中长期电量预测的傅里叶-马尔科夫修正模型

灰色模型在中长期电量预测中只对电量呈近似指数规律单调增长的序列才有较高的预测精度.随着电量变化随机波动性的增强,建立新的修正预测模型是十分必要的.针对灰色模型抗干扰能力差的问题,提出了灰色预测的傅里叶-马尔科夫修正模型,先利用傅里叶级数法,提取周期信息,优化电量变化的指数率,再采用马尔科夫链法,将电量波动随机性嵌入模型之中,从而对灰色预测的原始残差进行二重修正,提高预测模型的适应性和灵活性.通过实例分析以及对比验证表明,该模型有效地提高了预测精度.

Mid-long Term Load Forecasting Model with Fourier Series and Markov Theory Residual Error Correction

Gray model is widely used in mid-long term electricity demand forecasting, but the model fits exponentially increasing data more precisely. Due to China''s economic growth rate fluctuations, the increase in electricity consumption is slowing down, and electricity varies stochastically. So it is necessary to propose a new model to reflect the new situation. To solve the problem of the poor anti-interference ability of grey model, this paper proposes a model with Fourier series and Markov theory residual error correction based on grey model. This model applies Fourier series method to optimize electricity changing rate, and Markov chain method to embed the random property in gray forecasting model for doubly correcting the residual error, which can improve the adaptability and flexibility. The proposed model is verified by actual load data, and it indeed improves the forecasting accuracy.