Missing Data Imputation for Solar Yield Prediction using Temporal Multi-Modal Variational Auto-Encoder

Abstract

The accurate and robust prediction of short-term solar power generation is significant for the management of modern smart grids, where solar power has become a major energy source due to its green and economical nature. However, the solar yield prediction can be difficult to conduct in the real world where hardware and network issues can make the sensors unreachable. Such data missing problem is so prevalent that it degrades the performance of deployed prediction models and even fails the model execution. In this paper, we propose a novel temporal multi-modal variational auto-encoder (TMMVAE) model, to enhance the robustness of short-term solar power yield prediction with missing data. It can impute the missing values in time-series sensor data, and reconstruct them by consolidating multi-modality data, which then facilitates more accurate solar power yield prediction. TMMVAE can be deployed efficiently with an end-to-end framework. The framework is verified at our real-world testbed on campus. The results of extensive experiments show that our proposed framework can significantly improve the imputation accuracy when the inference data is severely corrupted, and can hence dramatically improve the robustness of short-term solar energy yield forecasting.