Abstract
Load forecasting is a basic work of power system dispatching. With the rapid development of smart grid technology, the accuracy of load forecasting is put forward by increasing demand. Fusion load, weather and other multi-sourced data, a refined load forecasting method of support vector machine (SVM) based on data mining is proposed. Firstly, the history load data is clustered and the operation days are divided into six categories. Then, the load data and weather data such as humidity and temperature are fused together, a refined load forecasting model based on data mining is proposed. And the parameters of the model are optimized globally. Forecasting the load of a prefecture-level city in Zhejiang Province in 2013, the load prediction error of sampling point and daily average load forecasting rate are used as indexes to evaluate the prediction accuracy, the prediction results show that the prediction accuracy of the support vector machine (SVM) refined load forecasting method based on historical data and real-time influencing factors proposed in this paper is significantly higher than that of the traditional load forecasting method.
Project Supported by ABB China Research Institute (No. ABB20171128REU-CTR).
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Xi, Y., Wu, J., Shi, C., Zhu, X., An, R., Cai, R. (2018). Research on Refined Load Forecasting Method Based on Data Mining. In: Li, K., Zhang, J., Chen, M., Yang, Z., Niu, Q. (eds) Advances in Green Energy Systems and Smart Grid. ICSEE IMIOT 2018 2018. Communications in Computer and Information Science, vol 925. Springer, Singapore. https://doi.org/10.1007/978-981-13-2381-2_1
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DOI: https://doi.org/10.1007/978-981-13-2381-2_1
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