A DEEP LEARNING MODEL FOR ELECTRICITY DEMAND FORECASTING BASED ON A TROPICAL DATA
Saheed A. 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)
Adeniran I. OLUWARANTI
Obafemi Awolowo University, Department of Computer Science and Engineering, Ile-Ife, Osun State (Nigeria)
Abstract
Electricity demand forecasting is a term used for prediction of users’ consumption on the grid ahead of actual demand. It is very important to all power stakeholders across levels. The power players employ electricity demand forecasting for sundry purposes. Moreover, the government’s policy on its market deregulation has greatly amplified its essence. Despite numerous studies on the subject using certain classical approaches, there exists an opportunity for exploration of more sophisticated methods such as the deep learning (DL) techniques. Successful researches about DL applications to com¬puter vision, speech recognition, and acoustic computing problems are motivation. However, such researches are not sufficiently exploited for electricity demand forecasting using DL methods. In this paper, we considered specific DL techniques (LSTM, CNN, and MLP) to short-term load fore¬casting problems, using tropical institutional data obtained from a Transmission Company. We also test how accurate are predictions across the techniques. Our results relatively revealed models appropriateness for the problem.
Keywords:
Electricity Demand Forecasting, STLF, Deep Learning Techniques, LSTM, CNN, MLPReferences
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. https://doi.org/10.23743/acs-2019-31
Google Scholar
Agrawal, R. K., Muchahary, F., & Tripathi, M. M. (2018). Long term load forecasting with hourly predictions based on long-short-term-memory networks. In 2018 IEEE Texas Power and Energy Conference (TPEC) (pp. 1–6). College Station, TX.
DOI: https://doi.org/10.1109/TPEC.2018.8312088
Google Scholar
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
Bouktif, S., Ali, F., Ali, O., & Mohamed, A. S. (2018). Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches. Energies, 11, 1636–1656.
DOI: https://doi.org/10.3390/en11071636
Google Scholar
Brownlee, J. (2018). Deep learning for time series forecasting: Predicting the future with MLPs, CNNs and LSTMs in Python. V1.2 ed. M. L. 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 (pp. 269–285). 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[cs. LG].
Google Scholar
Ghullam, M. U., & Angelos, K. M. (2017). Short-term power load forecasting using deep neural networks. ICNC, 10(1109), 594–598.
Google Scholar
Hamedmoghadam, H., Joorabloo, N., & Jalili, M. (2018). Australia's long-term electricity demand forecasting using deep neural networks, arXiv:1801.02148 [cs.NE].
Google Scholar
Hernandez, L., Baladron, C., Aquiar, J. M., Calavia, L., Carro, B., Sánchez-Esguevillas, A., Cook, D. J., Chinarro, D., & Gomez, J. (2012). A study of relationship between weather variables and electric power demand inside a smart grid/ smart world. MDPI Sensors, 22(9), 11571–11591. https://doi.org/10.3390/s120911571
DOI: https://doi.org/10.3390/s120911571
Google Scholar
Hernandez, L., Baladron, C., Aquiar, J. M., Calavia, L., Carro, B., Sánchez-Esguevillas, A., Cook, D. J., Chinarro, D., & Gomez, J. (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
Hernandez, L., Baladron, C., Aquiar, J. M., Calavia, L., Carro, B., Sánchez-Esguevillas, A., Perez, F., Fernández, A., & Lloret, J. (2014). Artificial neural network for short-term load forecasting in distribution systems. MDPI Energies, 7(3), 1576–1598.
DOI: https://doi.org/10.3390/en7031576
Google Scholar
Hosein, S., & Hosein, P. (2017). Load forecasting using deep neural networks. In Proceedings of the Power and Energy Society Conference on Innovative Smart Grid Technologies (pp. 1–5). IEEE.
DOI: https://doi.org/10.1109/ISGT.2017.8085971
Google Scholar
Hussein, A. (2018). Deep Learning Based Approaches for Imitation Learning (Doctoral dissertation). Robert Gordon University, Aberden, Scotland.
Google Scholar
International Energy Agency (IEA) Publications and data (n.n.). Retrieved August 12, 2018 from https://www.iea.org
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
Momani, M. A. (2013). Factors Affecting Electricity Demand in Jordan. Energy and Power Engineering, 5, 50–58.
DOI: https://doi.org/10.4236/epe.2013.51007
Google Scholar
Ronald, J. W., & Jing, P. (1990). An Efficient Gradient-Based Algorithm for On-Line Training of Recurrent Network Trajectories. Neural Computation, 2, 490–501.
DOI: https://doi.org/10.1162/neco.1990.2.4.490
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
Seunghyoung, 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
Stuart, R., & Norvig, P. (2013). Artificial Intelligence A modern Approach. Second ed. Prentice Hall.
Google Scholar
Sutskever, I. (2013). Training Rucurrent Neural Net-works (Doctoral dissertation). Computer Science, University of Toronto, Toronto.
Google Scholar
Swalin, A. (2018). How to Handle Missing Data. Towards Data Science. https://towardsdatascience.com/how-to-handle-missing-data-8646b18db0d4 on 18/01/19.
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, 2013, 208964. https://doi.org/10.1155/2013/208964
DOI: https://doi.org/10.1155/2013/208964
Google Scholar
Authors
Saheed A. ADEWUYIsaheed.adewuyi@uniosun.edu.ng
* Osun State University, Department of Information and Communication Technology, Osogbo, Osun State Nigeria
Authors
Segun AINAObafemi Awolowo University, Department of Computer Science and Engineering, Ile-Ife, Osun State Nigeria
Authors
Adeniran I. OLUWARANTIObafemi Awolowo University, Department of Computer Science and Engineering, Ile-Ife, Osun State Nigeria
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