Intelligent Forecasting and Optimization in Electrical Power Systems

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 t...

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Hōputu:	Online
Reo:	Ingarihi
I whakaputaina:	MDPI - Multidisciplinary Digital Publishing Institute 2023
Ngā marau:	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 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 electrical power demand power systems autoregressive forecasting methods classical forecasting methods artificial intelligence methods Big Data 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 hybrid methods time series analysis forecasting error evaluation criteria metrics wind power forecasting wind turbine statistical analysis of errors 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 thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNB Energy industries and utilities
Urunga tuihono:	ONIX_20231130_9783036590806_239
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Whakaahuatanga
Whakarāpopototanga:	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.

Intelligent Forecasting and Optimization in Electrical Power Systems

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