Abstract
Based on the daily power generation data, this paper constructs a combined model to forecast the monthly power consumption of the whole society. For the linear part of power consumption, mixed data sampling (MIDAS) model is used for prediction, while for the non-linear part, the autoregressive moving average (ARMA) model is used to forecast, and the results of the two parts are added together to get the final results. The research shows that the daily power generation has a high accuracy in forecasting the monthly power consumption of the whole society, and it can also ensure the time advance, which is of great significance for guaranteeing the power supply and studying the economic trend.
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Acknowledgments
This work was financially supported by the State Grid Science & Technology Project No. SGNY0000GXJS2100065 (Research on bidirectional data value mining technology and typical application of “power economy” in SGCC).
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Wu, S., Wang, X., Hou, H. (2022). Monthly Power Consumption Forecast of the Whole Society Based on Mixed Data Sampling Model. In: Hung, J.C., Yen, N.Y., Chang, JW. (eds) Frontier Computing. FC 2021. Lecture Notes in Electrical Engineering, vol 827. Springer, Singapore. https://doi.org/10.1007/978-981-16-8052-6_82
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DOI: https://doi.org/10.1007/978-981-16-8052-6_82
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