With the acceleration of China's energy transformation process and the rapid increase of renewable energy market demand, the photovoltaic (PV) industry has created more jobs and effectively alleviated the employment pressure of the labor market under the normalization of the epidemic situation. First, to accurately predict China’s solar PV installed capacity, this paper proposes a multi-factor installed capacity prediction model based on bidirectional long short-term memory-grey relation analysis. The results show that, the MAPE value of the GRA-LSTM combined model established in this paper is 5.995, compared with the prediction results of other models, the prediction accuracy of the GRA-BiLSTM model is higher. Second, the BiLSTM model is used to forecast China’s installed solar PV capacity from 2020 to 2035. The forecast results show that China’s newly installed solar PV capacity will continue to grow and reach 2833GW in 2035. Third, the employment number in China’s solar PV industry during 2020–2035 is predicted by the employment factors (EF) method. The results show that the energy transition in China during 2020–2035 will have a positive impact on the future stability and growth of the labor market in the solar PV industry. Overall, an accurate forecast of solar PV installed capacity can provide effective decision support for planning electric power development strategy and formulating employment policy of solar PV industry.
Forecasting of China’s solar PV industry installed capacity and analyzing of employment effect: based on GRA-BiLSTM model