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.

中文翻译：

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零排放社区能源系统投资的聚类方法评估

本文研究了在设计零排放社区 (ZEN) 的能源系统的背景下聚类的使用。ZEN 是旨在在其一生中实现净零排放的社区。虽然之前的工作已经使用和研究了聚类来设计社区的能源系统，但没有文章涉及像 ZEN 这样对太阳辐照度时间序列有很高要求的社区，包括 CO2 因子时间序列，并且零排放平衡限制了可能性。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优化模型中每种方法的性能。测试了与聚类方法相关的各个方面。研究的不同方面是：目标（聚类以获得天数或小时数）、算法（k-means 或 k-medoids）、归一化方法（基于标准偏差或值范围）和启发式的使用。结果强调，k-means 比 k-medoids 提供更好的结果，并且 k-means 系统地低估了目标值，而 k-medoids 不断高估它。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精度和求解时间。选择取决于用于优化模型的公式以及对季节性存储建模的需要。归一化方法的选择影响最小，但取值范围方法在求解时间方面显示出一些优势。当需要很好地表示太阳辐照度时间序列时，需要更多的天数或使用小时数。选择取决于可接受的求解时间。