A visitors’ presence model for a museum environment: Description and validation

The correct simulation of energy needs for heating, ventilation, and air conditioning of the rooms of a museum is strictly correlated to the estimation of internal loads deriving from the presence of visitors, who are responsible for both sensible and latent gains. However, visitors’ profiles on short time scales (e.g., 15 minutes) in museum rooms are often not available data. In this work, we present a correlation between the statistical distribution of visitors’ presence in the main exhibition rooms of a museum and the profile of sold tickets, which is usually readily accessible. We apply the procedure to a real museum, using the results of a monitoring campaign for the best-fitting of the correlation curves and for the validation of the model. The fitting curves are the superposition of three Gaussian distributions, where daily and weekly peaks are taken into account. Each distribution refers to a period of the day (late morning/early afternoon, afternoon, and late afternoon), and has a standard deviation obtained by a best-fit procedure, and an integral corresponding to the sum of the sold tickets in its specific period of the day. The model is validated comparing monitored data of temperature and specific humidity in the rooms with the results of a dynamic simulation, run on TRNSYS 17, using the modeled correlation. By means of this validated model, energy-saving measures on the examined museum can be reliably simulated. At the same time, it is reasonable to assume that the same method could be applicable to other buildings with similar occupancy (e.g., galleries, exhibitions).