The hourly load forecasting based on linear Gaussian state space model

Yanxia Lu, Huifeng Shi
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引用次数: 5

Abstract

In this paper, the linear gaussian state space model is used to forecast the hourly electricity load. Since the weather variables have significant impacts on electricity demand, thus in our forecasting model, the weather variables are considered as explanatory variables and added to the state space model. The variance parameters of the linear gaussian state space are estimated by the Markov chain Monte Carlo method. Given the estimated parameters, the linear gaussian state space is used to forecast the electricity load on two hours SAM and 14PM respectively. The result shows that this model has higher forecasting precision than the one to four days ahead forecasting, and the state space model estimated by Gibbs sampling algorithm has better performance than the model based on the MH algorithm.
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基于线性高斯状态空间模型的小时负荷预测
本文采用线性高斯状态空间模型对每小时电力负荷进行预测。由于天气变量对电力需求有显著影响,因此在我们的预测模型中,我们将天气变量作为解释变量加入到状态空间模型中。利用马尔可夫链蒙特卡罗方法估计了线性高斯状态空间的方差参数。给定估计的参数,利用线性高斯状态空间分别预测了两个小时SAM和14PM的电力负荷。结果表明,该模型比提前1 ~ 4天预测具有更高的预测精度,Gibbs抽样算法估计的状态空间模型比基于MH算法的模型具有更好的性能。
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