Abstract
A short-term load forecasting method based on meteorological similar days and error correction is proposed in this paper. First, SPSS software is used to carry on the regression analysis of meteorological factors, select most significant meteorological factors in each season, and determine the weight of each factor as the basis for selecting the weather similar days. Then the historical forecast error data sample set is set up. For a certain forecast date, the error data samples from the similar days are extracted to establish a set, and the probability density distribution model is established. Finally, the error fluctuation of the forecast point is analyzed to get the compensated value of forecast error. The sampling error closest to the error compensation value is selected as the fitted error values and added to the predicted value to improve the forecast accuracy.
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Lu, Z., Zheng, Z., Wang, H., Gao, Y., Lei, Y., Zhao, M. (2020). Study on Short-Term Load Forecasting Considering Meteorological Similar Days. In: Xhafa, F., Patnaik, S., Tavana, M. (eds) Advances in Intelligent Systems and Interactive Applications. IISA 2019. Advances in Intelligent Systems and Computing, vol 1084. Springer, Cham. https://doi.org/10.1007/978-3-030-34387-3_83
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DOI: https://doi.org/10.1007/978-3-030-34387-3_83
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Online ISBN: 978-3-030-34387-3
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