A model for the evaluation of thermal and electric energy consumptions in residential buildings: The case study in Torino (Italy)

This work analyzes the thermal and electrical consumptions of residential buildings located in the town of Turin (northwest Italy). The aim is to create mathematical models to evaluate monthly behavior of energy consumption as a function of two predictable variables. In the evaluation of the different variables, two climatic data were chosen: the monthly average temperature and the monthly average daily solar irradiation. Two multiple linear regression models have been determined, one using standardized variables to evaluate how climatic data influence thermal and electric energy consumptions in residential buildings. The fields of application of these models can be: the evaluation of monthly energy-use data knowing the annual value with the construction of seasonal trends, the prediction of monthly energy-use data knowing the weather forecast, the management of the buildings' energy consumptions to optimize the heating or electric networks for more future expansion in urban contests. After an initial state of the art review, the case study of Torino is presented, with more than 2700 users for the electric consumptions and 130 residential condominiums. A selection of the users was made to consider an homogeneous set of users and finally the models were developed with about 1200 electric users and 100 condominiums. The results show good correlations between energy consumption and climatic data, especially with the air temperature. For electrical consumption the monthly variation is limited compared with thermal energy-use, for both the seasonal patterns obtained have a deviation of less than 5% between calculated and real consumptions.

[1]  R. Rajagopal,et al.  Determinants of residential electricity consumption: Using smart meter data to examine the effect of climate, building characteristics, appliance stock, and occupants' behavior , 2013 .

[2]  Wontae Kim,et al.  Effect of length of measurement period on accuracy of predicted annual heating energy consumption of buildings , 2004 .

[3]  V. Jain,et al.  Modelling of electrical energy consumption in Delhi , 1999 .

[4]  Joseph Virgone,et al.  Development and validation of regression models to predict monthly heating demand for residential buildings , 2008 .

[5]  Yuk Yee Yan.  Climate and residential electricity consumption in Hong Kong , 1998 .

[6]  Jean-Louis Scartezzini,et al.  A simplified correlation method accounting for heating and cooling loads in energy-efficient buildings , 1998 .

[7]  P. Bodger,et al.  Forecasting electricity consumption in New Zealand using economic and demographic variables , 2005 .

[8]  V. Bianco,et al.  Electricity consumption forecasting in Italy using linear regression models , 2009 .

[9]  Neng Zhu,et al.  Multiple regression models for energy consumption of office buildings in different climates in China , 2013 .

[10]  D. Claridge,et al.  A Fourier Series Model to Predict Hourly Heating and Cooling Energy Use in Commercial Buildings With Outdoor Temperature as the Only Weather Variable , 1999 .

[11]  S. Beck,et al.  Using regression analysis to predict the future energy consumption of a supermarket in the UK , 2014 .

[12]  Roberto Lamberts,et al.  Development of envelope efficiency labels for commercial buildings: Effect of different variables on electricity consumption , 2008 .

[13]  David E. Claridge,et al.  Modeling Hourly Energy Use in Commercial Buildings With Fourier Series Functional Forms , 1998 .