【作者】 崔志坤；

【导师】 王翠茹；

【作者基本信息】 华北电力大学（河北） ， 计算机应用技术， 2008， 硕士

【摘要】 短期负荷预测是电力系统安全经济运行的前提,在电力系统发展日趋复杂的今天,传统的负荷预测技术越来越难以满足电力部门负荷预测精度要求,应用智能算法进行电力系统的短期负荷预测,提高负荷预测的精度和稳定性,具有十分重要的意义。在分析了电力系统负荷预测的意义和方法之后,本文在研究模糊推理和神经网络的基础上,提出了构造模糊神经网络模型的新方法,将模糊推理融入到了BP网络中,并且用遗传算法来训练网络参数,直到误差趋于一稳定值,然后用优化的权值进行BP算法,实现短期负荷预测,仿真实验给果表明该方法加快网络学习速度,并能提高负荷预测精度。更多还原

【Abstract】 Short-term load forecasting is the precondition of economic and secure operation of power system. With the power system becoming more and more complex, it’s demonstrated that those traditional load-forecasting technologies can’t satisfy the requirement of load forecasting which becomes more and more strict. So using intelligent technologies to improve the forecasting accuracy and stability of the load forecasting of electric power system is a new character of the short-term load forecasting field of electric power system. After analyzed the meaning and method of power system load forecasting, based on the analysis of fuzzy inference and neural network biology characteristic, the paper presents a new method of constructing Fuzzy Neural Network (FNN), which merges the fuzzy inference into BP network. In this method, GA is used to optimize connection weights of forward-back neural network until the learning error has tended to stability. Then we use BP algorithm with optimized weights to finish short-term load forecasting process. The results of the emulation experimental show that this method can quicken the learning speed of the network and improve the predicting precision.更多还原