Ricerc@Sapienza

The impacts of the ambient absolute and relative humidity on the performance of a photovoltaic (PV) solar module are investigated in details here. Using the experimental data recorded during a year as inputs, the artificial neural network is employed to develop models to predict voltage and current based on the effective parameters, including ambient temperature and relative humidity, as well as the wind velocity and irradiance, and having developed and validated the models, a comprehensive parametric study is conducted.

For the first time, the cluster analysis (k-means) has been applied on long time series of temperature and relative humidity measurements to identify the thermo-hygrometric features in a museum. Based on ASHRAE (2011) classification, 84% of time all rooms in the Napoleonic Museum in Rome (case study) were found in the class of control B. This result was obtained by analyzing all recorded data in 10 rooms of the museum as well as using the cluster aggregation.