OICC Press International Journal of Energy and Environmental Engineering 2251-6832 14 4 (December 2023) 2023 04 01 Development of a day-ahead solar power forecasting model chain for a 250 MW PV park in India 10.1007/s40095-023-00560-6 EN Arindam Roy German Aerospace Center (DLR) Institute of Networked Energy Systems, Oldenburg, 26129, DE Aravindakshan Ramanan Deutsche Gesellschaft Für Internationale Zusammenarbeit (GIZ) GmbH, New Delhi, 110029, IN Barun Kumar Solar Radiation Resource Assessment, National Institute of Wind Energy, Chennai, 600100, IN Chris Alice Abraham Solar Radiation Resource Assessment, National Institute of Wind Energy, Chennai, 600100, IN Annette Hammer German Aerospace Center (DLR) Institute of Networked Energy Systems, Oldenburg, 26129, DE Elena Barykina Overspeed GmbH & Co. KG, Oldenburg, 26129, DE Detlev Heinemann Institute of Physics, Carl von Ossietzky University of Oldenburg, Oldenburg, 26111, DE Naveen Kumar Solar Radiation Resource Assessment, National Institute of Wind Energy, Chennai, 600100, IN Hans-Peter Waldl Overspeed GmbH & Co. KG, Oldenburg, 26129, DE Indradip Mitra Deutsche Gesellschaft Für Internationale Zusammenarbeit (GIZ) GmbH, New Delhi, 110029, IN Prasun Kumar Das Solar Radiation Resource Assessment, National Institute of Wind Energy, Chennai, 600100, IN R. Karthik Solar Radiation Resource Assessment, National Institute of Wind Energy, Chennai, 600100, IN K. Boopathi Solar Radiation Resource Assessment, National Institute of Wind Energy, Chennai, 600100, IN K. Balaraman Solar Radiation Resource Assessment, National Institute of Wind Energy, Chennai, 600100, IN Journal Article 2023 04 01 Abstract Due to the steep rise in grid-connected solar Photovoltaic (PV) capacity and the intermittent nature of solar generation, accurate forecasts are becoming ever more essential for the secure and economic day-ahead scheduling of PV systems. The inherent uncertainty in Numerical Weather Prediction (NWP) forecasts and the limited availability of measured datasets for PV system modeling impacts the achievable day-ahead solar PV power forecast accuracy in regions like India. In this study, an operational day-ahead PV power forecast model chain is developed for a 250 MWp solar PV park located in Southern India using NWP-predicted Global Horizontal Irradiance (GHI) from the European Centre of Medium Range Weather Forecasts (ECMWF) and National Centre for Medium Range Weather Forecasting (NCMRWF) models. The performance of the Lorenz polynomial and a Neural Network (NN)-based bias correction method are benchmarked on a sliding window basis against ground-measured GHI for ten months. The usefulness of GHI transposition, even with uncertain monthly tilt values, is analyzed by comparing the Global Tilted Irradiance (GTI) and GHI forecasts with measured GTI for four months. A simple technique for back-calculating the virtual DC power is developed using the available aggregated AC power measurements and the inverter efficiency curve from a nearby plant with a similar rated inverter capacity. The AC power forecasts are validated against aggregated AC power measurements for six months. The ECMWF derived forecast outperforms the reference convex combination of climatology and persistence. The linear combination of ECMWF and NCMRWF derived AC forecasts showed the best result.