Intelligent Optimization Modelling In Energy Forecasting

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Intelligent Optimization Modelling In Energy Forecasting

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Author : Wei-Chiang Hong
language : en
Publisher: MDPI
Release Date : 2020-04-01

Intelligent Optimization Modelling In Energy Forecasting written by Wei-Chiang Hong and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-01 with Computers categories.

Accurate energy forecasting is important to facilitate the decision-making process in order to achieve higher efficiency and reliability in power system operation and security, economic energy use, contingency scheduling, the planning and maintenance of energy supply systems, and so on. In recent decades, many energy forecasting models have been continuously proposed to improve forecasting accuracy, including traditional statistical models (e.g., ARIMA, SARIMA, ARMAX, multi-variate regression, exponential smoothing models, Kalman filtering, Bayesian estimation models, etc.) and artificial intelligence models (e.g., artificial neural networks (ANNs), knowledge-based expert systems, evolutionary computation models, support vector regression, etc.). Recently, due to the great development of optimization modeling methods (e.g., quadratic programming method, differential empirical mode method, evolutionary algorithms, meta-heuristic algorithms, etc.) and intelligent computing mechanisms (e.g., quantum computing, chaotic mapping, cloud mapping, seasonal mechanism, etc.), many novel hybrid models or models combined with the above-mentioned intelligent-optimization-based models have also been proposed to achieve satisfactory forecasting accuracy levels. It is important to explore the tendency and development of intelligent-optimization-based modeling methodologies and to enrich their practical performances, particularly for marine renewable energy forecasting.

Intelligent Optimization Modelling In Energy Forecasting

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Author : Wei-Chiang Hong
language : en
Publisher:
Release Date : 2020

Intelligent Optimization Modelling In Energy Forecasting written by Wei-Chiang Hong and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Electronic computers. Computer science categories.

Accurate energy forecasting is important to facilitate the decision-making process in order to achieve higher efficiency and reliability in power system operation and security, economic energy use, contingency scheduling, the planning and maintenance of energy supply systems, and so on. In recent decades, many energy forecasting models have been continuously proposed to improve forecasting accuracy, including traditional statistical models (e.g., ARIMA, SARIMA, ARMAX, multi-variate regression, exponential smoothing models, Kalman filtering, Bayesian estimation models, etc.) and artificial intelligence models (e.g., artificial neural networks (ANNs), knowledge-based expert systems, evolutionary computation models, support vector regression, etc.). Recently, due to the great development of optimization modeling methods (e.g., quadratic programming method, differential empirical mode method, evolutionary algorithms, meta-heuristic algorithms, etc.) and intelligent computing mechanisms (e.g., quantum computing, chaotic mapping, cloud mapping, seasonal mechanism, etc.), many novel hybrid models or models combined with the above-mentioned intelligent-optimization-based models have also been proposed to achieve satisfactory forecasting accuracy levels. It is important to explore the tendency and development of intelligent-optimization-based modeling methodologies and to enrich their practical performances, particularly for marine renewable energy forecasting.

Intelligent Data Driven Modelling And Optimization In Power And Energy Applications

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Author : B Rajanarayan Prusty
language : en
Publisher: CRC Press
Release Date : 2024-05-09

Intelligent Data Driven Modelling And Optimization In Power And Energy Applications written by B Rajanarayan Prusty and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-09 with Technology & Engineering categories.

This book provides a comprehensive understanding of how intelligent data-driven techniques can be used for modelling, controlling, and optimizing various power and energy applications. It aims to develop multiple data-driven models for forecasting renewable energy sources and to interpret the benefits of these techniques in line with first-principles modelling approaches. By doing so, the book aims to stimulate deep insights into computational intelligence approaches in data-driven models and to promote their potential applications in the power and energy sectors. Its key features include: an exclusive section on essential preprocessing approaches for the data-driven model a detailed overview of data-driven model applications to power system planning and operational activities specific focus on developing forecasting models for renewable generations such as solar PV and wind power, and showcasing the judicious amalgamation of allied mathematical treatments such as optimization and fractional calculus in data-driven model-based frameworks This book presents novel concepts for applying data-driven models, mainly in the power and energy sectors, and is intended for graduate students, industry professionals, research, and academic personnel.

Hybrid Intelligent Technologies In Energy Demand Forecasting

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Author : Wei-Chiang Hong
language : en
Publisher: Springer Nature
Release Date : 2020-01-01

Hybrid Intelligent Technologies In Energy Demand Forecasting written by Wei-Chiang Hong and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-01 with Business & Economics categories.

This book is written for researchers and postgraduates who are interested in developing high-accurate energy demand forecasting models that outperform traditional models by hybridizing intelligent technologies. It covers meta-heuristic algorithms, chaotic mapping mechanism, quantum computing mechanism, recurrent mechanisms, phase space reconstruction, and recurrence plot theory. The book clearly illustrates how these intelligent technologies could be hybridized with those traditional forecasting models. This book provides many figures to deonstrate how these hybrid intelligent technologies are being applied to exceed the limitations of existing models.

Intelligent Energy Demand Forecasting

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Author : Wei-Chiang Hong
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-12

Intelligent Energy Demand Forecasting written by Wei-Chiang Hong and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-12 with Business & Economics categories.

