High-Resolution PV Forecasting from Imperfect Data: A Graph-Based Solution

High-Resolution PV Forecasting from Imperfect Data: A Graph-Based Solution

Blog Article

Operating power systems with large amounts of renewables requires predicting future photovoltaic (PV) production with fine temporal and spatial resolution.State-of-the-art techniques combine numerical weather predictions with statistical post-processing, but their resolution is too coarse for applications such as local congestion management.In this paper we introduce computing methods for multi-site PV forecasting, which exploit the intuition NUTRA SEA LIQUID GELS that PV systems provide a dense network of simple weather stations.These methods rely entirely on production data and address the real-life challenges that come with them, such as noise and gaps.Our approach builds on graph signal processing for signal reconstruction and for forecasting with a linear, spatio-temporal autoregressive (ST-AR) model.

It also introduces a data-driven clear-sky production estimation for normalization.The proposed framework was evaluated over one year on both 303 real PV systems under commercial monitoring across Switzerland, and 1000 simulated ones based on high-resolution weather data.The results demonstrate the performance and robustness of the approach: with gaps of four hours on average saddle accessores in the input data, the average daytime NRMSE over a six-hour forecasting horizon (in 15 min steps) and over all systems is 13.8% and 9% for the real and synthetic data sets, respectively.