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AuthorOzcan, Alper
AuthorCatal, Cagatay
AuthorKasif, Ahmet
Available date2022-11-30T11:23:21Z
Publication Date2021
Publication NameSensors
ResourceScopus
Resource2-s2.0-85118282148
URIhttp://dx.doi.org/10.3390/s21217115
URIhttp://hdl.handle.net/10576/36803
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. 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Languageen
PublisherMDPI
SubjectDual-stage attention-based recurrent neural network; Energy consumption prediction; Time series forecasting
TitleEnergy load forecasting using a dual-stage attention-based recurrent neural network
TypeArticle
Issue Number21
Volume Number21
dc.accessType Open Access


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