Kodaira, D.; Tsukazaki, K.; Kure, T.; Kondoh, J. Improving Forecast Reliability for Geographically Distributed Photovoltaic Generations. Energies2021, 14, 7340.
Kodaira, D.; Tsukazaki, K.; Kure, T.; Kondoh, J. Improving Forecast Reliability for Geographically Distributed Photovoltaic Generations. Energies 2021, 14, 7340.
Kodaira, D.; Tsukazaki, K.; Kure, T.; Kondoh, J. Improving Forecast Reliability for Geographically Distributed Photovoltaic Generations. Energies2021, 14, 7340.
Kodaira, D.; Tsukazaki, K.; Kure, T.; Kondoh, J. Improving Forecast Reliability for Geographically Distributed Photovoltaic Generations. Energies 2021, 14, 7340.
Abstract
Photovoltaic (PV) generation is potentially uncertain. Probabilistic PV generation forecasting methods have been proposed with prediction intervals (PIs). However, several studies have dealt with geographically distributed PVs in a certain area. In this study, a two-step probabilistic forecast scheme is proposed for geographically distributed PV generation forecasting. Each step of the proposed scheme adopts ensemble forecasting based on three different machine-learning methods. In this case study, the proposed scheme was compared with conventional non-multistep forecasting. The proposed scheme improved the reliability of the PIs and deterministic PV forecasting results through 30 days of continuous operation with real data in Japan.
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.