Prediction of Solar Power Generation and Maintenance Activities for 1MW Power Plant

Solar energy is going to play a crucial role in a green and clean environment by reducing pollution and global warming. Solar power generation forecasting is very important for the energy market and smart grids. The semi-structured data set was acquired from our university’s 1-Megawatt solar power facility. The data set was having twelve structured and one unstructured column. Manual entries concerning maintenance tasks and weather data are included in the unstructured column. With the help of weather parameters and maintenance operations, the unstructured column is utilized to generate new features. Random forest regressor, XG-boost, Naive Bayesian, univariate LSTM, and moving average models are applied to predict solar power generation using this data set. XG-boost and random forest regression models have less RMSE and MAE as compared to Naive Bayesian and univariate LSTM. The tree-based approaches work well with time-series data which has exogenous variables. The maintenance activities are also predicted in this research work. It is very difficult to predict the module cleaning in advance.