Energy Load Forecasting Using a Dual-Stage Attention-Based Recurrent Neural Network

Providing a stable, low-price, and safe supply of energy to end-users is a challenging task. The energy service providers are affected by several events such as weather, volatility, and special events. As such, the prediction of these events and having a time window for taking preventive measures ar...

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Autores principales:	Alper Ozcan, Cagatay Catal, Ahmet Kasif
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	dual-stage attention-based recurrent neural network time series forecasting energy consumption prediction Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/92832c8853084b328dba09ce20a87ec0
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spelling	oai:doaj.org-article:92832c8853084b328dba09ce20a87ec02021-11-11T19:07:20ZEnergy Load Forecasting Using a Dual-Stage Attention-Based Recurrent Neural Network10.3390/s212171151424-8220https://doaj.org/article/92832c8853084b328dba09ce20a87ec02021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7115https://doaj.org/toc/1424-8220Providing a stable, low-price, and safe supply of energy to end-users is a challenging task. The energy service providers are affected by several events such as weather, volatility, and special events. As such, the prediction of these events and having a time window for taking preventive measures are crucial for service providers. Electrical load forecasting can be modeled as a time series prediction problem. One solution is to capture spatial correlations, spatial-temporal relations, and time-dependency of such temporal networks in the time series. Previously, different machine learning methods have been used for time series prediction tasks; however, there is still a need for new research to improve the performance of short-term load forecasting models. In this article, we propose a novel deep learning model to predict electric load consumption using Dual-Stage Attention-Based Recurrent Neural Networks in which the attention mechanism is used in both encoder and decoder stages. The encoder attention layer identifies important features from the input vector, whereas the decoder attention layer is used to overcome the limitations of using a fixed context vector and provides a much longer memory capacity. The proposed model improves the performance for short-term load forecasting (STLF) in terms of the Mean Absolute Error (MAE) and Root Mean Squared Errors (RMSE) scores. To evaluate the predictive performance of the proposed model, the UCI household electric power consumption (HEPC) dataset has been used during the experiments. Experimental results demonstrate that the proposed approach outperforms the previously adopted techniques.Alper OzcanCagatay CatalAhmet KasifMDPI AGarticledual-stage attention-based recurrent neural networktime series forecastingenergy consumption predictionChemical technologyTP1-1185ENSensors, Vol 21, Iss 7115, p 7115 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	dual-stage attention-based recurrent neural network time series forecasting energy consumption prediction Chemical technology TP1-1185
spellingShingle	dual-stage attention-based recurrent neural network time series forecasting energy consumption prediction Chemical technology TP1-1185 Alper Ozcan Cagatay Catal Ahmet Kasif Energy Load Forecasting Using a Dual-Stage Attention-Based Recurrent Neural Network
description	Providing a stable, low-price, and safe supply of energy to end-users is a challenging task. The energy service providers are affected by several events such as weather, volatility, and special events. As such, the prediction of these events and having a time window for taking preventive measures are crucial for service providers. Electrical load forecasting can be modeled as a time series prediction problem. One solution is to capture spatial correlations, spatial-temporal relations, and time-dependency of such temporal networks in the time series. Previously, different machine learning methods have been used for time series prediction tasks; however, there is still a need for new research to improve the performance of short-term load forecasting models. In this article, we propose a novel deep learning model to predict electric load consumption using Dual-Stage Attention-Based Recurrent Neural Networks in which the attention mechanism is used in both encoder and decoder stages. The encoder attention layer identifies important features from the input vector, whereas the decoder attention layer is used to overcome the limitations of using a fixed context vector and provides a much longer memory capacity. The proposed model improves the performance for short-term load forecasting (STLF) in terms of the Mean Absolute Error (MAE) and Root Mean Squared Errors (RMSE) scores. To evaluate the predictive performance of the proposed model, the UCI household electric power consumption (HEPC) dataset has been used during the experiments. Experimental results demonstrate that the proposed approach outperforms the previously adopted techniques.
format	article
author	Alper Ozcan Cagatay Catal Ahmet Kasif
author_facet	Alper Ozcan Cagatay Catal Ahmet Kasif
author_sort	Alper Ozcan
title	Energy Load Forecasting Using a Dual-Stage Attention-Based Recurrent Neural Network
title_short	Energy Load Forecasting Using a Dual-Stage Attention-Based Recurrent Neural Network
title_full	Energy Load Forecasting Using a Dual-Stage Attention-Based Recurrent Neural Network
title_fullStr	Energy Load Forecasting Using a Dual-Stage Attention-Based Recurrent Neural Network
title_full_unstemmed	Energy Load Forecasting Using a Dual-Stage Attention-Based Recurrent Neural Network
title_sort	energy load forecasting using a dual-stage attention-based recurrent neural network
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/92832c8853084b328dba09ce20a87ec0
work_keys_str_mv	AT alperozcan energyloadforecastingusingadualstageattentionbasedrecurrentneuralnetwork AT cagataycatal energyloadforecastingusingadualstageattentionbasedrecurrentneuralnetwork AT ahmetkasif energyloadforecastingusingadualstageattentionbasedrecurrentneuralnetwork
_version_	1718431598614937600

Energy Load Forecasting Using a Dual-Stage Attention-Based Recurrent Neural Network

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