Identifying representative days of solar irradiance and wind speed in Brazil using machine learning techniques

作     者：Rafaela Ribeiro Bruno Fanzeres 

作者机构：Industrial Engineering DepartmentPontifical Catholic University of Rio de Janeiro(PUC-Rio)22451-900Rio de JaneiroRJBrazil 

出 版 物：《Energy and AI》 (能源与人工智能（英文）)

年 卷 期：2024年第15卷第1期

页      面：151-170页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 070601[理学-气象学] 081104[工学-模式识别与智能系统] 08[工学] 0706[理学-大气科学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, CAPES, (PD-10381-0322/2022) Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq, (133806/2021-9, 309064/2021-0) Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, FAPERJ, (E-26/202.825/2019) 

主　　题：Partitioning clustering methods Hierarchical clustering methods Model-based clustering methods Representative days Solar irradiance Wind speed 

摘      要：The investment levels in electricity production capacity from variable Renewable Energy Sources have substantially grown in Brazil over the last decades,following the worldwide-seeking-goal of a carbon-neutral economy and the country’s incentives in diversifying its generation *** a long-term perspective,the current non-storable capability of renewable energy sources requires an adequate uncertainty characterization over the *** this context,the main objective of this work is to provide a thorough descriptive analytics of the time-linked hourly-based daily dynamics of wind speed and solar irradiance in the main resourceful regions of *** on unsupervised Machine Learning methods,we focus on identifying similar days over the years,Representative Days,that can depict the fundamental underlying behaviour of each *** analysis is based on a historical dataset of different sites with the highest potential and installed capacity of each source spread over the country:three in the Northeast and one in the South Regions,for wind speed;and three in the Northeast and one in the Southeast Regions,for solar *** use two Partitioning Methods(𝐾-Means and𝐾-Medoids),the Hierarchical Ward’s Method,and a Model-Based Method(Self-Organizing Maps).We identified that wind speed and solar irradiance can be effectively represented,respectively,by only two representative days,and two or three days,depending on the region and method(segments data with respect to the intensity of each source).Analysis with higher Representative Days highlighted important hidden patterns such as different wind speed modulations and solar irradiance peak-hours along the days.