A DEEP LEARNING MODEL FOR ELECTRICITY DEMAND FORECASTING BASED ON A TROPICAL DATA

• Published in: Applied Computer Science (Lublin) Vol. 16; no. 1; pp. 5 - 17
• Main Authors: Saheed A. ADEWUYI, Segun AINA, Adeniran I. OLUWARANTI
• Format: Journal Article
• Language: English
• Published: Polish Association for Knowledge Promotion 01.03.2020
• Subjects: cnn; deep learning techniques; electricity demand forecasting; lstm; mlp; stlf
• ISSN: 1895-3735; 2353-6977
• DOI: 10.23743/acs-2020-01

Electricity demand forecasting is a term used for prediction of users’ con-sumption 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 computer 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 forecasting 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.