Proceedings of the 2021 Sixth International Conference on Research in Intelligent and Computing

DOI: http://dx.doi.org/10.15439/2021R10

Citation: Proceedings of the 2021 Sixth International Conference on Research in Intelligent and Computing, Vijender Kumar Solanki, Nguyen Ho Quang (eds). ACSIS, Vol. 27, pages 1–6 (2021)

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Abstract. Accurate electricity load forecasting is essential for operating electrical systems. Most of the studies on electricity load forecasting are based on electricity load data or weather data, which is air temperature, but there are not consider the heat index. This paper proposes a short-term electricity load forecasting model using Long-Short Term Memory (LSTM) based on electricity load history and heat index data. In addition, the proposed model is applied to the data of IEVN NLDC (National Load Dispatching Center) in forecasting electricity load before 48 hours. This model is used to predict the electricity load of the Vietnamese nation and the power corporations of Vietnam. For a fair comparison, the LSTM network has fixed parameters, then compared the results when using temperature and the heat index. According to experimental results based on the Mean absolute percentage errors (MAPE) assessment, the proposed model has better accuracy than the model based on electricity load history and temperature.

References

1. K. chao Miao, T. ting Han, Y. qing Yao, H. Lu, P. Chen, B. Wang, and J. Zhang, “Application of lstm for short term fog forecasting based on meteorological elements,” Neurocomputing, vol. 408, pp. 285–291, 2020.
2. I. Lezhenin, N. Bogach, and E. Pyshkin, “Urban sound classification using long short-term memory neural network,” in Proceedings of the 2019 Federated Conference on Computer Science and Information Systems, ser. Annals of Computer Science and Information Systems, M. Ganzha, L. Maciaszek, and M. Paprzycki, Eds., vol. 18. IEEE, 2019, pp. 57–60. [Online]. Available: http://dx.doi.org/10.15439/2019F185
3. Z. Cui, R. Ke, Z. Pu, and Y. Wang, “Stacked bidirectional and unidirectional lstm recurrent neural network for forecasting network-wide traffic state with missing values,” Transportation Research Part C: Emerging Technologies, vol. 118, p. 102674, 2020.
4. L. Mei, R. Hu, H. Cao, Y. Liu, Z. Han, F. Li, and J. Li, “Realtime mobile bandwidth prediction using lstm neural network and bayesian fusion,” Computer Networks, vol. 182, p. 107515, 2020.
5. Y. Fan, F. Fang, and X. Wang, “Probability forecasting for short-term electricity load based on lstm,” in 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC), 2019, pp. 516–522.
6. I. Agbehadji, R. Millham, S. Fong, and H. Yang, “Kestrel-based search algorithm (ksa) and long short term memory (lstm) network for feature selection in classification of high-dimensional bioinformatics datasets,” in FedCSIS, 09 2018, pp. 15–20.
7. Y. Wang, D. Gan, M. Sun, N. Zhang, Z. Lu, and C. Kang, “Probabilistic individual load forecasting using pinball loss guided lstm,” Applied Energy, vol. 235, pp. 10 – 20, 2019.
8. R. G. Steadman, “The Assessment of Sultriness. Part I: A Temperature-Humidity Index Based on Human Physiology and Clothing Science,” Journal of Applied Meteorology, vol. 18, no. 7, pp. 861–873, 07 1979.
9. ——, “The Assessment of Sultriness. Part II: Effects of Wind, Extra Radiation and Barometric Pressure on Apparent Temperature,” Journal of Applied Meteorology, vol. 18, no. 7, pp. 874–885, 07 1979.
10. L. P. Rothfusz and N. S. R. Headquarters, “The heat index equation (or, more than you ever wanted to know about heat index),” Fort Worth, Texas: National Oceanic and Atmospheric Administration, National Weather Service, Office of Meteorology, vol. 9023, 1990.
11. G. B. Anderson, M. L. Bell, and R. D. Peng, “Methods to calculate the heat index as an exposure metric in environmental health research,” Environmental Health Perspectives, vol. 121, no. 10, pp. 1111–1119, 2013.
12. S. Opitz-Stapleton, L. Sabbag, K. Hawley, P. Tran, L. Hoang, and P. H. Nguyen, “Heat index trends and climate change implications for occupational heat exposure in da nang, vietnam,” Climate Services, vol. 2-3, pp. 41 – 51, 2016.
13. C. Cui, T. Wu, M. Hu, J. D. Weir, and X. Li, “Short-term building energy model recommendation system: A meta-learning approach,” Solar Energy, vol. 172, pp. 251–263, 2016.
14. Y. He, R. Liu, H. Li, S. Wang, and X. Lu, “Short-term power load probability density forecasting method using kernel-based support vector quantile regression and copula theory,” Applied Energy, vol. 185, pp. 254–266, 2017.
15. N. Zeng, H. Zhang, W. Liu, J. Liang, and F. E. Alsaadi, “A switching delayed pso optimized extreme learning machine for short-term load forecasting,” Neurocomputing, 2017.
16. L. Song, G. Lalit, and W. Peng, “An ensemble approach for short-term load forecasting by extreme learning machine,” Applied Energy, vol. 170, pp. 22–29, 2016.
17. S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Comput., vol. 9, no. 8, p. 1735–1780, Nov. 1997.
18. F. A. Gers, J. A. Schmidhuber, and F. A. Cummins, “Learning to forget: Continual prediction with lstm,” Neural Comput., vol. 12, no. 10, p. 2451–2471, Oct. 2000.