ARTICLE | doi:10.20944/preprints202110.0037.v1
Subject: Engineering, Electrical & Electronic Engineering Keywords: photovoltaic generation forecast; probabilistic forecast; prediction interval; ensemble forecast; day ahead forecasting; multiple PV forecasting
Online: 4 October 2021 (09:55:37 CEST)
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.
ARTICLE | doi:10.20944/preprints202202.0143.v1
Subject: Engineering, Electrical & Electronic Engineering Keywords: photovoltaic (PV) power forecast; multiple PV forecasting; short-term PV forecasting; motion estimation; optical flow; smart grid
Online: 10 February 2022 (02:22:32 CET)
The power-generation capacity of grid-connected photovoltaic (PV) power systems is increasing. As output power forecasting is required by electricity market participants and utility operators for the stable operation of power systems, several methods have been proposed using physical and statistical approaches for various time ranges. A short-term (30 min ahead) forecasting method has been previously proposed by our laboratory for geographically distributed PV systems using motion estimation. This study focuses on an important parameter for estimating the proposed motion and optimizing the parameter. This parameter is important because it is associated with the smoothness of the vector field, which is the result of motion estimation and influences the forecasting accuracy. In the periods with drastic power output changes, the evaluation was conducted on 101 PV systems located within a circle of 15-km radius in the Kanto region of Japan. The results indicate that the absolute mean error of the proposed method with the optimized parameter is 10.3%, whereas that of the persistent prediction method is 23.7%. Therefore, the proposed method is effective in forecasting for periods when PV output changes drastically in a short time.