… The main contribution of this review is the ongoing exploration of STLF with DL models to
reveal the research direction of the load forecasting problem in terms of the future-oriented …

… This paper has reviewed the famous deep learning methods that applied to the SG load
forecasting. Most of these learning algorithms have successful approached the forecasting …

… load forecasting is crucial for the planning of power systems and operational decision making.
In this study, machine learning approaches … to forecast the electricity load requirements in …

… In this study, deep learning approaches are for the first time utilized in super-short-term …
PEV charging load forecasting. Unlike the previous shallow structure methods, the deep learning …

… This article discusses various algorithms and a new hybrid deep learning model which …
deep learning models such as the PLCNet in this article for online load forecasting tasks. …

… The biggest contribution of this paper is to propose a hybrid interval forecasting model
based on the deep learning approach with high forecasting accuracy, low computing costs and …

… side load forecast over short- and medium-term monthly horizons. Commonly used machine
learning approaches are … using feature selection and GA approaches. The performances of …

… predictions/forecasts of energy demands (loads) at … , load forecasting remains to be a
difficult problem. This paper presents a load forecasting methodology based on deep learning. …

… This section briefly presents short-term power system load forecasting models in the specific
… to forecast applications with noise in Zhang and Zhang [14]. The EWT decomposes the load …

… learning can be a powerful tool in short-term load forecasting. … after several hours of online
forecasting. Furthermore, we show that … learning from deterministic forecasting to probabilistic …