Power generation forecast for a parabolic trough solar thermal system based on LSTM

Parabolic trough solar thermal power system (PTSTPS) is a kind of renewable energy technology, which can not only bear a large proportion of the basic power load, but also bear the flexible peak regulation of the grid. Its generation prediction is an important basis for the design, investment, and operation of power station. Based on the hourly meteorological resources data of a 50MW PTSTPS in China from 2017 to 2021, this paper firstly conducted abnormal data elimination and normalization operations, and then conducted correlation analysis to determine the final training samples and test samples. In this paper, an LSTM neural network is constructed to predict hourly power generation of the PTSTPS, and the model is tested and optimized by using test samples. Finally, the predicted results are compared with hourly, daily, and monthly power generation calculated by SAM software. The relative errors are -8.96%-8.88%, -3.23%-4.07% and -0.35%- 0.33%, respectively. Therefore, the constructed LSTM neural network is suitable for the prediction of the electricity generation of the PTSTPS, and provides a new technical way for the real-time and accurate electricity generation prediction of the solar thermal power system.