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- W2983022217 abstract "In order to improve the accuracy of short-term load forecasting, a power load forecasting method based on the data driven concept is proposed in this paper. Before establishing the forecasting model, some pre-processing procedure are applied to the given data. Firstly, k-means clustering method is adopted for the collected massive data. Then, the gray correlation analysis method is used to analyze the relationship between the load data and the load influencing factors which can determine the main influencing factors of the load. The coupling rules are established by the CART decision tree algorithm between the main influencing factors and the category. At last, using the least squares support vector machine method to establish different load forecasting models for different types of load data. Taking the historical data of a district in Guangdong Province as an example. Compared with traditional method, the method proposed in this paper is more accurate." @default.
- W2983022217 created "2019-11-22" @default.
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- W2983022217 date "2019-09-01" @default.
- W2983022217 modified "2023-09-26" @default.
- W2983022217 title "A Daily Load Forecasting Method Based on Data Driven Concept" @default.
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- W2983022217 doi "https://doi.org/10.1109/isgt-la.2019.8895465" @default.
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