ISSN 2353-6977 (Online)
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.
- APA 6th style
Adewuyi, S., Aina, S., Uzunuigbe, M., Lawal, A., & Oluwaranti, A. (2019). An overview of Deep Learning techniques for Short-term Electricity Load Forecasting. Applied Computer Science, 15(4), 75-92. doi:10.23743/acs-2019-31
- Chicago style
Adewuyi, Saheed, Segun Aina, Moses Uzunuigbe, Aderonke Lawal, and Adeniran Oluwaranti. "An Overview of Deep Learning Techniques for Short-Term Electricity Load Forecasting." Applied Computer Science 15, no. 4 (2019): 75-92.
- IEEE style
S. Adewuyi, S. Aina, M. Uzunuigbe, A. Lawal, and A. Oluwaranti, "An overview of Deep Learning techniques for Short-term Electricity Load Forecasting," Applied Computer Science, vol. 15, no. 4, pp. 75-92, 2019, doi: 10.23743/acs-2019-31.
- Vancouver style
Adewuyi S, Aina S, Uzunuigbe M, Lawal A, Oluwaranti A. An overview of Deep Learning techniques for Short-term Electricity Load Forecasting. Applied Computer Science. 2019;15(4):75-92.
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