Rupendra Kumar Pachauri – författare

Applied Soft Computing and Embedded System Applications in Solar Energy deals with energy systems and soft computing methods from a wide range of approaches and application perspectives. The authors examine how embedded system applications can deal with the smart monitoring and controlling of stand-alone and grid-connected solar photovoltaic (PV) systems for increased efficiency. Growth in the area of artificial intelligence with embedded system applications has led to a new era in computing, impacting almost all fields of science and engineering. Soft computing methods implemented to energy-related problems regularly face data-driven issues such as problems of optimization, classification, clustering, or prediction. The authors offer real-time implementation of soft computing and embedded system in the area of solar energy to address the issues with microgrid and smart grid projects (both renewable and non-renewable generations), energy management, and power regulation. They also discuss and examine alternative solutions for energy capacity assessment, energy efficiency systems design, as well as other specific smart grid energy system applications. The book is intended for students, professionals, and researchers in electrical and computer engineering fields, working on renewable energy resources, microgrids, and smart grid projects.

Dr. Rupendra Kumar Pachauri is Assistant Professor – Selection Grade in the Department of Electrical and Electronics Engineering, University of Petroleum and Energy Studies (UPES), Dehradun, India.

Dr. Jitendra Kumar Pandey is Professor & Head of R&D in the University of Petroleum and Energy Studies (UPES), Dehradun, India.

Mr. Abhishek Sharma is working as a research scientist in the research and development department (UPES, India).

Dr. Om Prakash Nautiyal is working as a scientist in Uttarakhand Science Education & Research Centre (USERC), Department of Information and Science Technology, Govt. of Uttarakhand, Dehradun, India.

Prof. Mangey Ram is working as a Research Professor at Graphic Era Deemed to be University, Dehradun, India.

Applied Soft Computing and Embedded System Applications in Solar Energy deals with energy systems and soft computing methods from a wide range of approaches and application perspectives. The authors examine how embedded system applications can deal with the smart monitoring and controlling of stand-alone and grid-connected solar photovoltaic (PV) systems for increased efficiency. Growth in the area of artificial intelligence with embedded system applications has led to a new era in computing, impacting almost all fields of science and engineering. Soft computing methods implemented to energy-related problems regularly face data-driven issues such as problems of optimization, classification, clustering, or prediction. The authors offer real-time implementation of soft computing and embedded system in the area of solar energy to address the issues with microgrid and smart grid projects (both renewable and non-renewable generations), energy management, and power regulation. They also discuss and examine alternative solutions for energy capacity assessment, energy efficiency systems design, as well as other specific smart grid energy system applications. The book is intended for students, professionals, and researchers in electrical and computer engineering fields, working on renewable energy resources, microgrids, and smart grid projects.

Dr. Rupendra Kumar Pachauri is Assistant Professor – Selection Grade in the Department of Electrical and Electronics Engineering, University of Petroleum and Energy Studies (UPES), Dehradun, India.

Dr. Jitendra Kumar Pandey is Professor & Head of R&D in the University of Petroleum and Energy Studies (UPES), Dehradun, India.

Mr. Abhishek Sharma is working as a research scientist in the research and development department (UPES, India).

Dr. Om Prakash Nautiyal is working as a scientist in Uttarakhand Science Education & Research Centre (USERC), Department of Information and Science Technology, Govt. of Uttarakhand, Dehradun, India.

Prof. Mangey Ram is working as a Research Professor at Graphic Era Deemed to be University, Dehradun, India.

CLEAN and RENEWABLE ENERGY PRODUCTION

According to the World Renewable Energy Council (WREC), by the year 2100, the world''s population will increase to 12 billion and the worldwide energy demand will increase steeply to about five times the present scenario. Researchers are striving to find alternative forms of energy, and this quest is strongly forced by the increasing worry over climate change and planetary heating. Among the diverse varieties of alternative energy sources, biomass has the singular advantage of being carbon neutral. The carbon that is discharged to the atmosphere during its exercise is read back during the utilization of biomass resources for energy output. Currently, biomass provides approximately 13% of the world''s primary energy supply and more than 75% of global renewable energy. Indeed, it is estimated that bioenergy could contribute 25–33% of the global energy supply by 2050. Continued adoption of biomass will require efficient conversion rates and avoidance of competition with food and fibers.

This book focuses on the recent practices in clean energy and renewable energy. The contributors highlight how newer technologies are reducing the dependency on non-renewable resources, benefiting the researchers who are working in the area of clean and renewable energy production. This new volume will also benefit mechanical engineers, electrical engineers, and bioengineers as they will be updated with the recent work progressing all over the globe. It will benefit the professionals working in the renewable energy sector such as solar, wind, hydrothermal, hydrogen, and bioenergy, including professors, research scholars, industry professionals, and students working in this field.

CLEAN and RENEWABLE ENERGY PRODUCTION

According to the World Renewable Energy Council (WREC), by the year 2100, the world''s population will increase to 12 billion and the worldwide energy demand will increase steeply to about five times the present scenario. Researchers are striving to find alternative forms of energy, and this quest is strongly forced by the increasing worry over climate change and planetary heating. Among the diverse varieties of alternative energy sources, biomass has the singular advantage of being carbon neutral. The carbon that is discharged to the atmosphere during its exercise is read back during the utilization of biomass resources for energy output. Currently, biomass provides approximately 13% of the world''s primary energy supply and more than 75% of global renewable energy. Indeed, it is estimated that bioenergy could contribute 25–33% of the global energy supply by 2050. Continued adoption of biomass will require efficient conversion rates and avoidance of competition with food and fibers.

This book focuses on the recent practices in clean energy and renewable energy. The contributors highlight how newer technologies are reducing the dependency on non-renewable resources, benefiting the researchers who are working in the area of clean and renewable energy production. This new volume will also benefit mechanical engineers, electrical engineers, and bioengineers as they will be updated with the recent work progressing all over the globe. It will benefit the professionals working in the renewable energy sector such as solar, wind, hydrothermal, hydrogen, and bioenergy, including professors, research scholars, industry professionals, and students working in this field.

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.

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.