Data-Driven Photovoltaic Generation Forecasting Based on a Bayesian Network With Spatial–Temporal Correlation Analysis

Abstract

Spatiotemporal analysis has been recognized as one of the most promising techniques to improve the accuracy of photovoltaic (PV) generation forecasts. In recent years, PV generation data of a number of PV systems distributed in a geographical locale have become increasingly available. This paper conducts a thorough investigation of the spatial–temporal correlation amongst PV generation data of distributed PV systems. PV generation data of different PV systems located at different sites may exhibit similar time varying patterns. To quantify such spatial correlation, a suitable spatial similarity metric is chosen and its applicability is examined. To evaluate the temporal correlations amongst the PV generation data collected from distributed PV systems, a shape-based distance metric is proposed. A data-driven inference model, built on a Bayesian network, is developed for a very short-term PV generation forecast (less than 30 min). The model utilizes historic PV generation and weather data, and incorporates the abovementioned spatial similarity and temporal correlation to support the PV output forecast. The experiment results show that the proposed method achieves a promising performance compared to a number of baseline methods.