A Hybrid Approach for Short-Term PV Power Forecasting in Predictive Control Applications

Publication Type

Conference Paper

Date Published

06/2019

Abstract

The installed capacity of grid-connected photo-voltaic (PV) systems continues to grow. Due to variability in PV power production, accurate forecasts are essential to support power system operation. This paper presents a hybrid PV power forecasting method with parallel architecture, which combines a Seasonal Auto-Regressive Integrated Moving Average (SARIMA) model with an Artificial Neural Network (ANN) model using weighing factors computed periodically via a least squares problem. The method can be used to obtain short-term forecasts with prediction horizons from 15 minutes to 1 day or more, and is therefore well-suited for Model Predictive Control (MPC). We apply the method to forecast the power output of a rooftop PV system at the Lawrence Berkeley National Laboratory. Our analysis provides high-level suggestions to optimize the order and structure of SARIMA and ANN models. The results show that the hybrid method can reduce forecast error by 10% compared with using the individual models separately, while increasing resilience and redundancy due to its parallel architecture.

Conference Name

2019 IEEE Milan PowerTech

Year of Publication

2019

Publisher

IEEE

Conference Location

Milan, Italy
Research Areas