Mithilesh Kumar Dubey – författare

Introduction to AI techniques for Renewable Energy System

Artificial Intelligence (AI) techniques play an essential role in modeling, analysis, and prediction of the performance and control of renewable energy. The algorithms used to model, control, or predict performances of the energy systems are complicated, involving differential equations, enormous computing power, and time requirements. Instead of complex rules and mathematical routines, AI techniques can learn critical information patterns within a multidimensional information domain. Design, control, and operation of renewable energy systems require a long-term series of meteorological data such as solar radiation, temperature, or wind data. Such long-term measurements are often non-existent for most of the interest locations or, wherever they are available, they suffer from several shortcomings, like inferior quality of data, and in-sufficient long series. The book focuses on AI techniques to overcome these problems. It summarizes commonly used AI methodologies in renewal energy, with a particular emphasis on neural networks, fuzzy logic, and genetic algorithms. It outlines selected AI applications for renewable energy. In particular, it discusses methods using the AI approach for prediction and modeling of solar radiation, seizing, performances, and controls of the solar photovoltaic (PV) systems.

Features

This book, which includes real-life examples, is aimed at undergraduate and graduate students and academicians studying AI techniques used in renewal energy systems.

Introduction to AI techniques for Renewable Energy System

Artificial Intelligence (AI) techniques play an essential role in modeling, analysis, and prediction of the performance and control of renewable energy. The algorithms used to model, control, or predict performances of the energy systems are complicated, involving differential equations, enormous computing power, and time requirements. Instead of complex rules and mathematical routines, AI techniques can learn critical information patterns within a multidimensional information domain. Design, control, and operation of renewable energy systems require a long-term series of meteorological data such as solar radiation, temperature, or wind data. Such long-term measurements are often non-existent for most of the interest locations or, wherever they are available, they suffer from several shortcomings, like inferior quality of data, and in-sufficient long series. The book focuses on AI techniques to overcome these problems. It summarizes commonly used AI methodologies in renewal energy, with a particular emphasis on neural networks, fuzzy logic, and genetic algorithms. It outlines selected AI applications for renewable energy. In particular, it discusses methods using the AI approach for prediction and modeling of solar radiation, seizing, performances, and controls of the solar photovoltaic (PV) systems.

Features

This book, which includes real-life examples, is aimed at undergraduate and graduate students and academicians studying AI techniques used in renewal energy systems.

The book Artificial Intelligence and Sustainable Agriculture for Solanaceae Crops provides a comprehensive exploration of artificial intelligence techniques and their transformative role in promoting sustainable agriculture, particularly for Solanaceae crops such as tomato, potato, and eggplant. Focusing on disease detection and prediction, the book highlights advanced AI applications, including dimensionality reduction, feature extraction, and the analysis of complex genomics and phenotypic data. It systematically presents the design, implementation, and evaluation of predictive models using widely adopted tools such as MATLAB and Python, while also addressing both software- and hardware-based solutions for enhancing genomics research and crop disease management. Through detailed case studies, experimental results, and practical examples, the book demonstrates how AI can optimize precision agriculture practices, improve crop yield, and support early warning systems for disease outbreaks. Serving as both a theoretical reference and a practical guide, it is an invaluable resource for researchers, graduate students, agronomists, and professional engineers aiming to leverage AI, image analysis, and predictive modeling to address real-world challenges in Solanaceae crop production and genomics.

The book Artificial Intelligence and Sustainable Agriculture for Solanaceae Crops provides a comprehensive exploration of artificial intelligence techniques and their transformative role in promoting sustainable agriculture, particularly for Solanaceae crops such as tomato, potato, and eggplant. Focusing on disease detection and prediction, the book highlights advanced AI applications, including dimensionality reduction, feature extraction, and the analysis of complex genomics and phenotypic data. It systematically presents the design, implementation, and evaluation of predictive models using widely adopted tools such as MATLAB and Python, while also addressing both software- and hardware-based solutions for enhancing genomics research and crop disease management. Through detailed case studies, experimental results, and practical examples, the book demonstrates how AI can optimize precision agriculture practices, improve crop yield, and support early warning systems for disease outbreaks. Serving as both a theoretical reference and a practical guide, it is an invaluable resource for researchers, graduate students, agronomists, and professional engineers aiming to leverage AI, image analysis, and predictive modeling to address real-world challenges in Solanaceae crop production and genomics.