Seamless intra-day and day-ahead multivariate probabilistic forecasts at high temporal resolution

International audience; High temporal resolution intra-day and day-ahead photovoltaic (PV) power forecasts are important to maximize the value of PV systems because they enable stakeholders to participate in both the energy and ancillary services markets. Whereas most day-ahead electricity markets feature an hourly temporal resolution, intra-day markets may require forecasts at 5-minute resolution. In addition, battery integration can improve power system management in isolated grids with high PV power penetration, but battery control requires high temporal resolution forecasts. We propose an efficient method based on pattern matching to generate multivariate probabilistic forecasts, approximated by trajectories, at high temporal resolution and without the need to separately forecast the marginals and estimate the covariance matrix. We compare the proposed method against quantile regression forests in combination with copula theory and show that our method reduces the forecast time by approximately 98% and simplifies the modeling chain while incurring a minor performance penalty.