Weather Data Mixing Models for Day-Ahead PV Forecasting in Small-Scale PV Plants

Energies

ShreeKrishna Acharya¹  Jaehee Lee²  Young-Min Wi³ 

[1] Department of Electronics Engineering, Mokpo National University, Muan 58554, Korea;Department of Information and Electronic Engineering, Mokpo National University, Muan 58554, Korea;School of Electrical and Electronic Engineering, Gwanju University, Gwangju 61743, Korea;

关键词: small-scale PV forecasting;    weather data mixing model;    similar day detection (SDD);    long short-term memory (LSTM) network;   

DOI  :  10.3390/en14112998

来源: DOAJ

【 摘 要 】

As a large number of small-scale PV plants have been deployed in distribution systems, generation forecasting of such plants has recently been gaining interest. Because the PV power mainly depends on weather conditions, it is important to accurately collect weather data for relevant PV sites to enhance PV forecasting accuracy. However, small-scale PV plants do not often have their own measuring apparatus to get historical weather data, so they have used weather datasets from relatively nearby weather data centers (WDCs). Therefore, these small-scale PV plants have difficulty delivering robust and reliable forecasting accuracy because of inappropriate predicted weather data from a distance. In this paper, two weather data mixing models are proposed: (a) inverse distance weighting (IDW), and (b) inverse correlation weighting (ICW). These models acquire adequate mixed weather data for the day-ahead generation forecasting for small-scale PV plants. Furthermore, the mixed weather data are collected using the geographic distance between the PV site and WDCs, or correlation between the PV generation and weather variables from nearby WDCs. Interestingly, the proposed ICW model outperforms when WDCs are located distant from the PV plants, whereas IDW performs well with the closer WDCs. The forecasting performance of the proposed mixing models was compared with those of the existing weather data collection methods.

【 授权许可】

Unknown