基于LSSVM的超短期负荷区间预测

Ultra-Short-Term Load Interval Prediction Based on Least Squares Support Vector Machine

智能电网的快速发展给电网运行带来了新的挑战,为适应智能电网快速响应的要求,实现对电力负荷未来运行趋势的快速估计,本文提出一种基于LSSVM模型的超短期电力负荷区间预测方法,所提方法在点预测的基础上,通过对样本数据的整体噪声方差进行估算来预测区间,计算量小且大大减少了预测耗时.在模型参数选取问题上,首先使用Gamma Test噪声估计的参数确定方法确定最优的训练样本量和嵌入维数,然后采用网格搜索的方法选择最优超参数,使LSSVM模型在训练样本上的拟合误差逼近估计出的最小噪声.为验证本文所提方法的有效性,使用某电网的调度负荷数据进行了仿真实验,其结果表明该方法不仅能够体现LSSVM简单快速的特点,还通过对模型参数的优化使预测区间的准确性得到了保证.

The rapid development of smart grids has brought new challenges to grid operation. In order to adapt to the requirements of rapid response of smart grids and to rapidly estimate the future operation trend of power loads, we propose a prediction method of an ultra-short-term power load interval based on the least squares support vector machine(LSSVM) model. This method predicts the interval by estimating the overall noise variance of the sample data on the basis of point prediction. which has a small calculated amount and greatly reduces the prediction time consumption. With regard to model parameter selection, the optimal training sample size and embedding dimensions are first determined using the parameter determination method of Gamma Test noise estimation, and then the optimal hyper-parameters are selected by the grid search method so that the fitting error of the LSSVM model on the training samples approximates the estimated minimum noise. To verify the validity of the proposed method in this paper, we apply the scheduling load data from a certain grid.to simulation experiments. The results show that the proposed method not only reflects the simplicity and high speed of the LSSVM but also ensures the accuracy of the prediction intervals by optimizing the model parameters.