基于电力消耗的行业总产值预测

电力行业是国民经济的基础性能源产业，对其他行业的发展起着至关重要的支撑作用。电力行业本身不存在库存现象进而能够相对真实近乎实时地反映行业经济运行情况，这使得从电力消耗到行业总产值的预测成为可能。针对某省规模以上工业企业基于电力消耗的总产值预测问题展开研究，结合该省2010—2013年近38 000家规模以上工业企业的用电量和总产值数据，利用基于粒子群优化参数的支持向量机建立预测模型。以2010年1月至2013年12月的数据作为训练样本，对2013年8月至2013年12月各行业的总产值进行预测和检验，并与常规交叉验证寻优的支持向量机模型和BP（back propagation）神经网络模型进行对比。结果表明，所采用的方法较其他方法可以更准确、可靠地预测行业总产值，基于用电量的行业总产值预测方法是科学、可行的。

Forecast of Industrial Output Value Based on Power Consumption

Power industry is the basic energy sector of national economy and plays an important support role for development of other industries. This paper studies the forecasting method of the output value of industrial enterprises above designated size in terms of power consumption. Based on the data of power consumption and output value of nearly 40 000 industrial enterprises above designated size, the forecasting model is built by the support vector machine optimized by particle swarm optimization (PSO-SVM). By using the data from January 2010 to July 2013 as the training sample of SVM, a forecasting and testing is made on the data from August 2013 to December 2013 and a comparison is also conducted between conventional SVM model and BP Neural Networks model. Result of the simulation shows that the PSO-SVM model can be more accurate and reliable than both SVM model and BP model in forecasting the industry output, and the forecasting method of industry output based on power consumption is scientific and feasible.