Photovoltaic power forecasting:A Transformer based framework  

作　　者：Gabriele Piantadosi Sofia Dutto Antonio Galli Saverio De Vito Carlo Sansone Girolamo Di Francia 

机构地区：[1]ENEA CR-Portici,Energy Technologies and Renewable Sources Department,Portici,80055,Italy [2]University of Naples FedericoⅡ,Electrical Engineering and Information Technology Department,Napoli,80125,Italy

出　　处：《Energy and AI》2024年第4期445-458,共14页能源与人工智能(英文)

基　　金：funding from Ministero per lo Sviluppo nomico,Eco-Fondo per la Crescita Sostenibile,under the framework cordi"c-per l’innovazione di cui al D.M.31 Dicembre 2021 e DD 18 Marzo 2022",project MARTA,n.:F/310193/01-02/X56;It was also supported in part by the Piano Nazionale Ripresa silienza Re-(PNRR)Ministero dell’Universitàe della Ricerca(MUR)Project under Grant PE0000013-FAIR.

摘　　要：The accurate prediction of photovoltaic(PV)energy production is a crucial task to optimise the integration of solar energy into the power grid and maximise the benefit of renewable source trading in the energy market.This paper systematically and quantitatively analyses the literature by comparing different machine learning techniques and the impact of different meteorological forecast providers.The methodology consists of an irradiance model coupled with a meteorological provider;this combination removes the constraint of a local irradiance measurement.The result is a Transformer Neural Network architecture,trained and tested using OpenMeteo data,whose performance is superior to other combinations,providing a MAE of 1.22 kW(0.95%),and a MAPE of 2.21%.The implications of our study suggest that adopting a comprehensive approach,grating inte-local weather data,modelled irradiance,and PV plant configuration data,can significantly improve the accuracy of PV power forecasting,thus contributing to more effective technological and economic integration.

关 键 词：PHOTOVOLTAIC Forecasting Deep learning TRANSFORMERS 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]