Probabilistic Interval Forecasting of Power Load Based on Structured Load model

doi:10.11930/j.issn.1004-9649.202007217

Abstract

Abstract: Probability interval forecasting has become one of the main methods for power load forecasting because of the uncertainties of power load. In order to solve the problem that the conventional probability interval forecasting methods cannot consider the impact of the uncertainties of different components on the power load, a structural power load probability interval forecasting method is proposed based on the time-series state space model. Firstly, based on an analysis of the components of power load, models are established respectively for different load components. And then, based on the historical load data, the variational bayesian inference is used to train the posterior probability distribution of model parameters. Finally, the probability distribution of the future power load is predicted based on the trained model, thus realizing the forecasting of the power load probability interval. The proposed method is verified using the real power load data and compared with other methods. The experimental results show that the proposed method has a higher forecasting interval coverage probability and a narrower forecasting interval average width.

Key words: power load forecasting, power load probability interval forecasting, structural power load model, variational bayesian inference

PANG Chuanjun, ZHANG Bo, YU Jianming, LIU Yan. Probabilistic Interval Forecasting of Power Load Based on Structured Load model[J]. Electric Power, 2021, 54(9): 89-95.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202007217

https://www.electricpower.com.cn/EN/Y2021/V54/I9/89

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