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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Bibliographic Details
Other Authors: Piotrowski, Paweł (Editor), Dudek, Grzegorz (Editor), Baczyński, Dariusz (Editor)
Format: Electronic Book Chapter
Language:English
Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2023
Subjects:
GA
PSO
n/a
Online Access:DOAB: download the publication
DOAB: description of the publication
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700 1 |a Piotrowski, Paweł  |4 oth 
700 1 |a Dudek, Grzegorz  |4 oth 
700 1 |a Baczyński, Dariusz  |4 oth 
245 1 0 |a Intelligent Forecasting and Optimization in Electrical Power Systems 
260 |a Basel  |b MDPI - Multidisciplinary Digital Publishing Institute  |c 2023 
300 |a 1 electronic resource (468 p.) 
336 |a text  |b txt  |2 rdacontent 
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338 |a online resource  |b cr  |2 rdacarrier 
506 0 |a Open Access  |2 star  |f Unrestricted online access 
520 |a 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. 
540 |a Creative Commons  |f https://creativecommons.org/licenses/by/4.0/  |2 cc  |4 https://creativecommons.org/licenses/by/4.0/ 
546 |a English 
650 7 |a Technology: general issues  |2 bicssc 
650 7 |a History of engineering & technology  |2 bicssc 
650 7 |a Energy industries & utilities  |2 bicssc 
653 |a hybrid AC/DC microgrid 
653 |a optimization of configuration and operating states 
653 |a CLONALG 
653 |a modified hypermutation operator 
653 |a wind energy 
653 |a wind farm 
653 |a ensemble methods 
653 |a short-term forecasting 
653 |a electric energy production 
653 |a machine learning 
653 |a deep neural network 
653 |a swarm intelligence 
653 |a voltage control 
653 |a voltage quality 
653 |a renewable energy 
653 |a metaheuristic optimisation 
653 |a medium voltage 
653 |a Q(U) characteristics 
653 |a microgrids 
653 |a operation control 
653 |a power generation 
653 |a PV system 
653 |a very-short-term forecasting 
653 |a interval type-2 fuzzy logic system 
653 |a distribution of electric power 
653 |a distributed storage and generation 
653 |a smart grids 
653 |a power distribution reliability 
653 |a information and communication technology 
653 |a energy efficiency 
653 |a cooling towers 
653 |a chillers 
653 |a evolutionary multi-objective optimization 
653 |a mid-term forecast 
653 |a e-mobility 
653 |a electric vehicles (EVs) 
653 |a power system demand 
653 |a load profile forecast 
653 |a machine learning (ML) 
653 |a electricity load forecasting 
653 |a bootstrap aggregating 
653 |a singular spectrum analysis 
653 |a time series forecasting 
653 |a calendar variation 
653 |a electrical power demand 
653 |a power systems 
653 |a autoregressive forecasting methods 
653 |a classical forecasting methods 
653 |a artificial intelligence methods 
653 |a Big Data 
653 |a Data Mining 
653 |a auto-regressive integrated moving average (ARIMA) 
653 |a long short-term memory (LSTM) 
653 |a Optuna 
653 |a isolation forest (IF) 
653 |a elliptic envelope (EE) 
653 |a one-class support vector machine (OCSVM) 
653 |a neuromorphic computing 
653 |a spiking neural network 
653 |a short-term wind power forecasting 
653 |a random forest 
653 |a regression tree 
653 |a pattern representation of time series 
653 |a short-term load forecasting 
653 |a transfer learning 
653 |a wind power 
653 |a photovolatic power 
653 |a autoencoders 
653 |a deep learning 
653 |a time series 
653 |a wind power prediction 
653 |a hybrid methods 
653 |a time series analysis 
653 |a forecasting error 
653 |a evaluation criteria metrics 
653 |a wind power forecasting 
653 |a wind turbine 
653 |a statistical analysis of errors 
653 |a medium-term load forecasting 
653 |a pattern-based forecasting 
653 |a time-series preprocessing 
653 |a photovoltaic (PV) 
653 |a forecast 
653 |a behind-the-meter (BTM) 
653 |a spatio-temporal 
653 |a strategic training 
653 |a deep neural networks 
653 |a LSTM 
653 |a time series prediction 
653 |a optimisation 
653 |a GA 
653 |a PSO 
653 |a n/a 
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856 4 0 |a www.oapen.org  |u https://directory.doabooks.org/handle/20.500.12854/128787  |7 0  |z DOAB: description of the publication