Energy load forecasting using a dual-stage attention-based recurrent neural network

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dc.authorid0000-0003-2707-6075en_US
dc.authorscopusid57205615393en_US
dc.contributor.authorOzcan A.
dc.contributor.authorCatal C.
dc.contributor.authorKaşif, Ahmet
dc.date.accessioned2022-02-16T07:49:41Z
dc.date.available2022-02-16T07:49:41Z
dc.date.issued2021en_US
dc.departmentBTÜ, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractProviding 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.en_US
dc.identifier.doi10.3390/s21217115en_US
dc.identifier.issn14248220
dc.identifier.issue21en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/20.500.12885/1817
dc.identifier.volume21en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorKaşif, Ahmet
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.relation.ispartofSensorsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectDual-stage attention-based recurrent neural networken_US
dc.subjectEnergy consumption predictionen_US
dc.subjectTime series forecastingen_US
dc.titleEnergy load forecasting using a dual-stage attention-based recurrent neural networken_US
dc.typeArticleen_US

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