Intelligent Forecasting and Optimization in Electrical Power Systems

doi:10.3390/books978-3-0365-9081-3

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Intelligent Forecasting and Optimization in Electrical Power Systems

Edited by

November 2023

468 pages

ISBN978-3-0365-9080-6 (Hardback)
ISBN978-3-0365-9081-3 (PDF)

https://doi.org/10.3390/books978-3-0365-9081-3

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This book is a reprint of the Special Issue Intelligent Forecasting and Optimization in Electrical Power Systems that was published in

Chemistry & Materials Science

Engineering

Environmental & Earth Sciences

Physical Sciences

Summary

This reprint explores the latest developments and advancements in the application of artificial intelligence (AI) and machine learning (ML) for forecasting and optimization in the field of power engineering. In recent years, AI and ML methods have been gaining significant traction and are becoming two of the most important fields in computing. These methods have proven to be effective in solving forecasting and optimization problems in power engineering. The topics covered in the chapters fall into four categories: electricity demand forecasting, wind power forecasting, photovoltaic power forecasting, and optimization.

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Hardback

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Keywords

hybrid AC/DC microgrid; optimization of configuration and operating states; CLONALG; modified hypermutation operator; wind energy; wind farm; ensemble methods; short-term forecasting; electric energy production; machine learning; deep neural network; swarm intelligence; voltage control; voltage quality; renewable energy; metaheuristic optimisation; medium voltage; Q(U) characteristics; microgrids; operation control; power generation; PV system; very-short-term forecasting; machine learning; interval type-2 fuzzy logic system; distribution of electric power; distributed storage and generation; smart grids; power distribution reliability; information and communication technology; energy efficiency; cooling towers; chillers; evolutionary multi-objective optimization; mid-term forecast; e-mobility; electric vehicles (EVs); power system demand; load profile forecast; machine learning (ML); electricity load forecasting; bootstrap aggregating; singular spectrum analysis; time series forecasting; calendar variation; short-term forecasting; electrical power demand; power systems; autoregressive forecasting methods; classical forecasting methods; artificial intelligence methods; Big Data; machine learning; Data Mining; auto-regressive integrated moving average (ARIMA); long short-term memory (LSTM); Optuna; isolation forest (IF); elliptic envelope (EE); one-class support vector machine (OCSVM); neuromorphic computing; spiking neural network; short-term wind power forecasting; random forest; regression tree; pattern representation of time series; short-term load forecasting; transfer learning; wind power; photovolatic power; autoencoders; deep learning; time series; wind power prediction; machine learning; deep learning; hybrid methods; time series analysis; forecasting error; evaluation criteria metrics; wind power forecasting; wind turbine; wind farm; statistical analysis of errors; hybrid methods; ensemble methods; machine learning; deep neural network; medium-term load forecasting; pattern-based forecasting; time-series preprocessing; photovoltaic (PV); forecast; behind-the-meter (BTM); spatio-temporal; strategic training; deep neural networks; LSTM; time series prediction; optimisation; GA; PSO; n/a