Studies on a Load Forecasting Method with the Evolutionary Parallel Algorithm

提出了一种基于并行禁忌搜索的简化模糊推理中确定模糊隶属度函数位置和个数的有效方法。PTS是禁忌搜索(TS)的一种改进算法，用于求解组合优化问题，具有几种策略。一种是将TS的邻域分解为若干子邻域，以减少计算量。二是在TS算法中引入多个禁忌长度，使解的候选者更加多样化，以获得更好的解。因此，PTS可以在求解精度和计算时间方面改进传统的TS。具体地说，PTS对优化电力系统负荷预测的简化模糊推理结构具有关键作用。将基于PTS的简化模糊推理成功地应用于超前一步日最大负荷预测的实际数据。将PTS与SA、GA、TS等传统方法进行了比较，验证了PTS的有效性。仿真结果表明：1.PTS算法能够有效地求出全局极小值的高度近似解。该方法允许在信息准则下最小化最大预测误差。利用简化的模糊推理，通过隶属度函数给出输入变量的有用信息。

This project has proposed an efficient method for determining the location and number of fuzzy membership functions in simplified fuzzy inference with parallel tabu search(PTS). PTS is an improved algorithm of tabu search (TS) for solving a combinatorial optimization problem and has a couple of strategies. One is to decompose the neighborhood of TS into several subneighborhoods so that computational effort is reduced. The other is to introduce multiple tabu lengths into the TS algorithm and make solution candidates more diverse to obtain a better solution. As a result, PTS allows to improve the conventional TS in terms of solution accuracy and computational time. Specifically, PTS plays a key role to optimize the structure of simplified fuzzy inference for load forecasting in power systems. The PTS-based simplified fuzzy inference was successfully applied to real data of one-step ahead daily maximum load forecasting. A comparison between PTS and the conventional methods such as SA, GA and TS was made to demonstrate the effectiveness of PTS. The simulation results have shown the following:1. PTS is capable of evaluating a highly approximate solution of a global minimum efficiently.2. The proposed method allows to minimizes the maximum prediction error with the information criterion.3. The use of simplified fuzzy inference gives useful information of input variables through the membership functions.