Abstract
Directed at the problem of more and increasing data types and volume in power grid, a short-term power load forecasting algorithm based on big data and Extreme Gradient Boosting (XGBoost) is proposed, based on the analysis of power grid load big data low. The algorithm includes the following steps. First, the outlier data and missing data are preprocessed. Then, the K-means algorithm is used to cluster the load big data of the power grid. Finally, The XGBoost algorithm was used to train the load forecasting model, based on the impact of historical load, calendar effect and meteorological factors on the load. Simulation results show that compared with support vector machine, random forest and decision tree, the proposed algorithm has a higher prediction accuracy and smoother prediction error, with smaller mean absolute percentage error, mean absolute error and relative error.
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Acknowledgements
This research was supported by the National Natural Science Foundation of China (Grant No. 91538201); Taishan scholar project special fund project (Grant No. Ts201511020).
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Limin Zhang, Qiang Guo design experiments and collect data. Liqiang Ren and Haipeng Wang conducted a case study and analyzed the results. Liqiang Ren wrote this paper. All authors read and approved the final manuscript.
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Ren, L., Zhang, L., Wang, H., Guo, Q. (2019). An Extreme Gradient Boosting Algorithm for Short-Term Load Forecasting Using Power Grid Big Data. In: Jia, Y., Du, J., Zhang, W. (eds) Proceedings of 2018 Chinese Intelligent Systems Conference. Lecture Notes in Electrical Engineering, vol 528. Springer, Singapore. https://doi.org/10.1007/978-981-13-2288-4_46
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DOI: https://doi.org/10.1007/978-981-13-2288-4_46
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