Forecasting Electricity Load With Hybrid Scalable Model Based on Stacked Non Linear Residual Approach

Power has totally different attributes than other material commodities as electrical energy stockpiling is a costly phenomenon. Since it should be generated when demanded, it is necessary to forecast its demand accurately and efficiently. As electrical load data is represented through time series pa...

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Autores principales:	Ayush Sinha, Raghav Tayal, Aamod Vyas, Pankaj Pandey, O. P. Vyas
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	vector auto regression convolutional neural network long short term memory electrical load forecasting time series General Works A
Acceso en línea:	https://doaj.org/article/479b5ff751af4fe0aa2dc5b6a0d53d12
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spelling	oai:doaj.org-article:479b5ff751af4fe0aa2dc5b6a0d53d122021-12-01T08:08:16ZForecasting Electricity Load With Hybrid Scalable Model Based on Stacked Non Linear Residual Approach2296-598X10.3389/fenrg.2021.720406https://doaj.org/article/479b5ff751af4fe0aa2dc5b6a0d53d122021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fenrg.2021.720406/fullhttps://doaj.org/toc/2296-598XPower has totally different attributes than other material commodities as electrical energy stockpiling is a costly phenomenon. Since it should be generated when demanded, it is necessary to forecast its demand accurately and efficiently. As electrical load data is represented through time series pattern having linear and non-linear characteristics, it needs a model that may handle this behavior well in advance. This paper presents a scalable and hybrid approach for forecasting the power load based on Vector Auto Regression (VAR) and hybrid deep learning techniques like Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN). CNN and LSTM models are well known for handling time series data. The VAR model separates the linear pattern in time series data, and CNN-LSTM is utilized to model non-linear patterns in data. CNN-LSTM works as CNN can extract complex features from electricity data, and LSTM can model temporal information in data. This approach can derive temporal and spatial features of electricity data. The experiment established that the proposed VAR-CNN-LSTM(VACL) hybrid approach forecasts better than more recent deep learning methods like Multilayer Perceptron (MLP), CNN, LSTM, MV-KWNN, MV-ANN, Hybrid CNN-LSTM and statistical techniques like VAR, and Auto Regressive Integrated Moving Average (ARIMAX). Performance metrics such as Mean Square Error, Root Mean Square Error, and Mean Absolute Error have been used to evaluate the performance of the discussed approaches. Finally, the efficacy of the proposed model is established through comparative studies with state-of-the-art models on Household Power Consumption Dataset (UCI machine learning repository) and Ontario Electricity Demand dataset (Canada).Ayush SinhaRaghav TayalAamod VyasPankaj PandeyO. P. VyasFrontiers Media S.A.articlevector auto regressionconvolutional neural networklong short term memoryelectrical load forecastingtime seriesGeneral WorksAENFrontiers in Energy Research, Vol 9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	vector auto regression convolutional neural network long short term memory electrical load forecasting time series General Works A
spellingShingle	vector auto regression convolutional neural network long short term memory electrical load forecasting time series General Works A Ayush Sinha Raghav Tayal Aamod Vyas Pankaj Pandey O. P. Vyas Forecasting Electricity Load With Hybrid Scalable Model Based on Stacked Non Linear Residual Approach
description	Power has totally different attributes than other material commodities as electrical energy stockpiling is a costly phenomenon. Since it should be generated when demanded, it is necessary to forecast its demand accurately and efficiently. As electrical load data is represented through time series pattern having linear and non-linear characteristics, it needs a model that may handle this behavior well in advance. This paper presents a scalable and hybrid approach for forecasting the power load based on Vector Auto Regression (VAR) and hybrid deep learning techniques like Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN). CNN and LSTM models are well known for handling time series data. The VAR model separates the linear pattern in time series data, and CNN-LSTM is utilized to model non-linear patterns in data. CNN-LSTM works as CNN can extract complex features from electricity data, and LSTM can model temporal information in data. This approach can derive temporal and spatial features of electricity data. The experiment established that the proposed VAR-CNN-LSTM(VACL) hybrid approach forecasts better than more recent deep learning methods like Multilayer Perceptron (MLP), CNN, LSTM, MV-KWNN, MV-ANN, Hybrid CNN-LSTM and statistical techniques like VAR, and Auto Regressive Integrated Moving Average (ARIMAX). Performance metrics such as Mean Square Error, Root Mean Square Error, and Mean Absolute Error have been used to evaluate the performance of the discussed approaches. Finally, the efficacy of the proposed model is established through comparative studies with state-of-the-art models on Household Power Consumption Dataset (UCI machine learning repository) and Ontario Electricity Demand dataset (Canada).
format	article
author	Ayush Sinha Raghav Tayal Aamod Vyas Pankaj Pandey O. P. Vyas
author_facet	Ayush Sinha Raghav Tayal Aamod Vyas Pankaj Pandey O. P. Vyas
author_sort	Ayush Sinha
title	Forecasting Electricity Load With Hybrid Scalable Model Based on Stacked Non Linear Residual Approach
title_short	Forecasting Electricity Load With Hybrid Scalable Model Based on Stacked Non Linear Residual Approach
title_full	Forecasting Electricity Load With Hybrid Scalable Model Based on Stacked Non Linear Residual Approach
title_fullStr	Forecasting Electricity Load With Hybrid Scalable Model Based on Stacked Non Linear Residual Approach
title_full_unstemmed	Forecasting Electricity Load With Hybrid Scalable Model Based on Stacked Non Linear Residual Approach
title_sort	forecasting electricity load with hybrid scalable model based on stacked non linear residual approach
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/479b5ff751af4fe0aa2dc5b6a0d53d12
work_keys_str_mv	AT ayushsinha forecastingelectricityloadwithhybridscalablemodelbasedonstackednonlinearresidualapproach AT raghavtayal forecastingelectricityloadwithhybridscalablemodelbasedonstackednonlinearresidualapproach AT aamodvyas forecastingelectricityloadwithhybridscalablemodelbasedonstackednonlinearresidualapproach AT pankajpandey forecastingelectricityloadwithhybridscalablemodelbasedonstackednonlinearresidualapproach AT opvyas forecastingelectricityloadwithhybridscalablemodelbasedonstackednonlinearresidualapproach
_version_	1718405408447528960

Forecasting Electricity Load With Hybrid Scalable Model Based on Stacked Non Linear Residual Approach

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