This paper presents an overview of some Deep Learning (DL) techniques applicable to forecasting electricity consumptions, especially in the short-term horizon. The paper introduced key parts of four DL architectures including the RNN, LSTM, CNN and SAE, which are recently adopted in implementing Short-term (electricity) Load Forecasting problems. It further presented a model approach for solving such problems. The eventual implication of the study is to present an insightful direction about concepts of the DL methods for forecasting electricity loads in the short-term period, especially to a potential researcher in quest of solving similar problems.

Electricity demand forecasting is a term used for prediction of users’ consumption on the grid ahead of actual demand. It is very important to all power stakeholders across levels. The power players employ electricity demand forecasting for sundry purposes. Moreover, the government’s policy on its market deregulation has greatly amplified its essence. Despite numerous studies on the subject using certain classical approaches, there exists an opportunity for exploration of more sophisticated methods such as the deep learning (DL) techniques. Successful researches about DL applications to com¬puter vision, speech recognition, and acoustic computing problems are motivation. However, such researches are not sufficiently exploited for electricity demand forecasting using DL methods. In this paper, we considered specific DL techniques (LSTM, CNN, and MLP) to short-term load fore¬casting problems, using tropical institutional data obtained from a Transmission Company. We also test how accurate are predictions across the techniques. Our results relatively revealed models appropriateness for the problem.