Short‐term energy forecasting using deep neural networks: Prospects and challenges
Shewit Tsegaye,
Padmanaban Sanjeevikumar,
Lina Bertling Tjernberg
et al.
Abstract:This study presents an in‐depth overview of deep neural networks (DNN) and their hybrid applications for short‐term energy forecasting (STEF). It examines DNN‐based STEF from three perspectives: basics, challenges, and prospects. The study compares recent literature using metrics like mean absolute error (MAE), mean average percentage error (MAPE), and root mean square error (RMSE). Findings indicate that combining automated data‐driven models with enhanced DNNs effectively addresses forecasting challenges. It… Show more
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