基于特征空间重构的差分-LSSVR短期电煤价格预测

掌握电煤价格变化关系到企业参与电力市场的定价策略以及优化内部运行成本的方针制定。针对导致煤价变化的影响因子数量众多且影响权重变化频繁的难题,在煤价影响因素精细化分析、因素检验与季节差分等方法对特征空间重构的基础上,建立中短期煤价最小二乘支持向量机模型。依据计量经济学等理论筛选煤价相关影响因素,并利用协整检验及格兰杰检验提取主要影响因素压缩特征因子维度;通过建立多年同期对比时序信息数据集,利用季节性差分消除时序信息中周期性因素以及随机干扰的影响,实现特征空间重构,改善输入数据质量。建立基于LSSVR的趋势提取学习模型,并结合周期价格及残差,实现中短期的煤炭价格预测。构建多个对比模型,验证了所提模型的有效性。

Difference-LSSVR Short-Term Electricity Coal Price Forecast Based on Feature Space Reconstruction

A good command of the price changes of thermal coal is closely related to the pricing strategy of companies participating in the power market and the formulation of policies to optimize internal operating costs.To solve the problem that there are a large number of influencing factors leading to the change of the coal price and frequent changes of the influence weight,this paper establishes the Least Square Support Vector Regression(LSSVR)model of the coal price in the short and medium term on the basis of the reconstruction of feature space by means of refinement analysis,factor checking and seasonal difference and other methods of influencing factors of the coal price.Firstly,according to econometrics and other theories,the influencing factors of the coal price are screened,and the main influencing factors are extracted by co-integration check and Granger test to compress the characteristic factor dimension.By establishing the data set of multi-year synchronous comparison timing sequence information and using seasonal difference to eliminate the influence of periodic factors and random interference in timing sequence information,the reconstruction of feature space is realized and the quality of input data is improved.Furthermore,a trend extraction learning model based on LSSVR is established,and combined with periodic prices and residuals,the medium and short-term coal price forecasts are realized.Finally,several comparison models are constructed to verify the effectiveness of the proposed model.