捷克短期负荷预测的软计算模型
Short Term Load Forecasting Models in Czech Republic Using Soft Computing Paradigms
摘要：
本文提出了一个比较研究的软计算模型，即多层感知器网络，Elman递归神经网络，径向基函数网络，Hopfield模型，模糊推理系统和混合模糊神经网络的捷克共和国的小时电力需求预测。使用七年的实际小时负荷数据对软计算模型进行了训练和测试。提出的技术预测提前2天的电力需求的比较。仿真结果表明，混合模糊神经网络和径向基函数网络是电力需求分析和预测的最佳选择。
英文摘要：
This paper presents a comparative study of six soft computing models namely multilayer perceptron networks, Elman recurrent neural network, radial basis function network, Hopfield model, fuzzy inference system and hybrid fuzzy neural network for the hourly electricity demand forecast of Czech Republic. The soft computing models were trained and tested using the actual hourly load data for seven years. A comparison of the proposed techniques is presented for predicting 2 day ahead demands for electricity. Simulation results indicate that hybrid fuzzy neural network and radial basis function networks are the best candidates for the analysis and forecasting of electricity demand.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
