摘要
以我国月电力消费量为例,研究了季节型增长趋势中长期电力指标的预测问题。提出采用离散小波变换对季节型增长趋势历史数据进行分解并对各频率分解系数分别进行重构,在剔除随机性波动后,将长期增长趋势及各规律性波动趋势通过RBF网络进行趋势外推预测,进而通过对不同趋势预测结果进行组合得到电力消费量的预测值。实证分析表明,经过离散小波分解处理后,RBF网络样本的规律性得到增强,其在有效模拟非线性变化规律的同时,泛化能力得以提高,因而具有较好的预测精度。
Based on the historical data of Chinese monthly electric energy demand,this paper researches the forecasting methods of seasonal increasing mid-long term electric indexes.It adopts discrete wavelet transform to decompose the sample series and reconstruct the decomposed results separately.After discarding the stochastic series,the long term increasing and fluctuant vectors are extended by RBF neural network.Adding the extended values together,it gets the forecasting results of electric energy demand.Empirical results show that after the decomposition by discrete wavelet transform,fluctuant trends of sample for RBF neural network are simplified.When it simulates the nonlinear trends,the generalization capability of RBF neural network is improved and the forecasting results are of good performance.
出处
《中国管理科学》
CSSCI
北大核心
2010年第2期108-112,共5页
Chinese Journal of Management Science
基金
国家自然科学基金资助项目(70671039)
华北电力大学青年教师科研基金
关键词
季节型增长趋势
离散小波变换
RBF网络
泛化能力
seasonal increasing trend
discrete wavelet transform
RBF neural network
generalization capability
