Deep Learning for Short-Term Load Forecasting—Industrial Consumer Case Study

In the current trend of consumption, electricity consumption will become a very high cost for the end-users. Consumers acquire energy from suppliers who use short, medium, and long-term forecasts to place bids in the power market. This study offers a detailed analysis of relevant literature and prop...

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Autores principales:	Stefan Ungureanu, Vasile Topa, Andrei Cristinel Cziker
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	machine learning deep learning short-term forecasting industrial electricity load Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/4ff471bcf9b84a75918e0375669cd943
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spelling	oai:doaj.org-article:4ff471bcf9b84a75918e0375669cd9432021-11-11T15:11:24ZDeep Learning for Short-Term Load Forecasting—Industrial Consumer Case Study10.3390/app1121101262076-3417https://doaj.org/article/4ff471bcf9b84a75918e0375669cd9432021-10-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10126https://doaj.org/toc/2076-3417In the current trend of consumption, electricity consumption will become a very high cost for the end-users. Consumers acquire energy from suppliers who use short, medium, and long-term forecasts to place bids in the power market. This study offers a detailed analysis of relevant literature and proposes a deep learning methodology for forecasting industrial electric usage for the next 24 h. The hourly load curves forecasted are from a large furniture factory. The hourly data for one year is split into training (80%) and testing (20%). The algorithms use the previous two weeks of hourly consumption and exogenous variables as input in the deep neural networks. The best results prove that deep recurrent neural networks can retain long-term dependencies in high volatility time series. Gated recurrent units (GRU) obtained the lowest mean absolute percentage error of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>4.82</mn><mo>%</mo></mrow></semantics></math></inline-formula> for the testing period. The GRU improves the forecast by <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>6.23</mn><mo>%</mo></mrow></semantics></math></inline-formula> compared to the second-best algorithm implemented, a combination of GRU and Long short-term memory (LSTM). From a practical perspective, deep learning methods can automate the forecasting processes and optimize the operation of power systems.Stefan UngureanuVasile TopaAndrei Cristinel CzikerMDPI AGarticlemachine learningdeep learningshort-term forecastingindustrial electricity loadTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10126, p 10126 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	machine learning deep learning short-term forecasting industrial electricity load Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	machine learning deep learning short-term forecasting industrial electricity load Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Stefan Ungureanu Vasile Topa Andrei Cristinel Cziker Deep Learning for Short-Term Load Forecasting—Industrial Consumer Case Study
description	In the current trend of consumption, electricity consumption will become a very high cost for the end-users. Consumers acquire energy from suppliers who use short, medium, and long-term forecasts to place bids in the power market. This study offers a detailed analysis of relevant literature and proposes a deep learning methodology for forecasting industrial electric usage for the next 24 h. The hourly load curves forecasted are from a large furniture factory. The hourly data for one year is split into training (80%) and testing (20%). The algorithms use the previous two weeks of hourly consumption and exogenous variables as input in the deep neural networks. The best results prove that deep recurrent neural networks can retain long-term dependencies in high volatility time series. Gated recurrent units (GRU) obtained the lowest mean absolute percentage error of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>4.82</mn><mo>%</mo></mrow></semantics></math></inline-formula> for the testing period. The GRU improves the forecast by <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>6.23</mn><mo>%</mo></mrow></semantics></math></inline-formula> compared to the second-best algorithm implemented, a combination of GRU and Long short-term memory (LSTM). From a practical perspective, deep learning methods can automate the forecasting processes and optimize the operation of power systems.
format	article
author	Stefan Ungureanu Vasile Topa Andrei Cristinel Cziker
author_facet	Stefan Ungureanu Vasile Topa Andrei Cristinel Cziker
author_sort	Stefan Ungureanu
title	Deep Learning for Short-Term Load Forecasting—Industrial Consumer Case Study
title_short	Deep Learning for Short-Term Load Forecasting—Industrial Consumer Case Study
title_full	Deep Learning for Short-Term Load Forecasting—Industrial Consumer Case Study
title_fullStr	Deep Learning for Short-Term Load Forecasting—Industrial Consumer Case Study
title_full_unstemmed	Deep Learning for Short-Term Load Forecasting—Industrial Consumer Case Study
title_sort	deep learning for short-term load forecasting—industrial consumer case study
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/4ff471bcf9b84a75918e0375669cd943
work_keys_str_mv	AT stefanungureanu deeplearningforshorttermloadforecastingindustrialconsumercasestudy AT vasiletopa deeplearningforshorttermloadforecastingindustrialconsumercasestudy AT andreicristinelcziker deeplearningforshorttermloadforecastingindustrialconsumercasestudy
_version_	1718436936545206272

Deep Learning for Short-Term Load Forecasting—Industrial Consumer Case Study

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