in solar en...

Figure 11.3 Comparison plot between power output of PV (experimental) and PV/T...

Figure 11.4 Design optimization of grid-connected solar power plants [15].

Chapter 12

Figure 12.1 Cost-competitiveness of renewable energy resources. Data source: M...

Figure 12.2 Potential of renewable energy resources [5].

Figure 12.3 Solar PV system.

Figure 12.4 Factors influencing the efficiency of solar PV system.

Figure 12.5 Steps of building a smart system for solar PV forecasting.

Figure 12.6 Finding a hyperplane for SVR.

Figure 12.7 Role of kernel in SVR.

Figure 12.8 Artificial neural network architecture.

Figure 12.9 Multi-layered artificial neural networks.

Figure 12.10 Covariance matrix of the dataset.

Figure 12.11 Predictor variables: (a) irradiation, (b) ambient temperature, an...

Figure 12.12 (a) SVR performance and (b) ANN performance.

Chapter 13

Figure 13.1 Proposed workflow.

Figure 13.2 One-year data division into three seasons.

Figure 13.3(a) Seasonal 24-hour average solar irradiance in W/m

2

.

Figure 13.3(b) Seasonal 24-hour average humidity in %age.

Figure 13.3(c) Seasonal 24-hour average temperature in Fahrenheit.

Figure 13.3(d) Correlation coefficient matrix.

Figure 13.4 Seasonal 24-hour average electrical load in kW.

Figure 13.5 Structure of an ANN model.

Figure 13.6 R-value of training, validation, and testing.

Figure 13.7 RF model for regression.

Figure 13.8 R-value for validation.

Chapter 14

Figure 14.1 Percentage change in renewable energy consumption for five differe...

Figure 14.2 ML types and algorithms.

Guide

Cover Page

Table of Contents

Series Page

Title Page

Copyright Page

Preface

Begin Reading

About the Editors

Index

Also of Interest

WILEY END USER LICENSE AGREEMENT

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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106

Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])

Forecasting Methods for Renewable Power Generation

Edited by

Jai Govind Singh

Rupendra Kumar Pachauri

and

Sasidharan Sreedharan

This edition first published 2025 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2025 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.

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Library of Congress Cataloging-in-Publication Data

ISBN 9781394249435

Front cover images supplied by Pixabay.comCover design by Russell Richardson

Preface

As we stride toward a sustainable future, the integration of renewable power sources into our energy mix becomes increasingly pivotal. However, the intermittent nature of these resources poses unique challenges for power system operators, planners, and policymakers alike.

Forecasting Methods for Renewable Power Generation emerges as a comprehensive guide to navigating these challenges. In this edited book, we delve deep into the realm of forecasting techniques tailored specifically for renewable energy, demand, and electricity price. This book serves as a platform for experts from diverse backgrounds, including academia, industry, and research institutions, to share their insights, methodologies, and practical experiences in forecasting renewable power generation, demand patterns, and electricity prices. By integrating theoretical foundations with real-world applications, it offers the readers a holistic understanding of the complexities involved in predicting variables crucial for the efficient operation and planning of modern power systems.

The chapters in this volume cover a wide spectrum of topics, ranging from statistical methods to cutting-edge machine learning algorithms and hybrid models. Each chapter not only elucidates the underlying principles but also provides extensive guidance on implementing these methods using various software tools and platforms. Furthermore, this book explores the interdisciplinary nature of forecasting in the context of renewable energy, drawing connections between meteorology, economics, and engineering. By fostering collaboration and knowledge exchange across disciplines, it aims to accelerate the development and adoption of innovative forecasting solutions that can drive the transition toward a more sustainable and resilient energy landscape.

Whether you are a researcher, practitioner, policymaker, or student, Forecasting Methods for Renewable Power Generation offers valuable insights and practical strategies to address the forecasting methods in renewable energy integration. We hope that this book serves as a catalyst for further advancements in the field.

