并行自适应混沌优化方法在中长期电量预测中的应用

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.

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

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.