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
Bengio, Y. (2009). Learning deep architectures for AI. Foundation and Trends in Machine Learning, 2(1), 1–127.
DOI: https://doi.org/10.1561/2200000006
Google Scholar
Brownlee, J. (Ed.) (2018). Deep learning for time series forecasting: Predicting the future with MLPs, CNNs and LSTMs in Python. Machine learning mastery.
Google Scholar
Chengdong, L., Zixiang, D., Dongbin, Z., Jianqiang, Y., & Guiqing, Z. (2017). Building energy consumption prediction: An extreme deep learning approach. Energies, 10(10), 1525–1545.
DOI: https://doi.org/10.3390/en10101525
Google Scholar
Deng, L. (2013). A tutorial survey of architectures, algorithms, and applications for deep learning. APSIPA Transactions on Signal and Information Processing, 3(2). https://doi.org/10.1017/ATSIP
DOI: https://doi.org/10.1017/atsip.2013.9
Google Scholar
Deng, L., & Yu, D. (2013). Deep learning: methods and applications. Foundations and Trends in Signal Processing, 7(3-4), 197–387.
DOI: https://doi.org/10.1561/2000000039
Google Scholar
Feinberg, E. A., & Genethliou, D. (2005). Load forecasting. In J. H. Chow, F.F. Wu, & J. Momoh (Eds.), Applied Mathematics for Restructured Electric Power Systems. Power Electronics and Power Systems. Springer, Boston, MA.
DOI: https://doi.org/10.1007/0-387-23471-3_12
Google Scholar
Gamboa, J. (2017). Deep learning for time-series analysis. arXiv: 1701.01887.
Google Scholar
Ghullam, M. U., & Angelos, K. M. (2017). Short term power load forecasting using deep neural networks. ICNC, 10(1109), 594–598, 7876196.
Google Scholar
Hamedmoghadam, H., Joorabloo, N., & Jalili, M. (2018). Australia's long-term electricity demand forecasting using deep neural networks. arXiv: preprint arXiv:1801.02148.
Google Scholar
Hussein, A. (2018). Deep Learning Based Approaches for Imitation Learning (doctoral dissertation). Robert Gordon University Aberdeen, Scotland.
Google Scholar
Hussein, S., & Hussein, P. (2017). Load forecasting using deep neural networks. In 2017 IEEE
Google Scholar
Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT). IEEE. https://doi.org/10.1109/ISGT.2017.8085971
DOI: https://doi.org/10.1109/ISGT.2017.8085971
Google Scholar
Kuo, P., & Huang, C. (2018). A high-precision artificial neural networks model for short-term energy load management. Energy, 11(1), 213–226.
DOI: https://doi.org/10.3390/en11010213
Google Scholar
Luis, H., Carlos, B., Javier, M. A., Lorena, C., Belen, C., Antonio, S., Diane, J. C., David, C., & Jorge, G. (2012). A study of relationship between weather variables and electric power demand inside a smart grid/ smart world. MDPI Sensors, 22(9), 11571–11591.
DOI: https://doi.org/10.3390/s120911571
Google Scholar
Luis, H., Carlos, B., Javier, M. A., Lorena, C., Belen, C., Antonio, S., Diane, J. C., David, C., & Jorge, G. (2013). Short-term load forecasting for micro-grids based on artificial neural networks, MDPI Sensors, 6(3), 1385–1408.
DOI: https://doi.org/10.3390/en6031385
Google Scholar
Luis, H., Carlos, B., Javier, M. A., Lorena, C., Belen, C., Antonio, S., & Jaime, L. (2014). Artificial neural network for short-term load forecasting in distribution systems, MDPI, 7(3), 1576–1598.
DOI: https://doi.org/10.3390/en7031576
Google Scholar
Merkel, G. D., Povinelli, R. J., & Brown, R. H. (2017). Deep neural network regression for shortterm load forecasting of natural gas. Report: Marquette University.
Google Scholar
Nor, H. M., Rahaini, M. S., & Siti, H. H. A. (2018). ARIMA with Regression Model in Modelling electricity load demand, Journal of Telecommunication, Electronic and Computer Engineering, 8(12), 113–116.
Google Scholar
Rahul, K. A., Frankle, M., & Madan, M. T. (2018). Long term load forecasting with hourly predictions based on long-short-term-memory networks. In 2018 IEEE Texas Power and Energy Conference (TPEC). IEEE. https://doi.org/10.1109/TPEC.2018.8312088
DOI: https://doi.org/10.1109/TPEC.2018.8312088
Google Scholar
Sarabjit, S., & Rupinderjit, S. (2013). ARIMA Based Short Term Load Forecasting for Punjab Region. IJSR, 4(6), 1919–1822.
Google Scholar
Schmidhuber, J., & Sepp, H. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.
DOI: https://doi.org/10.1162/neco.1997.9.8.1735
Google Scholar
Seunghoung, R., Hongseok, K., & Jaekoo, N. (2017). Deep neural network based demand side short term load forecasting. Energies MDPI, 10(1), 3–23.
DOI: https://doi.org/10.3390/en10010003
Google Scholar
Swalin, A. (2019). How to handle missing data. Towards Data Science. Retrieved from https://towardsdatascience.com/how-tohandle-missing-data-8646b18db on 18/01/2019.
Google Scholar
Wan, H. (2014). Deep neural network based load forecast. Computer Modelling and New Technologies, 18(3), 258–262.
Google Scholar
Yi, Y., Jie, W., Yanhua, C., & Caihong L. (2013). A new strategy for short-term load forecasting. Hindawi.
Google Scholar
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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