Editors

Dr. Jai Govind Singh

Dr. Rupendra Kumar Pachauri

Dr. Sasidharan Sreedharan

2Location Analysis and Environmental Validation for Installation of Hybrid Solar-Wind Energy Generation System in Hilly Areas of Uttarakhand: Study Toward Forecasting

Paramjeet Singh Paliyal1, Shyam Kumar Menon1, Surajit Mondal1* and Vikas Thapa2

1Electrical Cluster, School of Advanced Engineering, UPES, Dehradun, India

2School of Design, UPES, Dehradun, India

Abstract

Although the sunshine and wind resources in Uttarakhand are abundant, they are not fully used. The power demand is currently rising quickly, and nonrenewable energy sources cannot satisfy it on their own. Nature provides renewable energy sources like solar and wind power. Although it is a growing option to meet energy demand, renewable energy is unpredictable. Systems that combine two or more renewable energy sources, such as solar panels and wind turbine systems, are known as hybrid solar and wind renewable energy systems (HRESs). The battery output serves as the voltage for the load after the outputs of the solar and wind power conversion systems are combined, charging the same battery. This study’s objective is to present the application of a combination arrangement and a thorough examination of numerous HRES-related issues. With less developed capacity, renewable energy in Uttarakhand has enormous potential. As a result, there is a significant difference between potential and installed power. Renewable energy from solar, wind, and hydropower installations is effective in Uttarakhand. The most rapidly expanding area of technology is in hybrid systems, which has increased energy-generating productivity. There are various locations in Uttarakhand where weather statistics can be researched to use hybrid energy systems, even though the resources to draw energy are not yet being employed there. The Uttarakhand weather should be taken into account while using this engineering in Uttarakhand’s mountainous areas for small-scale loads like lighting systems in a particular village.

Keywords: Hybrid system, PV system, wind mills, Uttarakhand, energy generation

2.1 Introduction

Both conventional and unconventional energy sources are employed to produce electricity. The main drawbacks are the connection to global warming and climate change, as they would vanish from the planet if a traditional source was still used. As a result, the hunt for many other kinds of energy ends with both unconventional types of energy. Due to their complementing nature, some renewable energy sources, like solar and wind energy, are frequently utilized to produce electrical power [3].

Two systems—solar and wind hybrid energy generation system—make up the energy systems that generate the electrical fuel. According to experts in renewable energy, compact hybrid systems that integrate wind and solar energy have some advantages for use in domestic applications. Energy will be the most crucial resource because, without electricity, daily existence will be impossible in the future. The concept behind a hybrid power generation is to obtain continuous electricity during the day and at night for both small-scale power usage and the battery system [4].

Solar wind hybrid energy system (SWHES) are used to generate the entire amount of power from the wind and solar dual generating units. Depending on the amount of required load, these systems enter service mode. The remaining time is spent charging this battery feeding system. We can operate a wind and solar power project simultaneously in this single unit to obtain continuous energy supply, increasing the overall system efficiency through this integrated mode of operation. With batteries serving as the storage option for household applications, the combined power generation would offer a constant supply of power.

The state of Uttarakhand has achieved the biggest advances over the past few years, yet the state is still developing in the country. Because of the state’s steep terrain, the improvement graph is much clearer than in other states. Given that it requires the least amount of care, it appears that this project is being carried out to promote growth in the hilly region. There are thermal energy stations and hydroelectric power projects spread throughout the state of Uttarakhand that can handle the necessary demand [5].

