零排放小区能源系统投资评价的聚类方法
Clustering Methods Assessment for Investment in Zero Emission Neighborhoods Energy System
摘要：
本文探讨了使用聚类的背景下，设计的能源系统的零排放社区（ZEN）。ZEN社区的目标是在其一生中实现净零排放。虽然以前的工作已经使用和研究集群设计的能源系统的街区，没有文章处理的街区，如ZEN，有很高的要求，太阳辐照度时间序列，包括一个CO2因子时间序列，并有一个零排放平衡限制的可能性。为此，使用了几种方法，并比较了它们的结果。结果一方面是聚类本身的性能，另一方面是使用数据的最优化模型中每种方法的性能。与聚类方法相关的各个方面进行了测试。研究的不同方面是：目标（聚类以获得天数或小时），算法（k-均值或k-中心点），归一化方法（基于标准差或值的值域）和启发式的使用。结果表明，k-means比k-medoids提供了更好的结果，并且k-means系统地低估了目标值，而k-medoids不断地高估了它。当可以在聚类天数和小时数之间进行选择时，聚类天数似乎提供了最佳的精度和求解时间。该选择取决于用于最优化模型的公式和对季节性存储建模的需要。归一化方法的选择影响最小，但值域方法在求解时间方面显示出一定的优势。当需要很好地表示太阳辐照度时间序列时，需要更高的天数或使用小时数。选择取决于什么样的解决时间是可以接受的。
英文摘要：
This paper investigates the use of clustering in the context of designing the energy system of Zero Emission Neighborhoods (ZEN). ZENs are neighborhoods who aim to have net zero emissions during their lifetime. While previous work has used and studied clustering for designing the energy system of neighborhoods, no article dealt with neighborhoods such as ZEN, which have high requirements for the solar irradiance time series, include a CO2 factor time series and have a zero emission balance limiting the possibilities. To this end several methods are used and their results compared. The results are on the one hand the performances of the clustering itself and on the other hand, the performances of each method in the optimization model where the data is used. Various aspects related to the clustering methods are tested. The different aspects studied are: the goal (clustering to obtain days or hours), the algorithm (k-means or k-medoids), the normalization method (based on the standard deviation or range of values) and the use of heuristic. The results highlight that k-means offers better results than k-medoids and that k-means was systematically underestimating the objective value while k-medoids was constantly overestimating it. When the choice between clustering days and hours is possible, it appears that clustering days offers the best precision and solving time. The choice depends on the formulation used for the optimization model and the need to model seasonal storage. The choice of the normalization method has the least impact, but the range of values method show some advantages in terms of solving time. When a good representation of the solar irradiance time series is needed, a higher number of days or using hours is necessary. The choice depends on what solving time is acceptable.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
