Forecasting of Electrical Energy Consumption of Households in a Smart Grid

<p>This paper aims to develop a hybrid model for forecasting electrical energy consumption of households based on a Particle Swarm Optimization (PSO) algorithm associated with the Grey and Adaptive Neuro-Fuzzy Inference System (ANFIS). This paper proposes a new Grey-ANFIS-PSO model that is bas...

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Autores principales:	Felix Ghislain Yem Souhe, Camille Franklin Mbey, Alexandre Teplaira Boum, Pierre Ele
Formato:	article
Lenguaje:	EN
Publicado:	EconJournals 2021
Materias:	Environmental sciences GE1-350 Energy industries. Energy policy. Fuel trade HD9502-9502.5
Acceso en línea:	https://doaj.org/article/029ba97106a8451ebcd149f02bb4d37d
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spelling	oai:doaj.org-article:029ba97106a8451ebcd149f02bb4d37d2021-11-12T07:27:31ZForecasting of Electrical Energy Consumption of Households in a Smart Grid2146-4553https://doaj.org/article/029ba97106a8451ebcd149f02bb4d37d2021-11-01T00:00:00Zhttps://econjournals.com/index.php/ijeep/article/view/11761https://doaj.org/toc/2146-4553<p>This paper aims to develop a hybrid model for forecasting electrical energy consumption of households based on a Particle Swarm Optimization (PSO) algorithm associated with the Grey and Adaptive Neuro-Fuzzy Inference System (ANFIS). This paper proposes a new Grey-ANFIS-PSO model that is based on historical data from smart meters in order to estimate and improve the accuracy of forecasting electrical energy consumption. This accuracy will be characterized by coefficients such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). The PSO will allow to optimally design the Neuro-fuzzy forecasting. This method is implemented on Cameroon consumption data over the 24-years period in order to forecast energy consumption for the next years. Using this model, we were able to estimate that electricity consumption will be 1867 GWH in 2028 with 0.20158 RMSE and 0.62917% MAPE. The simulation results obtained show that implementation of this new optimized Neuro-fuzzy model on consumption data for a long period presents better results on prediction of electrical energy consumption compared to single artificial intelligence models of literature such as Support Vector Machine (SVM) and Artificial Neural Network (ANN).</p><p><strong>Keywords:</strong> Forecast model, PSO, ANFIS model, Grey model, electricity consumption</p><p class="Default"><strong>JEL Classifications: </strong>C22, C25, C32, C41, C45<strong></strong></p><p class="Default">DOI: <a href="https://doi.org/10.32479/ijeep.11761">https://doi.org/10.32479/ijeep.11761</a></p>Felix Ghislain Yem SouheCamille Franklin MbeyAlexandre Teplaira BoumPierre EleEconJournalsarticleEnvironmental sciencesGE1-350Energy industries. Energy policy. Fuel tradeHD9502-9502.5ENInternational Journal of Energy Economics and Policy, Vol 11, Iss 6, Pp 221-233 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Environmental sciences GE1-350 Energy industries. Energy policy. Fuel trade HD9502-9502.5
spellingShingle	Environmental sciences GE1-350 Energy industries. Energy policy. Fuel trade HD9502-9502.5 Felix Ghislain Yem Souhe Camille Franklin Mbey Alexandre Teplaira Boum Pierre Ele Forecasting of Electrical Energy Consumption of Households in a Smart Grid
description	<p>This paper aims to develop a hybrid model for forecasting electrical energy consumption of households based on a Particle Swarm Optimization (PSO) algorithm associated with the Grey and Adaptive Neuro-Fuzzy Inference System (ANFIS). This paper proposes a new Grey-ANFIS-PSO model that is based on historical data from smart meters in order to estimate and improve the accuracy of forecasting electrical energy consumption. This accuracy will be characterized by coefficients such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). The PSO will allow to optimally design the Neuro-fuzzy forecasting. This method is implemented on Cameroon consumption data over the 24-years period in order to forecast energy consumption for the next years. Using this model, we were able to estimate that electricity consumption will be 1867 GWH in 2028 with 0.20158 RMSE and 0.62917% MAPE. The simulation results obtained show that implementation of this new optimized Neuro-fuzzy model on consumption data for a long period presents better results on prediction of electrical energy consumption compared to single artificial intelligence models of literature such as Support Vector Machine (SVM) and Artificial Neural Network (ANN).</p><p><strong>Keywords:</strong> Forecast model, PSO, ANFIS model, Grey model, electricity consumption</p><p class="Default"><strong>JEL Classifications: </strong>C22, C25, C32, C41, C45<strong></strong></p><p class="Default">DOI: <a href="https://doi.org/10.32479/ijeep.11761">https://doi.org/10.32479/ijeep.11761</a></p>
format	article
author	Felix Ghislain Yem Souhe Camille Franklin Mbey Alexandre Teplaira Boum Pierre Ele
author_facet	Felix Ghislain Yem Souhe Camille Franklin Mbey Alexandre Teplaira Boum Pierre Ele
author_sort	Felix Ghislain Yem Souhe
title	Forecasting of Electrical Energy Consumption of Households in a Smart Grid
title_short	Forecasting of Electrical Energy Consumption of Households in a Smart Grid
title_full	Forecasting of Electrical Energy Consumption of Households in a Smart Grid
title_fullStr	Forecasting of Electrical Energy Consumption of Households in a Smart Grid
title_full_unstemmed	Forecasting of Electrical Energy Consumption of Households in a Smart Grid
title_sort	forecasting of electrical energy consumption of households in a smart grid
publisher	EconJournals
publishDate	2021
url	https://doaj.org/article/029ba97106a8451ebcd149f02bb4d37d
work_keys_str_mv	AT felixghislainyemsouhe forecastingofelectricalenergyconsumptionofhouseholdsinasmartgrid AT camillefranklinmbey forecastingofelectricalenergyconsumptionofhouseholdsinasmartgrid AT alexandreteplairaboum forecastingofelectricalenergyconsumptionofhouseholdsinasmartgrid AT pierreele forecastingofelectricalenergyconsumptionofhouseholdsinasmartgrid
_version_	1718431089307942912

Forecasting of Electrical Energy Consumption of Households in a Smart Grid

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