Intra-hour Forecasting for a 50 MW Photovoltaic System in Uruguay: Baseline Approach

Abstract

The increased penetration of photovoltaic (PV) generation introduces new challenges for the stability of electricity grids. In this work, machine learning (ML) techniques were implemented to forecast PV power production up to 1-hour ahead with a 10-minute granularity. Three different input combinations were utilised: Model 1 (M1) using the AC power only, Model 2 (M2) using the elevation angle (α), azimuth angle (φ) and AC power and Model 3 (M3) using α, φ, the AC power and satellite observations (SAT) aiming to improve the forecasting performance. Historical PV operational data were used for the training and validation stages of intra-hour PV forecasting models for time t + 10 to 60 minutes ahead. The results obtained over the test set period (15% of the data, i.e. ≈ 110 days) have shown that M2 exhibits the best-performance with a normalised root mean square error (nRMSE) in the range of 7.6% to 14.2%, whereas the skill score (SS) ranged between 6.5% and 30.9% for the 10- to 60-minute ahead, respectively.