Study on Land Use Regionalization in Henan Province

According to the natural ecology and socio-economic conditions in Henan Province, a land use regionalization index system with 6 factors and 24 factor layers was constructed by combining with the characteristics of land use in Henan Province. Expert scoring method was used to determine the weights of the indicators. Based on the similarities and differences of these factors in the index system at county （city, district） levels, hierarchical clustering method was used to make the quantitative analysis to the land use regionalization in Henan Province. And constrastive analysis and qualitative analysis were made to the regionalization scheme by combining with the acutal conditions in the counties （cities, districts）, and finally, Henan Province was classified into 6 regions.

Mathematical Tools of Cluster Analysis

The paper deals with cluster analysis and comparison of clustering methods. Cluster analysis belongs to multivariate statistical methods. Cluster analysis is defined as general logical technique, procedure, which allows clustering variable objects into groups-clusters on the basis of similarity or dissimilarity. Cluster analysis involves computational procedures, of which purpose is to reduce a set of data on several relatively homogenous groups-clusters, while the condition of reduction is maximal and simultaneously minimal similarity of clusters. Similarity of objects is studied by the degree of similarity (correlation coefficient and association coefficient) or the degree of dissimilarity-degree of distance (distance coefficient). Methods of cluster analysis are on the basis of clustering classified as hierarchical or non-hierarchical methods.

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

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 mix.From a long-term perspective,the current non-storable capability of renewable energy sources requires an adequate uncertainty characterization over the years.In 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 Brazil.Leveraging 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 source.The 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 irradiance.We 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.