Classification of Hourly Clearness Index of Solar Radiation in the District of Yamoussoukro

作     者：Siaman Paule Carine Yeboua Yao N’Goran Kouakou Konan 

作者机构：Laboratory of Electricity and Energy Conversion (ECEN) Polytechnic National Institute Felix Houphouet-Boigny (INP-HB) Yamoussoukro Cô te d’Ivoire Laboratory of Physics of Condensed Matter and Technology (LPMCT) UFR SSMT University Félix Houphouet-Boigny Abidjan Cô te d’Ivoire 

出 版 物：《Energy and Power Engineering》 (能源与动力工程（英文）)

年 卷 期：2019年第11卷第5期

页      面：220-231页

学科分类：1002[医学-临床医学] 100214[医学-肿瘤学] 10[医学] 

主　　题：Clearness Index Hierarchical Clustering Principal Component Analysis K-Means Method Classification 

摘      要：The exploitation of systems using solar energy as a source of energy is not fluctuations free because of short passage of clouds on solar radiation. The amplitude, the persistence and the frequency of these fluctuations should be analyzed with appropriate tools, instead of focusing on their location over time. The analysis of these fluctuations should use the instantaneous clearness index whose distribution is given as a first approximation which is independent not only of the season but also of the site. It is important to evaluate the potential solar energy in a region. Indeed such evaluation helps the decision-makers in their reflections on agricultural or photovoltaic solar projects. Then this study was conducted for a predictive purpose. The method used in our work combines the classification method which is the hierarchical ascending classification and two partitioning methods, the principal component?analysis and the K-means method. The partitioning method enabled to?achieve a number of well-known situations (in advance) that are representative of the day. The study was based on the data of a climatic weather station in the district of Yamoussoukro located in the center region of Côte d’Ivoire during the 2017 year. Using the clearness index, the study allowed the classification of the solar radiation in the region. Thus, it showed that only 346 days of the 365 days in 2017 were classified (95%). We identified three clusters of days, the cloudy sky (29%), the partly cloudy sky?(32%) and the clear sky (39%). The statistical tests used for the characterization?of these clusters will be detailed in a future study.