基于小波变换和季节性Holt-Winters模型的 短期负荷预测方法

精准的负荷预测对售电公司在电力市场中的运行起着十分重要的作用,而企业用户的负荷受多种因素的影响具有不平稳的特性,对此,提出了基于离散小波分解和粒子群优化的季节性Holt-Winters模型的短期负荷预测方法.针对原始负荷序列周期性不平稳的特性,利用离散小波变换对原始负荷序列进行分解,并采用季节性Holt-Winters模型进行预测,同时借助小波去噪和粒子群算法进一步提高预测模型的准确性.小波去噪在过滤原始数据中潜在的噪声的同时,对数据进行平滑处理,而粒子群算法能让Holt-Winters模型在训练过程找到最优参数.采用该模型来预测具有不同变化趋势的日负荷曲线,结果表明所提出的模型具有较高的预测精度,可适用于不同用电类型的用户负荷短期预测.

A short-term load prediction method based on the wavelet transform and seasonal Holt-Winters model

Accurate load forecasting plays a very important role in the operation of power selling companies in the power market. However, the load of enterprise users is unstable under the influence of various factors. Therefore, a short-term load forecasting method based on the seasonal Holt-Winters model of discrete wavelet decomposition and particle swarm optimization is proposed. In view of the unsteady periodicity of the original load sequence, the discrete wavelet transform is used to decompose the original load sequence, and the seasonal Holt-Winters model is adopted for prediction. Meanwhile, wavelet denoising and particle swarm optimization algorithm are used to further improve the accuracy of the prediction model. Wavelet denoising not only eliminates the potential noise in the original data, but also smoothes the data, and particle swarm optimization allows Holt-Winters model to find the optimal parameters during training. The model is used for short-term prediction of load data with different variation characteristics, and the experimental results show that the model has good prediction accuracy and can be applied to short-term load prediction of users with different power consumption types.