Photovoltaic Short-term Power Forecasting Model Based on Hierarchical Clustering&BILSTM
To tackle the challenges of inefficiency and inaccuracy in nonlinear photovoltaic power forecasting,a novel hybrid photovoltaic power forecasting model is introduced.Firstly,the input data dimensionality is reduced through the support vector machine(SVM)extraction module;Then,the balanced iterative reducing and clustering using hierarchies(BIRCH)clustering module is used to mine the information from the data and segment the feature library.Finally,a bi-directional long short-term memory network(bilstm)forecasting model is established according to the fluctuation characteristics of photovoltaic power output.When tested on real-world datasets from European Centre for Medium-Range Weather Forecasts(ECMWF),the proposed hybrid model significantly outperforms eight mainstream machine learning algorithms,mean absolute error(MAE)and mean squared error(MSE)are reduced by 4.3%to 59.75%and 35.65%to 78.29%respectively.The model's strong interpretability further underscores its potential for the widespread application in power industry.
photovoltaic power generationSVMBIRCHBILSTMpower forecasting
万方数据
维普
