A DEEP LEARNING MODEL FOR ELECTRICITY DEMAND FORECASTING BASED ON A TROPICAL DATA
Saheed A. ADEWUYI
saheed.adewuyi@uniosun.edu.ng* Osun State University, Department of Information and Communication Technology, Osogbo, Osun State (Nigeria)
Segun AINA
Obafemi Awolowo University, Department of Computer Science and Engineering, Ile-Ife, Osun State (Nigeria)
Adeniran I. OLUWARANTI
Obafemi Awolowo University, Department of Computer Science and Engineering, Ile-Ife, Osun State (Nigeria)
Abstract
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.
Keywords:
Electricity Demand Forecasting, STLF, Deep Learning Techniques, LSTM, CNN, MLPReferences
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Authors
Saheed A. ADEWUYIsaheed.adewuyi@uniosun.edu.ng
* Osun State University, Department of Information and Communication Technology, Osogbo, Osun State Nigeria
Authors
Segun AINAObafemi Awolowo University, Department of Computer Science and Engineering, Ile-Ife, Osun State Nigeria
Authors
Adeniran I. OLUWARANTIObafemi Awolowo University, Department of Computer Science and Engineering, Ile-Ife, Osun State Nigeria
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