Multivariate Spatio-temporal Solar Generation Forecasting: A Unified Approach to Deal With Communication Failure and Invisible Sites

Short-term prediction of multivariate dynamical processes evolving over time when data are partially observable is a challenging task. The power from solar resources has reached grid parity and must be predicted based on real-time observations available up to the current time step to ensure efficient power systems operation. However, solar data in the form of a time series generated by a network of sensors are not always available. Invisi-ble solar sites and communication failure are two causes that leave the energy management data acquisition system with an incomplete solar time series, thus leading to inaccurate forecasts. This paper addresses the impact of partially observable measurements on short-term solar power prediction. We present a low-rank tensor learning scheme to predict six-hour-ahead solar power generation. We use actual multivariate spatio-temporal National Renewable Energy Laboratory solar data in the state of Kansas presented in the form of tensors along with a photovoltaic power conversion model. A design of experiments experimental framework has been proposed to evaluate single and joint effects of spatio-temporal partially observable sites and the regulating parameters on forecasting accuracy. Numerical results show the capability of the framework to uncover detailed insight into the forecasting model behavior.