Intelligent Optimization Modelling In Energy Forecasting
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Intelligent Optimization Modelling in Energy Forecasting
Author | : Wei-Chiang Hong |
Publsiher | : MDPI |
Total Pages | : 262 |
Release | : 2020-04-01 |
Genre | : Computers |
ISBN | : 9783039283644 |
Download Intelligent Optimization Modelling in Energy Forecasting Book in PDF, Epub and Kindle
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
![Intelligent Optimization Modelling in Energy Forecasting](https://youbookinc.com/wp-content/uploads/2024/06/cover.jpg)
Author | : Wei-Chiang Hong |
Publsiher | : Unknown |
Total Pages | : 262 |
Release | : 2020 |
Genre | : Electronic computers. Computer science |
ISBN | : 3039283650 |
Download Intelligent Optimization Modelling in Energy Forecasting Book in PDF, Epub and Kindle
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
Author | : B Rajanarayan Prusty,Neeraj Gupta,Kishore Bingi,Rakesh Sehgal |
Publsiher | : CRC Press |
Total Pages | : 253 |
Release | : 2024-05-09 |
Genre | : Technology & Engineering |
ISBN | : 9781040016114 |
Download Intelligent Data Driven Modelling and Optimization in Power and Energy Applications Book in PDF, Epub and Kindle
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
Author | : Wei-Chiang Hong |
Publsiher | : Springer Nature |
Total Pages | : 179 |
Release | : 2020-01-01 |
Genre | : Business & Economics |
ISBN | : 9783030365295 |
Download Hybrid Intelligent Technologies in Energy Demand Forecasting Book in PDF, Epub and Kindle
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.
Predictive Modelling for Energy Management and Power Systems Engineering
Author | : Ravinesh Deo,Pijush Samui,Sanjiban Sekhar Roy |
Publsiher | : Elsevier |
Total Pages | : 553 |
Release | : 2020-09-30 |
Genre | : Science |
ISBN | : 9780128177730 |
Download Predictive Modelling for Energy Management and Power Systems Engineering Book in PDF, Epub and Kindle
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
Short Term Load Forecasting by Artificial Intelligent Technologies
Author | : Wei-Chiang Hong,Ming-Wei Li,Guo-Feng Fan |
Publsiher | : MDPI |
Total Pages | : 445 |
Release | : 2019-01-29 |
Genre | : Electronic Book |
ISBN | : 9783038975823 |
Download Short Term Load Forecasting by Artificial Intelligent Technologies Book in PDF, Epub and Kindle
This book is a printed edition of the Special Issue "Short-Term Load Forecasting by Artificial Intelligent Technologies" that was published in Energies
Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting
Author | : Wei-Chiang Hong |
Publsiher | : MDPI |
Total Pages | : 251 |
Release | : 2018-10-19 |
Genre | : Electronic books |
ISBN | : 9783038972860 |
Download Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting Book in PDF, Epub and Kindle
This book is a printed edition of the Special Issue "Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting" that was published in Energies
Intelligent Renewable Energy Systems
Author | : Neeraj Priyadarshi,Akash Kumar Bhoi,Sanjeevikumar Padmanaban,S. Balamurugan,Jens Bo Holm-Nielsen |
Publsiher | : John Wiley & Sons |
Total Pages | : 484 |
Release | : 2022-01-19 |
Genre | : Computers |
ISBN | : 9781119786276 |
Download Intelligent Renewable Energy Systems Book in PDF, Epub and Kindle
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.