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...
详细信息
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.
Defining populations and identifying ecological and life-history characteristics affecting genetic structure is important for understanding species biology and hence, for managing threatened or endangered species or p...
详细信息
Defining populations and identifying ecological and life-history characteristics affecting genetic structure is important for understanding species biology and hence, for managing threatened or endangered species or populations. In this study, populations of the world's largest indigenous Atlantic salmon (Salmo salar) stock were first inferred using model-based clustering methods, following which life-history and habitat variables best predicting the genetic diversity of populations were identified. This study revealed that natal homing of Atlantic salmon within the Teno River system is accurate at least to the tributary level. Generally, defining populations by main tributaries was observed to be a reasonable approach in this large river system, whereas in the mainstem of the river, the number of inferred populations was fewer than the number of distinct sampling sites. Mainstem and headwater populations were genetically more diverse and less diverged, while each tributary fostered a distinct population with high genetic differentiation and lower genetic diversity. Population structure and variation in genetic diversity among populations were poorly explained by geographical distance. In contrast, age-structure, as estimated by the proportion of multisea-winter spawners, was the most predictive variable in explaining the variation in the genetic diversity of the populations. This observation, being in agreement with theoretical predictions, emphasizes the essence of large multisea-winter females in maintaining the genetic diversity of populations. In addition, the unique genetic diversity of populations, as estimated by private allele richness, was affected by the ease of accessibility of a site, with more difficult to access sites having lower unique genetic diversity. Our results show that despite this species' high capacity for migration, tributaries foster relatively closed populations with little gene flow which will be important to consider when developing ma
暂无评论