Numerical modelling of the thermal energy demand in Italian households through statistical data

Numerical modelling of the thermal energy demand in Italian households through statistical data

Matteo CalderaGiovanni Puglisi Fabio Zanghirella Paola Ungaro Giuliano Cammarata 

ENEA (Italian National Agency for New Technologies, Energy and Sustainable Economic Development), via Anguillarese 301, 00123 Roma, Italy

ISTAT (Italian National Institute of Statistics), viale Oceano Pacifico 171, 00144 Roma, Italy

Università degli Studi di Catania, piazza Università 2, 95125 Catania, Italy

Corresponding Author Email:
13 October 2017
10 April 2018
30 June 2018
| Citation



The availability of reliable and up-to-date data on energy uses and consumption is a key aspect in order to achieve the goals set out by European Directives on energy efficiency, and to monitor the effectiveness of energy policies supporting buildings’ retrofit actions. In such a context, a numerical model, implemented in Excel® and Matlab®, was developed in order to determine the energy consumption for space heating, domestic hot water and cooking in households, based on a statistical dataset of 20,000 records collected in a survey on the energy consumption of Italian families carried out by the National Institute of Statistics (ISTAT). The space heating model is based on the definition of classes of dwelling-types, while the energy use for DHW and cooking are calculated on a record-by-record basis according to a Standard-based approach. The present paper presents the results of a refined version of the model, in particular the calibration of relevant parameters accounted for the secondary equipment for space heating, resulting in improvements in the reliability for the allocation of the fuel consumption among the end-uses. The refinement and validation of the model are still in progress, since they are functional for the assessment of the energy consumption of households in the period between two subsequent surveys.


energy consumption, households, numerical model, statistical survey

1. Introduction
2. Methodology
3. Results and Discussion
4. Conclusions

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