AN OVERVIEW OF DEEP LEARNING TECHNIQUES FOR SHORT-TERM ELECTRICITY LOAD FORECASTING
Saheed ADEWUYI
saheed.adewuyi@uniosun.edu.ngOsun 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, SAEReferences
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Authors
Saheed ADEWUYIsaheed.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 LAWALObafemi Awolowo University, Department of Computer Science and Engineering, Ile-Ife, Osun State Nigeria
Authors
Adeniran OLUWARANTIObafemi Awolowo University, Department of Computer Science and Engineering, Ile-Ife, Osun State Nigeria
Authors
Moses UZUNUIGBETransmission Company of Nigeria, 132/33 kV, Ajebandele, Ile Ife, Osun State Nigeria
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