Abstract
Green and low-carbon is an essential direction for environmentally sustainable development. Under the vision of carbon neutrality, the entire society has accelerated the development of electrification, resulting in a continuous increase in electricity demand. Therefore, it is of great practical significance to analyze and forecast electricity demand reasonably and accurately. However, existing single forecasting models based on deterministic forecasting are difficult to capture the seasonal characteristics and nonlinear fluctuations in power demand data and obtain high forecasting accuracy. To fill this disadvantage, a hybrid electricity demand forecasting system based on a seasonal selection method, completely non-recursive decomposition strategy, and the sunflower optimization algorithm is proposed. Actual electricity demand data obtained in different seasons in various Australian states were used for the simulation. Empirical results indicate that the seasonality of irregular data is successfully classified by the seasonal selection method; the high-frequency noise of the original data are removed by the completely non-recursive decomposition strategy, and the forecasting accuracy and reliability of the optimized forecasting module are effectively improved. Based on the sunflower optimization algorithm, the kernel parameters and forecasting system are optimized to provide technical support for the development of electricity demand response and environmental energy protection.
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This work was supported by Major Program of National Social Science Foundation of China (Grant No. 17ZDA093).
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Dong, Y., Wang, J. Research on hybrid electricity demand forecasting system based on sunflower optimization and completely non-recursive decomposition strategy. Environ Ecol Stat 30, 529–554 (2023). https://doi.org/10.1007/s10651-023-00569-4
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DOI: https://doi.org/10.1007/s10651-023-00569-4