Renewable energy generation prediction and optimization control based on intelligent algorithms and big data

Renewable energy, as one of the main development directions in the current energy field, mainly faces issues such as power generation load prediction, production prediction, and facility maintenance prediction. Therefore, this article took the main direction of renewable energy, namely photovoltaic power generation, as an example and proposed collecting and analyzing massive data such as lighting and power generation faced by photovoltaic power generation systems through big data analysis. Then, based on intelligent algorithms (SaDE-BPNN), the power generation was predicted, and the system control was optimized by tracking the sun’s concentrated light. Finally, this article validated the effectiveness of intelligent algorithms and big data analysis through comparative testing divided into three groups. The test results showed that the accuracy of using only the LSTM (Long Short-Term Memory) prediction model was 87.1%. The accuracy of further adding big data analysis was 89.8%, and the accuracy of further adding intelligent algorithms was 96.4%. Therefore, this study found that intelligent algorithms and big data analysis could effectively improve the accuracy of renewable energy generation prediction.