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Short-Term Load Forecasting Based on RS-ART

Abstract : This paper presents a short-term electric load forecasting method based on Autoregressive Tree Algorithm and Rough Set Theory. Firstly, Rough Set Theory was used to reduce the testing properties of Autoregressive Tree. It can optimize the Autoregressive Tree Algorithm. Then, Autoregressive Tree Model of Short-term electric load forecasting is set up. Using Rough Set Theory, the attributes will be reduced off; whose dependence is zero, through knowledge reduction method. It not only avoids the complexity and long training time of the model, but also considers various factors comprehensively. At the same time, this algorithm has improved the prediction rate greatly by using automatic Data Mining Algorithms. Practical examples show that it can improve the load forecast accuracy effectively, and reduce the prediction time.
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https://hal.inria.fr/hal-01563438
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Tao Yang, Feng Zhang, Qingji Li, Ping Yang. Short-Term Load Forecasting Based on RS-ART. 4th Conference on Computer and Computing Technologies in Agriculture (CCTA), Oct 2010, Nanchang, China. pp.413-418, ⟨10.1007/978-3-642-18354-6_49⟩. ⟨hal-01563438⟩

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