AN OVERVIEW OF DEEP LEARNING TECHNIQUES FOR SHORT-TERM ELECTRICITY LOAD FORECASTING

Saheed ADEWUYI; Segun AINA; Aderonke LAWAL; Adeniran OLUWARANTI; Moses UZUNUIGBE

doi:10.23743/acs-2019-31

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Published: Dec 30, 2019

DOI: https://doi.org/10.23743/acs-2019-31

Issue Vol. 15 No. 4 (2019)

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AN OVERVIEW OF DEEP LEARNING TECHNIQUES FOR SHORT-TERM ELECTRICITY LOAD FORECASTING
Saheed ADEWUYI, Segun AINA, Aderonke LAWAL, Adeniran OLUWARANTI, Moses UZUNUIGBE

75-92
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DOI

https://doi.org/10.23743/acs-2019-31

Authors

Saheed ADEWUYI

saheed.adewuyi@uniosun.edu.ng

Osun State University, Department of Information and Communication Technology, Osogbo, Osun State, Nigeria

Segun AINA

s.aina@oauife.edu.ng

* Obafemi Awolowo University, Department of Computer Science and Engineering, Ile-Ife, Osun State, Nigeria

Aderonke LAWAL

s.aina@oauife.edu.ng

Obafemi Awolowo University, Department of Computer Science and Engineering, Ile-Ife, Osun State, Nigeria

Adeniran OLUWARANTI

s.aina@oauife.edu.ng

Obafemi Awolowo University, Department of Computer Science and Engineering, Ile-Ife, Osun State, Nigeria

Moses UZUNUIGBE

moses_uzunuigbe@yahoo.com

Transmission Company of Nigeria, 132/33 kV, Ajebandele, Ile Ife, Osun State, Nigeria

Abstract

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.

Keywords:

Short-term Load Forecasting, Deep Learning Architectures, RNN, LSTM, CNN, SAE

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ADEWUYI, S., AINA, S., LAWAL, A., OLUWARANTI, A., & UZUNUIGBE, M. (2019). AN OVERVIEW OF DEEP LEARNING TECHNIQUES FOR SHORT-TERM ELECTRICITY LOAD FORECASTING. Applied Computer Science, 15(4), 75–92. https://doi.org/10.23743/acs-2019-31

AN OVERVIEW OF DEEP LEARNING TECHNIQUES FOR SHORT-TERM ELECTRICITY LOAD FORECASTING

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