并行自适应混沌优化方法在中长期电量预测中的应用  

APPLICATION OF PARALLEL ADAPTIVE CHAOTIC OPTIMIZATION IN MIDDLE AND LONG TERM ELECTRICITY CONSUMPTION FORECASTING

作　　者：唐巍[1] 高春成[1] 

机构地区：[1]中国农业大学信息与电气工程学院,北京市海淀区100083

出　　处：《电网技术》2005年第11期30-35,共6页Power System Technology

摘　　要：根据不同地区电量特点将电量、电量增量发展规律进行了分类,给出了相应的电量和电量增量预测模型。基于混沌运动的初值敏感性和对混沌优化搜索过程的分析,提出了并行自适应混沌优化方法。在此基础上,应用并行自适应混沌优化方法确定电量预测模型参数,给出了具体实现步骤和主要措施。实际电网电量预测结果表明:并行自适应混沌优化方法能够更为快速、准确地确定预测模型参数,电量增量预测的精度高于电量预测精度,同时也进一步证实了文中提出的各种预测模型的有效性。According to the electricity consumption features of different districts, the electricity consumption and incremental electricity consumption are classified, and corresponding models to forecast them are given. Based on the initial value sensitivity of chaotic motion and the analysis of optimal searching process, a parallel adaptive chaotic optimization (PACO) method is proposed. On this basis the parameters of the forecasting model for electricity consumption are determined by use of PACO, and the concrete procedure and main measures to implement the proposed method are presented. The forecasting results of electricity consumption of practical power network show that using the proposed method the parameters of the forecasting models can be rapidly and accurately determined, the forecasting accuracy of incremental electricity consumption is better than that of electricity consumption, and the effectiveness of the proposed forecasting models are verified.

关 键 词：混沌优化方法 电量预测 自适应 并行 应用 中长期 预测模型 模型参数 初值敏感性 发展规律 搜索过程 混沌运动 实现步骤 预测结果 预测精度 增量预测 

分 类 号：TP273.2[自动化与计算机技术—检测技术与自动化装置] TH112.1[自动化与计算机技术—控制科学与工程]