摘要
光伏电站的发电功率高度依赖于不同的天气条件,其变化无规律可循,从而给电网管理带来挑战。因此,对光伏发电功率进行预测研究,以确保电网安全、稳定运行。首先,按季节和天气类型划分历史发电数据,经数据分析后,将温度与历史发电功率作为输入,构建了ANFIS与模糊聚类-ESN两个光伏发电功率预测模型。利用Matlab模糊逻辑工具箱构建ANFIS模型,而对于模糊聚类-ESN模型的构建,先采用模糊聚类处理输入数据,再利用ESN进行训练与预测。通过对两个预测结果的比较,模糊聚类-ESN模型的预测精度高于ANFIS模型。
Power generation output of a PV plant is highly dependent on different weather conditions, but its changes are irregular, which pose a challenge to grid management. For this, scholars to predict the photovoltaic power generation to ensure that the grid safe and stable operation. In this paper, the historical power generation data is divided directly by season and weather type. After the data analysis, take the temperature and historical power generation as input. Two photovoltaic power generation models, the adaptive neural fuzzy reasoning system （ANFIS） and the fuzzy clustering-ESN, are constructed. ANFIS model is constructed by Matlab fuzzy logic toolbox. The construction of fuzzy clustering-ESN model is used to process the input data with fuzzy clustering, and then ESN is used to train and predict. Comparing the two prediction results, the fuzzy clustering-ESN model has higher prediction accuracy than ANFIS model.
出处
《软件导刊》
2018年第1期157-161,164,共6页
Software Guide
关键词
自适应神经模糊推理系统
模糊聚类
回声状态网络
光伏发电功率预测
adaptive neuro-fuzzy inference system（ANFIS）
fuzzy clustering
echo state network （ESN）
photo-voltaic power generation prediction
