Article
Version 1
Preserved in Portico This version is not peer-reviewed
Improving Forecast Reliability for Geographically Distributed Photovoltaic Generations
Version 1
: Received: 3 October 2021 / Approved: 4 October 2021 / Online: 4 October 2021 (09:55:37 CEST)
A peer-reviewed article of this Preprint also exists.
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. 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.
Keywords
photovoltaic generation forecast; probabilistic forecast; prediction interval; ensemble forecast; day ahead forecasting; multiple PV forecasting
Subject
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.
Comments (0)
We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.
Leave a public commentSend a private comment to the author(s)
* All users must log in before leaving a comment