Short-term power load forecasting for a region based on
Power system management and operation rely heavily on short-term power
load forecasting. Accurate forecasting results can help reduce power
waste and economic losses. The existing power forecasting methods only
forecast the future load based on historical data, which factors have
the greatest influence on the power load is not considered enough, and
there are no effective methods for simultaneously mining time
characteristics and correlation characteristics of multidimensional time
series. Therefore, we propose a new hybrid approach, which combines LSTM
with attention mechanism and GA (genetic algorithm). In LSTM, GA
optimizes the number of layers, dense layers, hidden layer neurons, and
dense layer neurons, so as to determine the optimal parameters. On the
basis of the load data set containing five characteristics of dry bulb
temperature, dew point temperature, wet bulb temperature, humidity and
electricity price, the method proposed in this paper will be verified.
By comparing with RNN, LSTM, GRU, LSTM-Attention and GRU-Attention.
According to the experimental results, the application of the proposed
method noticeably minimizes the prediction error and elevates the
goodness of fit of the model.