考虑需求价格弹性的CS-SVM短期负荷预测方法

Short-term load forecasting method based on cuckoo search algorithm and support vector machine considering demand price elasticity

针对电力市场中电力需求受多元扰动因素影响、需求价格弹性变化增大的问题,为获取更准确、更综合的电力负荷预测值,提出一种考虑需求价格弹性的CS-SVM短期负荷预测方法.采用Pearson相关系数法分析负荷自相关性以及负荷与历史温度、湿度、电价、负荷差和电价差之间的相关性.基于需求价格弹性定义,建立了需求价格弹性模型,来反映电力市场交易对负荷的影响.利用布谷鸟搜索算法优化支持向量机的参数,建立了考虑需求价格弹性的CS-SVM短期负荷预测模型.以美国PJM电力市场哈特福德州的实际数据为例进行预测,并与对比模型进行预测结果误差对比分析.结果表明,所提出模型的平均相对百分比误差为13.43%,且相比于不引入需求价格弹性时的模型预测精度提高了5.31%.

To solve the problems that electricity demand was affected by multiple disturbance factors in the electricity market,and the price elasticity of demand was increased,for obtaining more accurate and comprehensive power load forecast value,the cuckoo search algorithm and support vector machine(CS-SVM)short-term load forecasting method was proposed with considering demand price elasticity.The Pearson correlation coefficient method was used to analyze the load autocorrelation and the correlation among load and historical load temperature,humidity,electricity price,load difference and electricity price difference.Based on the definition of demand price elasticity,a demand price elasticity model was established to reflect the impact of electricity market transactions on load.The cuckoo search algorithm was used to optimize the parameters of support vector machine,and the CS-SVM short-term load forecasting model was established with considering the price elasticity of demand.The actual data of a certain state in the PJM power market in the United States was taken as example to conduct the prediction,and the prediction error was compared with that of comparison model.The results show that the average relative percentage error of the proposed model is 13.43%,and the prediction accuracy is improved by 5.31%compared with that of the model without introduction of demand price elasticity.