**2. Materials and Methods**

In this paper, the data collection to investigate the energy consumptions in residential buildings was undertaken by means of a questionnaire survey. Data collection by means of the survey is related to:


The questionnaire was built in the Excel environment based on Visual Basic for Applications. Real-time simulation of energy consumptions came from entered inputs. The in-house code adopted in previous studies [39–41] was validated by means of TRNSYS and EnergyPlus results comparison. The Heat Balance Method (HBM) was the base of the model implemented with a conduction finite difference (CondFD) solution algorithm [42]. Italian adoption of EU regulation was used for the calculation of heating, cooling, and DHW production efficiencies [43,44] for an expeditious assessment of the input data of questionnaire. Solar energy plants and their thermal and electrical outputs were also studied with technical norms in force [45]. Energy consumption of appliances and devices installed in the dwellings was based on their energy label [46–51]. Referring to lighting, further information was collected on the installed lamps, occupancy profile, and their location in the dwelling.

The simulation results were checked by comparison with energy bill values entered by the users. Further data sources were the Terna (Italian TSO) report [52] and ISTAT (National Statistics Institute) survey on residential energy consumption [53]. Given that, a first subdivision of loads in flexible and rigid ones was made. Five categories were identified:


The main flexible loads in dwellings are "storable loads," i.e., heating, cooling, domestic hot water when equipped with battery or water tank and "shiftable loads," i.e., laundry, dishwasher, tumble dryer, vacuum cleaner, stove. The loads were identified by means of gathering surveys filled in for 419 dwellings from students of Faculty of Architecture at Sapienza University of Rome.

The yearly PEC primary energy consumptions equation in kWh/y reads as:

$$PEC = \sum\_{i} \sum\_{j} Q\_{i,j} \cdot f\_{\text{ren},j} + \sum\_{i} \sum\_{j} Q\_{i,j} \cdot f\_{\text{ren},j} \tag{1}$$

where:


The yearly emission *E* in kg/y reads as:

$$E = \sum\_{i} \sum\_{j} Q\_{i,j} \cdot f\_{\text{CO2},j} \tag{2}$$

where:


The considered KPIs were calculated on the base of number of occupants, building surface, and in accordance with the Italian building energy certification system [54]. As already mentioned, some of the typical indicators were not present due to the high correlation. However, for this reason it was easy to calculate its value by rearranging some of the other ones [55].

## *2.1. Building Envelope Retrofitting Measures*

Energy retrofitting of the building envelope has positive effects on the dwelling energy performance leading to a reduction of energy demand both in the winter heating season and in the summer cooling one. Conventional solutions are often the cost-effective ones even if crucial indicators such as levelized cost of energy [56] are not applied to them due to their large spread.

Considering the status quo, five alternative scenarios of building envelope energy retrofitting were simulated: (i) the insulation of vertical opaque walls, (ii) floors, (iii) ceilings, and (iv) the replacement of windows; first these interventions were considered individually and, then, (v) all together, as reported in Table 1. In all the cases it was assumed that, following the intervention, the values of the transmittance of the retrofitted building component were equal to those indicated later in Table 4 for the "after 2015" period. This parameter is essential for energy evaluation, while it does not provide information about other kinds of performances such as acoustics [57].


**Table 1.** Energy retrofitting measures for the building envelope.

A limitation of generalized extension and size of the retrofitting measures is due to the fact that several building typologies were surveyed and in some cases, such as an apartment, not all the surfaces can be retrofitted without involving the next apartment or the roof is actually not present if the apartment is located at ground floor. It entails a new share of available interventions as below:


## *2.2. Heating, Cooling, and DHW Systems Upgrading*

The energy upgrading of technological systems produces positive effects on the energy performance of the dwelling, leading to an increase in the average seasonal yields of the systems. They are dynamically computed according to UNI/TS 11300/2 [43] for heating systems production and their regulation efficiencies. While, for a heat pump and its A+++ version, the Coefficient Of Performance (COP) is calculated in compliance with [46]. Considering the status quo, five alternative scenarios for improving the efficiency of the heating, DHW, and cooling systems were simulated, as reported in Table 2. Three measures (#6, #7, #8) are the replacement of existing equipment by a more efficient one whereas two measures (#9, #10) involve the heat pump technology installation for heating and for the DHW preparation, with electrification of these services if they were gas-fired.


**Table 2.** Interventions of technological systems upgrading.

A first assessment was done accounting for the separated interventions, although in many of the examined dwellings, space heating and DHW rely on the same heat generator. Indeed, a limitation of the system upgrading is done by the presence of the already most efficient one or the absence of the one to be upgraded, such as the case of dwellings not equipped with cooling systems. In detail, among the total dwellings option #6 is applicable to 409 houses, i.e., 97.6%; option #7 to 414 houses, i.e., 98.8%; option #8 to 380 houses, i.e., 90.7%; option #9 to 411 homes, i.e., 98.1% and, finally, option #10 only to 204 homes, 48.7%. This latter one is actually the case of the dwellings equipped with cooling systems, therefore, suitable for upgrading.
