30 March 2024 | Aristeidis Mystakidis, Paraskevas Koukaras, Nikolaos Tsalikidis, Dimosthenis Ioannidis and Christos Tjortjis
The paper "Energy Forecasting: A Comprehensive Review of Techniques and Technologies" by Aristeidis Mystakidis et al. provides an extensive review of energy forecasting (EF) techniques and technologies, emphasizing their significance for the energy industry. The authors highlight the importance of accurate EF in managing energy resources, optimizing demand response, and ensuring grid stability. The review covers various time-series forecasting techniques, including sequence-to-sequence, recursive, and direct forecasting, and discusses evaluation criteria such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R²). The paper also explores the use of statistical, Machine Learning (ML), and Deep Learning (DL) methods, as well as ensemble approaches to improve prediction accuracy. Additionally, it delves into the application of EF in Energy Load Forecasting (ELF) and Energy Generation Forecasting (EGF), discussing the impact of weather conditions and renewable energy sources on forecasting accuracy. The review concludes with a standard methodology for EF and identifies future research directions, emphasizing the need for further development in this field.The paper "Energy Forecasting: A Comprehensive Review of Techniques and Technologies" by Aristeidis Mystakidis et al. provides an extensive review of energy forecasting (EF) techniques and technologies, emphasizing their significance for the energy industry. The authors highlight the importance of accurate EF in managing energy resources, optimizing demand response, and ensuring grid stability. The review covers various time-series forecasting techniques, including sequence-to-sequence, recursive, and direct forecasting, and discusses evaluation criteria such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R²). The paper also explores the use of statistical, Machine Learning (ML), and Deep Learning (DL) methods, as well as ensemble approaches to improve prediction accuracy. Additionally, it delves into the application of EF in Energy Load Forecasting (ELF) and Energy Generation Forecasting (EGF), discussing the impact of weather conditions and renewable energy sources on forecasting accuracy. The review concludes with a standard methodology for EF and identifies future research directions, emphasizing the need for further development in this field.