2.1.1 Brief Introduction to the State of Uttarakhand

Following the dissolution of the state of Uttar Pradesh in 2000, Uttarakhand was created. Due to Uttar Pradesh’s division, Uttarakhand is now included in the region’s development plan. However, Uttarakhand was given the status of an autonomous region in response to the expanding political and economic activities, taking into account the territory’s expansion and infrastructure requirements, as well as those of the capital Dehradun, the GIS map of Uttarakhand state is given in Figure 2.1. This has increased the state’s energy needs as well but does not address the current power situation. According to the Central Electricity Authority, Uttarakhand was expected to use 8,363 MU of usable energy and 10,480 MU of electricity between 2019 and 2020. However, the state’s overall demand during this period was 1,600 MW, while the available capacity was only 1,430 MW, leading to a significant energy shortfall. Although Uttarakhand has undoubtedly improved over the past few years, improvement has not been universal. Because the majority of the state’s production units are located in its plain regions, Uttarakhand’s growth cycle is limited to those areas, with the exception of its hilly areas. In some states, 40% of the people still struggle to make ends meet. Even if the unemployment rate in the state is too low and there are a lot of poor people, Uttarakhand is an odd instance of poverty, this refers to an economy’s low rate of wages and profits, which results in a particular issue known as the primary problem of subpar working conditions. In the state of Uttarakhand, there is currently a need to provide the rural inhabitants with sustainable work. A rural community can do this by having efficient and secure access to power. Furthermore, construction methods are heavily dependent on the traditional energy sources that function in a plains setting, probably because of the challenging terrain that they require. These terrain types did not prove to be highly productive in the hills [6].

Figure 2.1 GIS Map of the Uttarakhand state.

2.1.2 Solar and Wind Energy Availability in Uttarakhand State

A total integrated aggregate of 576.38 MW of renewable power (grid linked and decentralized power stations) is used in the state in addition to renewable sources. Small hydro and bioenergy have been employed to a maximum of 45.46% (262 MW), 34.31% (197.78 MW), and 20.23% (116.60 MW) total. The Tehri region of Uttarakhand has been prepared to be one of the first Himalayan locales, and the state government has also been given permission to construct a wind power facility on top of a hill in Bachelikhal. For the 2.4 MW of wind energy output, this impact has previously been announced in a circular. The Chennai Wind Energy Technology Centre laid the groundwork and conducted all other surveys on the effectiveness of wind energy in the Bachelikhal area (CWET). In order to understand the potential of wind power in the state’s steep hills, Uttarakhand Renewable Energy Development Agency (UREDA) plans to install wind mapping equipment there. Through CWET, UREDA has begun to negotiate the mounting of particular equipment for such a purpose [7].

2.1.2.1 The Solar Statistics of the Uttarakhand State

Figures 2.2 and 2.3 show that solar insolation, also known as solar radiation, refers to the amount of solar energy received per unit area (typically per square meter) on the Earth’s surface. It represents the energy from the Sun that reaches a particular location and is an essential factor in understanding and harnessing solar energy for various purposes, including electricity generation, heating, and powering of devices. Several factors influence the variation of solar insolation throughout the year, including the following [25–30]:

Seasonal Changes

: The angle of the Sun relative to the Earth’s surface changes throughout the year due to the tilt of the Earth’s axis and its orbit around the Sun. This results in seasonal variations in solar insolation, with higher levels during the summer months when the Sun is more directly overhead and lower levels during the winter months when the Sun is lower in the sky.

Figure 2.2 Monthly solar insolation from 2009 to 2023 in kilowatt hours per meter square.

Figure 2.3 Long-term monthly solar insolation from year with minimum, maximum, average, and standard value.

Day Length

: The duration of daylight varies throughout the year, with longer days in the summer and shorter days in the winter. This affects the total amount of solar energy received in a day and contributes to seasonal variations in solar insolation.

Weather Conditions

: Cloud cover, atmospheric pollution, and other weather phenomena can affect the amount of sunlight reaching the Earth’s surface. Cloudy days result in lower levels of solar insolation compared to clear days.

Latitude

: Solar insolation varies with latitude, with higher levels of solar radiation received near the equator and lower levels toward the poles. This is due to the angle at which sunlight strikes the Earth’s surface, with more direct sunlight at lower latitudes.

Topography

: The terrain and elevation of a location can also influence solar insolation. Areas with high mountains or tall buildings may experience shading effects that reduce the amount of sunlight reaching the surface, whereas flat, unobstructed areas receive more direct sunlight.

The Figure 2.4 illustrates the long-term monthly temperature variation (in °C) throughout the year, with the red curve representing maximum temperatures and the black curve showing minimum temperatures. The temperatures gradually rise from January, peaking in May and June,