Analysis of solar generation and weather data in smart grid with simultaneous inference of nonlinear time series

Abstract

Smart Grid is an important component of Smart City, where more renewable power generation and better energy management is required. Forecast on renewable power generation, from sources such as solar and wind, is crucial for better energy management. However, the current forecast methods lack a comprehensive understanding of the natural processes, and are thus limited in precise prediction. In this paper, we introduce simultaneous inference to analyze the solar generation and weather data for better predictions. We first introduce a local linear model for nonlinear time series, and present the construction of the simultaneous confidence bands (SCB) of the time-varying coefficients, which provide more information on the dynamic properties of the model. We then use the simultaneous inference for solar intensity prediction using a real trace, where the superior performance of the proposed scheme is demonstrated over existing approaches.