As industrial, commercial, and residential demands increase and with the rise of privatization and deregulation of the electric energy industry around the world, it is necessary to improve the performance of electric operational management. Intelligent Energy Demand Forecasting offers approaches and methods to calculate optimal electric energy allocation to reach equilibrium of the supply and demand. Evolutionary algorithms and intelligent analytical tools to improve energy demand forecasting accuracy are explored and explained in relation to existing methods. To provide clearer picture of how these hybridized evolutionary algorithms and intelligent analytical tools are processed, Intelligent Energy Demand Forecasting emphasizes on improving the drawbacks of existing algorithms. Written for researchers, postgraduates, and lecturers, Intelligent Energy Demand Forecasting helps to develop the skills and methods to provide more accurate energy demand forecasting by employing novel hybridized evolutionary algorithms and intelligent analytical tools.

Predictive Modelling For Energy Management And Power Systems Engineering

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Author : Ravinesh Deo
language : en
Publisher: Elsevier
Release Date : 2020-09-30

Predictive Modelling For Energy Management And Power Systems Engineering written by Ravinesh Deo and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-30 with Science categories.

Predictive Modeling for Energy Management and Power Systems Engineering introduces readers to the cutting-edge use of big data and large computational infrastructures in energy demand estimation and power management systems. The book supports engineers and scientists who seek to become familiar with advanced optimization techniques for power systems designs, optimization techniques and algorithms for consumer power management, and potential applications of machine learning and artificial intelligence in this field. The book provides modeling theory in an easy-to-read format, verified with on-site models and case studies for specific geographic regions and complex consumer markets. Presents advanced optimization techniques to improve existing energy demand system Provides data-analytic models and their practical relevance in proven case studies Explores novel developments in machine-learning and artificial intelligence applied in energy management Provides modeling theory in an easy-to-read format

Intelligent Renewable Energy Systems

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Author : Neeraj Priyadarshi
language : en
Publisher: John Wiley & Sons
Release Date : 2022-01-19

Intelligent Renewable Energy Systems written by Neeraj Priyadarshi and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-19 with Computers categories.

INTELLIGENT RENEWABLE ENERGY SYSTEMS This collection of papers on artificial intelligence and other methods for improving renewable energy systems, written by industry experts, is a reflection of the state of the art, a must-have for engineers, maintenance personnel, students, and anyone else wanting to stay abreast with current energy systems concepts and technology. Renewable energy is one of the most important subjects being studied, researched, and advanced in today’s world. From a macro level, like the stabilization of the entire world’s economy, to the micro level, like how you are going to heat or cool your home tonight, energy, specifically renewable energy, is on the forefront of the discussion. This book illustrates modelling, simulation, design and control of renewable energy systems employed with recent artificial intelligence (AI) and optimization techniques for performance enhancement. Current renewable energy sources have less power conversion efficiency because of its intermittent and fluctuating behavior. Therefore, in this regard, the recent AI and optimization techniques are able to deal with data ambiguity, noise, imprecision, and nonlinear behavior of renewable energy sources more efficiently compared to classical soft computing techniques. This book provides an extensive analysis of recent state of the art AI and optimization techniques applied to green energy systems. Subsequently, researchers, industry persons, undergraduate and graduate students involved in green energy will greatly benefit from this comprehensive volume, a must-have for any library. Audience Engineers, scientists, managers, researchers, students, and other professionals working in the field of renewable energy.

Short Term Load Forecasting By Artificial Intelligent Technologies

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Author : Wei-Chiang Hong
language : en
Publisher: MDPI
Release Date : 2019-01-29

Short Term Load Forecasting By Artificial Intelligent Technologies written by Wei-Chiang Hong and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-29 with categories.

This book is a printed edition of the Special Issue "Short-Term Load Forecasting by Artificial Intelligent Technologies" that was published in Energies

Energy Management Systems

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Author : Giridhar Kini
language : en
Publisher: IntechOpen
Release Date : 2011-08-01

Energy Management Systems written by Giridhar Kini and has been published by IntechOpen this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-08-01 with Technology & Engineering categories.

This book comprises of 13 chapters and is written by experts from industries, and academics from countries such as USA, Canada, Germany, India, Australia, Spain, Italy, Japan, Slovenia, Malaysia, Mexico, etc. This book covers many important aspects of energy management, forecasting, optimization methods and their applications in selected industrial, residential, generation system. This book also captures important aspects of smart grid and photovoltaic system. Some of the key features of books are as follows: Energy management methodology in industrial plant with a case study; Online energy system optimization modelling; Energy optimization case study; Energy demand analysis and forecast; Energy management in intelligent buildings; PV array energy yield case study of Slovenia;Optimal design of cooling water systems; Supercapacitor design methodology for transportation; Locomotive tractive energy resources management; Smart grid and dynamic power management.

Practical Examples Of Energy Optimization Models

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Author : Samsul Ariffin Abdul Karim
language : en
Publisher: Springer Nature
Release Date : 2020-01-02

Practical Examples Of Energy Optimization Models written by Samsul Ariffin Abdul Karim and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-02 with Technology & Engineering categories.

This book highlights state-of-the-art research on renewable energy integration technology and suitable and efficient power generation, discussing smart grids, renewable energy grid integration, prediction control models, and econometric models for predicting the global solar radiation and factors that affect solar radiation, performance evaluation of photovoltaic systems, and improved energy consumption prediction models. It discusses several methods, algorithms, environmental data-based performance analyses, and experimental results to help readers gain a detailed understanding of the pros and cons of technologies in this rapidly growing area. Accordingly, it offers a valuable resource for students and researchers working on renewable energy optimization models.