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

Saheed 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)

Aderonke LAWAL


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

Adeniran OLUWARANTI


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

Moses UZUNUIGBE


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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Published
2019-12-30

Cited by

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

Authors

Saheed ADEWUYI 
saheed.adewuyi@uniosun.edu.ng
Osun State University, Department of Information and Communication Technology, Osogbo, Osun State Nigeria

Authors

Segun AINA 

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

Authors

Aderonke LAWAL 

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

Authors

Adeniran OLUWARANTI 

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

Authors

Moses UZUNUIGBE 

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

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