Comparison of the Deep Learning Performance for Short-Term Power Load Forecasting

Electricity demand forecasting enables the stable operation of electric power systems and reduces electric power consumption. Previous studies have predicted electricity demand through a correlation analysis between power consumption and weather data; however, this analysis does not consider the inf...

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Autor principal:	Namrye Son
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	electric load forecasting deep learning multilayer perceptron recurrent neural network convolution neural network long short-term memory Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/372085b571954cafa5afab648326f3f0
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spelling	oai:doaj.org-article:372085b571954cafa5afab648326f3f02021-11-25T19:01:32ZComparison of the Deep Learning Performance for Short-Term Power Load Forecasting10.3390/su1322124932071-1050https://doaj.org/article/372085b571954cafa5afab648326f3f02021-11-01T00:00:00Zhttps://www.mdpi.com/2071-1050/13/22/12493https://doaj.org/toc/2071-1050Electricity demand forecasting enables the stable operation of electric power systems and reduces electric power consumption. Previous studies have predicted electricity demand through a correlation analysis between power consumption and weather data; however, this analysis does not consider the influence of various factors on power consumption, such as industrial activities, economic factors, power horizon, and resident living patterns of buildings. This study proposes an efficient power demand prediction using deep learning techniques for two industrial buildings with different power consumption patterns. The problems are presented by analyzing the correlation between the power consumption and weather data by season for industrial buildings with different power consumption patterns. Four models were analyzed using the most important factors for predicting power consumption and weather data (temperature, humidity, sunlight, solar radiation, total cloud cover, wind speed, wind direction, humidity, and vapor pressure). The prediction horizon for power consumption forecasting was kept at 24 h. The existing deep learning methods (DNN, RNN, CNN, and LSTM) cannot accurately predict power consumption when it increases or decreases rapidly. Hence, a method to reduce this prediction error is proposed. DNN, RNN, and LSTM were superior when using two-year electricity consumption rather than one-year electricity consumption and weather data.Namrye SonMDPI AGarticleelectric load forecastingdeep learningmultilayer perceptronrecurrent neural networkconvolution neural networklong short-term memoryEnvironmental effects of industries and plantsTD194-195Renewable energy sourcesTJ807-830Environmental sciencesGE1-350ENSustainability, Vol 13, Iss 12493, p 12493 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	electric load forecasting deep learning multilayer perceptron recurrent neural network convolution neural network long short-term memory Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
spellingShingle	electric load forecasting deep learning multilayer perceptron recurrent neural network convolution neural network long short-term memory Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 Namrye Son Comparison of the Deep Learning Performance for Short-Term Power Load Forecasting
description	Electricity demand forecasting enables the stable operation of electric power systems and reduces electric power consumption. Previous studies have predicted electricity demand through a correlation analysis between power consumption and weather data; however, this analysis does not consider the influence of various factors on power consumption, such as industrial activities, economic factors, power horizon, and resident living patterns of buildings. This study proposes an efficient power demand prediction using deep learning techniques for two industrial buildings with different power consumption patterns. The problems are presented by analyzing the correlation between the power consumption and weather data by season for industrial buildings with different power consumption patterns. Four models were analyzed using the most important factors for predicting power consumption and weather data (temperature, humidity, sunlight, solar radiation, total cloud cover, wind speed, wind direction, humidity, and vapor pressure). The prediction horizon for power consumption forecasting was kept at 24 h. The existing deep learning methods (DNN, RNN, CNN, and LSTM) cannot accurately predict power consumption when it increases or decreases rapidly. Hence, a method to reduce this prediction error is proposed. DNN, RNN, and LSTM were superior when using two-year electricity consumption rather than one-year electricity consumption and weather data.
format	article
author	Namrye Son
author_facet	Namrye Son
author_sort	Namrye Son
title	Comparison of the Deep Learning Performance for Short-Term Power Load Forecasting
title_short	Comparison of the Deep Learning Performance for Short-Term Power Load Forecasting
title_full	Comparison of the Deep Learning Performance for Short-Term Power Load Forecasting
title_fullStr	Comparison of the Deep Learning Performance for Short-Term Power Load Forecasting
title_full_unstemmed	Comparison of the Deep Learning Performance for Short-Term Power Load Forecasting
title_sort	comparison of the deep learning performance for short-term power load forecasting
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/372085b571954cafa5afab648326f3f0
work_keys_str_mv	AT namryeson comparisonofthedeeplearningperformanceforshorttermpowerloadforecasting
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Comparison of the Deep Learning Performance for Short-Term Power Load Forecasting

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