@inproceedings{34108,
  author = {Evangelos Vrettos and Christoph Gehbauer},
  title = {A Hybrid Approach for Short-Term PV Power Forecasting in Predictive Control Applications},
  abstract = {<p><span>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.</span></p>},
  year = {2019},
  journal = {2019 IEEE Milan PowerTech},
  month = {06/2019},
  publisher = {IEEE},
  address = {Milan, Italy},
  doi = {10.1109/PTC.2019.8810672},
  language = {eng},
}