基于模糊神经网络的电力负荷短期预测

针对电力负荷的特点，综合考虑了温度及日期类型等因素对日最大负荷的影响，提出了一种采用模糊神经网络进行短期负荷预测的方法，并详细介绍了该方法的实现过程。通过对EUNITE(the European Network of Excellence on Intelligent Technologies for Smart Adaptive Systems)网络提供的实际数据进行详细分析确定了影响日最大负荷的相关因素，进而选择了合适的模糊输入以建立相应的模糊神经网络预测模型，并取得了较为理想的预测结果。算例分析结果充分证明了模糊神经网络在短期电力负荷预测方面具有较好的应用前景。

Short-Term Load Forecasting Based on Fuzzy Neural Network

According to features of power load and considering the combined influence of temperature and day type, a fuzzy neural network approach based short-term load forecasting method is proposed and its implementation are presented in detail. By means of detailed analysis of the actual data offered by EUNITE(the European Network of Excellence on Intelligent Technologies for Smart Adaptive Systems) network, the correlative factors influencing daily peak load are determined, Here from appropriate fuzzy inputs are chosen and corresponding forecasting model based on fuzzy neural network is built. The forecasting results based on the data from East-Slovakia Power Distribution Company show that fuzzy neural network possesses evident potentiality in the field of short-term load forecasting.