Generalization of solar power yield modeling using knowledge transfer

Abstract

Recently, the energy issue is an important factor all over the world due to the fact of sustainability perspective. This paper has proposed a new Transfer Learning (TL) based PV energy yield model, which generalizes the knowledge transfer process from the source domain to the target domain differ on time and location. The solar power yield forecasting highly depends on meteorological data, which has high variability influenced by both time and target place/location. Therefore, a solar energy forecasting model from a source may not perform well to a target domain due to differences in the data distribution as evident by the experiment result presented in this paper. The extended historical data-driven solar power yield models may perform better to a certain extent with the complex nonlinear meteorological data relationships. But due to rapid climate change issue, the knowledge learned from historical data may not apply to the near future. Furthermore, it is impractical to assume the availability of a large dataset and ignore the probability of negative knowledge transfer tendency of traditional regression models. To address this gap, this paper has proposed Transfer Support Vector Regression (Tr-SVR) that uses a novel weighting model based on patterns of a hybrid dataset, both source and target data for training. The proposed Tr-SVR only transfer the knowledge fits the target domain to reduce negative transfer and dependency on long historical data. The experimental results show that when the distribution of training and test data differs, the traditional machine learning method represented by ANN may face serious generalization challenges. In this scenario, the proposed Tr-SVR model has clear advantages in terms of error metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Maximum error, Mean Absolute Percentage Error (MAPE), and Sum of Squares of the Error (SSE).