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.Comment: 12 pages, 19 figures, 7 tables, 1 Appendix
