基于DPBIL-SVM混合模型的电力现货市场出清价预测研究

为准确预测现货市场出清价,利用改进的基于种群增量学习的进化算法(DPBIL)对SVM参数进行优化,构建了基于DPBIL-SVM的混合预测模型,将该模型应用于挪威电力市场短期电价预测中,并与灰色GM(1,1)模型和BP人工神经网络模型进行比较。结果表明,所提模型能够将现货市场出清价预测误差控制在5%以下,合格率97%,效果优于灰色GM(1,1)模型和BP人工神经网络模型,符合现货市场实际报价的要求。

Power Spot Market Clearing Price Forecasting Based on DPBIL-SVM Hybrid Model

In order to predict the clearing price of spot market accurately, the improved evolutionary algorithm based on population learning was used to optimize the SVM parameters, and a hybrid prediction model was constructed based on the DPBIL-SVM. The model was applied to the short-term electricity price prediction of Norwegian electricity market. Compared with the grey GM (1,1) model and BP artificial neural network model, the results show that the proposed model can control the clearing price prediction error of the spot market to less than 5%, and the qualified rate was 97%. The prediction effect was better than that of the GM (1,1) and BP artificial neural network model, which meets the actual quotation requirements of the spot market.