Aiming at the strong power volatility of photovoltaic power generation and the fact that traditional power prediction methods are difficult to meet the high-precision requirements, a TBiLSTM hybrid network model based on multi-temporal sequence feature fusion is proposed. Firstly,this model normalizes the input multi-dimensional temporal sequence features and introduces learnable positional encoding to retain the sequential information of the time series. Secondly,the preprocessed feature matrix is input into the Transformer encoder,and the multi-head self-attention mechanism is used to model the global dependencies among time steps. After the output of the encoder undergoes residual connection and layer normalization,it is fed into the Bidirectional Long Short-Term Memory Network （BiLSTM）,and the BiLSTM extracts the local temporal dynamic characteristics through forward and backward propagation. Finally,the hidden states of the BiLSTM are mapped to the predicted power sequence through the fully connected layer. Two groups of public datasets are used in the experiment,covering multi-dimensional temporal sequence features such as irradiance,temperature,pressure,and humidity. The experimental results show that compared with the BiLSTM,CNN,and CNN-BiLSTM prediction models,the proposed model reduces the mean absolute error by an average of 37.16%,47.63%,and 12.43%,the mean absolute percentage error by an average of 55.59%,68.80%,and 8.79%, the mean squared error by an average of 52.11%,68.47%,and 20.98%,the root mean squared error by an average of 33.75%,45.49%,and 11.13%, and increases the coefficient of determination by an average of 4.03%,4.03%,and 2.13%. This verifies the reliability and superiority of the proposed model.
Key words
photovoltaic power generation /
power forecasting /
time series /
multi-head self-attention mechanism /
transformer /
BiLSTM
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