Day-Ahead Wind Power Forecasting Based on Wind Load Data Using Hybrid Optimization Algorithm

X.) Abstract: Accurate wind power forecasting is essential to reduce the negative impact of wind power on the operation of the grid and the operation cost of the power system. [...] The advantage of the statistical methods is that the forecasting spontane- ously adapts to the position of the wind farm to automatically reduce the system error. [...] In contrast, the VMD is a good way to decompose and process the original data, which reduces the non-stationarity of the wind power sequence and improves the anti-interference ability and robustness of the model. [...] After the optimization of the firefly algorithm, it was determined that the parameter combination of the LSTM neural network is as follows: the number of hidden layers is 120, the time window step is 6, the number of training times is 160, and the learning rate is 0.015. [...] R2 can measure the regression fitting effect of the model; the larger R2 is, the better the fitting effect of the model will be.