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

Applied Computer Science

Segun AINA¹  Adeniran OLUWARANTI²  Aderonke LAWAL²  Saheed ADEWUYI³  Moses UZUNUIGBE⁴ 

[1] Obafemi Awolowo University, Department of Computer Science and Engineering, Ile-Ife, Osun State, Nigeria, [e-mail];Obafemi Awolowo University, Department of Computer Science and Engineering, Ile-Ife, Osun State, Nigeria;Osun State University, Department of Information and Communication Technology, Osogbo, Osun State, Nigeria, [e-mail];Transmission Company of Nigeria, 132/33 kV, Ajebandele, Ile Ife, Osun State, Nigeria, [e-mail];

关键词: short-term load forecasting;    deep learning architectures;    rnn;    lstm;    cnn;    sae;   

DOI  :  10.23743/acs-2019-31

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown