Work

Short‐term energy forecasting using deep neural networks: Prospects and challenges

Year: 2024

Type: article

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 also highlights the role of DNNs in integrating energy prosumers, renewable energy systems, microgrids, big data, and smart grids to improve STEF.

Source: The Journal of Engineering

DOI: 10.1049/tje2.70022

Authors: Shewit Tsegaye, Padmanaban Sanjeevikumar, Lina Bertling Tjernberg, Kinde Anlay Fante

Institutions: Jimma University, University of South-Eastern Norway, KTH Royal Institute of Technology

Language: English

Topic: Energy Load and Power Forecasting

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Climate action

Open Access status: gold

Estimated APC paid: $950