With the aim of investigating the effect of climate on the electricity use for space heating and cooling, the correlation between the daily electricity consumption of the city of Milan (Italy) in the five-year period 2013–2017 and the daily average outdoor dry bulb temperature of the same location was analyzed by means of statistical tools. To filter out the effect of the variables different from weather the analysis was limited to workdays. Electricity use and outdoor temperature were correlated by using a parametric model, within a parameter estimation approach, in order to highlight the relevant physical phenomena and to identify the value of the building stock characteristic parameters. A modified five-parameter model (M5PM) was proposed, based on the second principle of thermodynamics, which accounts for the effect of the past temperature by adopting an effective temperature approach. The comparison between the actual data for the workdays of the whole 5-year period and the electricity consumption obtained by means of the M5PM revealed a good agreement between the two distributions, as confirmed by the value of the coefficient of determination (R2 = 0.93), by the value of the normalized root mean square error (NRMSE = 2.3%) and by the value of the mean absolute percentage error (MAPE = 1.3%). The regression model was then applied to the electricity use analysis of the individual days of the week. By increasing the number of data set and their disaggregation, the approach based on regressing the energy consumption historical data series could be successfully adopted for applications that are currently approached by direct simulation or by measurement. In this perspective big data analytics in combination with the parameter estimation approach remains a promising tool to facilitate the interpretability of the energy use model.
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