光伏功率的准确预测对于电网的安全稳定和经济运行具有重大意义.为此,提出了一种日前光伏功率预测方法,利用小波变换(wavelet transform,WT)将数值天气预报数据(numerical weather prediction,NWP)和光伏功率数据分解为具有时间信息的频率数据,消除数据信息中随机性和波动性对预测精度的影响,利用卷积神经网络(convolutional neural network,CNN)模型深度挖掘输入数据的季节性特征和空间关联特性,利用双向长短期记忆网络(bi-directional long-short term memory,BiLSTM)模型获取输入数据序列的时间相关性,构建基于WT-CNN-BiLSTM的日前光伏功率预测模型.以某一光伏电站为计算对象,在不同季节和气候条件下对比分析WT-CNN-BiLSTM模型、CNN-BiLSTM模型、LSTM(long-short term memory)模型、GRU(gated recurrent unit)模型以及PSO-BP(particle swarm optimization-back propagation)模型的预测结果,计算结果表明WT-CNN-BiLSTM模型的预测精度高于其他模型的预测精度.
Day-Ahead Photovoltaic Power Forecasting Based on WT-CNN-BiLSTM Model
The accurate forecast of photovoltaic power is of great significance for the security,stability and economic operation of the power grid.Therefore,a day-ahead photovoltaic power forecasting method is proposed.The method of wavelet transform(WT)is used to decompose numerical weather prediction(NWP)data and photovoltaic power data into frequency data with time information,eliminating the influence of randomness and volatility in data information on forecasting accuracy.Convolutional neural network(CNN)model is used to deeply excavate the seasonal characteristics and spatial correlation characteristics of input data,and bi-directional long-short term memory(BiLSTM)model is used to obtain the temporal correlation of input data series.A day-ahead photovoltaic power forecasting model based on WT-CNN-BiLSTM is constructed.Taking a certain photovoltaic power station as the calculation object,the forecasting results of WT-CNN-BiLSTM model,CNN-BiLSTM model,LSTM model,GRU model and PSO-BP model are compared and analyzed under different seasons and climatic conditions.The calculation results show that the forecast-ing accuracy of WT-CNN-BiLSTM model is higher than that of other models.
photovoltaic power forecastwavelet transform(WT)convolutional neural network(CNN)bi-directional long-short term memory(BiLSTM)
万方数据
维普
