**3. DHOMUS Platform**

Data Homes and Users (DHOMUS), is a platform developed by ENEA (https:// dhomus.smartenergycommunity.enea.it, accessed on 1 December 2022) that is dedicated to residential users for smart home applications. The layout of the smart home is depicted in Figure 1 [45]. The main objective of DHOMUS is to make users aware of their energy data, to let them understand how much energy they consume and why, to support reduction of both consumption and costs, thereby contributing to decrease their impact on the environment, to increase energy awareness, and to transform residential users into active subjects that contribute to the stability of the grid. The DHOMUS platform currently collects and analyses real-time data from smart homes located in Rome. They consist of 24 homes equipped with a kit of commercial sensors (their characteristics are described in Table 1), which monitor the electrical consumption of the home meter and of selected appliances, as well as the presence of people and indoor comfort. The management of all these devices is wireless, based on Z-Wave protocol, and the gateway is connected to the Internet through the energy box, which collects, integrates, and sends sensors' data to the cloud platform. As the electrical data acquisition time varies from sensor to sensor, postprocessing is performed to calculate their 15-min averages, similar to the method used for smart meters.

**Figure 1.** Smart home: representation of sensors.


**Table 1.** Sensors used in the experimentation.

#### *3.1. Datasets*

The model has been calibrated and validated based on two datasets of electrical consumption of homes and single appliances monitored by DHOMUS platform, in the following Dataset A and Dataset B. Dataset A includes energy measurements of 58 appliances in 14 homes over 2.2 years, collected with a 15-min granularity. The start and end times of the measurements are not the same for all dwellings, as reported in Table A1 in Appendix A. Figure 2 depicts the available data for the appliances included in Dataset A. For most of the appliances, the data are recorded over the period from February 2018 to March 2020. Fridges, dishwashers, washing machines, and TVs are the most commonly monitored appliances.

**Figure 2.** Percentage of available data for Dataset A.

Dataset B includes measurements of 48 appliances from 10 homes from June 2021 to November 2021 and data were collected with a 15-min granularity as reported in Table A2. Figure 3 shows the available data for the appliances in dataset B. Dishwashers and washing machines have the most data available.

**Figure 3.** Percentage of available data for Dataset B.

#### **4. Methodology**

### *4.1. Appliance Data Analysis Model*

A model has been implemented in the MATLAB® environment to analyse the data collected by the data acquisition system interacting with ENEA's DHOMUS platform. The model performs data mining and a statistical analysis of the energy use of the dwelling energy meter and of single appliances, distinguishing between those that operate with cycles (e.g., washing machine, dishwasher, tumble dryer) and those that operate continuously (e.g., air conditioners, TV, etc.). The model can detect the operation mode (standby, operating, off, monitoring sensor disconnected/not working) of all domestic appliances, as well as the start, end, and length of operation. Moreover, a statistical analysis of electricity consumption is also performed for each operation, determining the total consumption, the average energy consumption on the reference time step, which is equal to 15 min, and the maximum and minimum energy. Additionally, consumption is split into time slots set by the ARERA Authority [46], as well as customised time slots between 8 a.m. and 8 p.m. (with a minimum resolution of one hour). The model then carries out a monthly analysis of the operation and calculates the number of times the appliance is working, the duration of operation, and the total monthly energy, and then it compares these outputs with reference values, based on similar users or benchmarks derived from the technical literature, in order to make assessments on the actual use of the appliance. Additional outputs are calculated for domestic appliances that operate in a cyclic mode. The model is simple, fast, and it can distinguish nearby cycles in which one cycle runs immediately after another. It calculates the number of times the appliance has been used and the corresponding consumption; this information is used to provide households with feedback aimed at improving rational energy use. The algorithms are detailed as follows.

The flow chart in Figure 4 represents the structure of the numerical model. First, the algorithm imports monitoring data from .csv or .txt files exported from the data acquisition system. Data for various homes, different household appliances, and for an arbitrary time span could well be stored in the file. Input files have the following structure (taken from an example file), and the description of parameters is shown in Table 2:

```
home_id, sensor, date,sum_of_energy_of_power, delta_energy, last_value_switch
EnergyBox1, fridge plug,2019-01-01 00:30:00.0000,8.8,0,0
EnergyBox1, fridge plug,2019-01-01 00:45:00.0000,8.8,0,0
EnergyBox1, fridge plug,2019-01-01 01:00:00.0000,8.8,0,0
```
The quantities sum\_of\_energy\_of\_power and delta\_energy provide similar information on the consumption of the appliance; therefore, the model uses the former for the energy calculations.

Module Smart Home Analyzer (SHAM) (i.e., module 1 in Figure 4) imports the monitoring data in a table format. Then, it sequentially calls the modules explained below. The call syntax is as follows:

smart\_home\_analyser\_v2(B\_id, B\_name, elettrodom, energy\_data).

The required inputs are B\_id , the ID of the building which represents the energy box (e.g., EB-1 to EB-14 for dataset A, and NEB-1 to NEB-12 for dataset B), B\_name , the acronym of the building used to plot the results and save the results, elettrodom, the name of the appliance (e.g., "dishwasher"), and energy\_data, a table with the imported quarter-hour monitoring data. The code can recognize all possible appliances installed in the home.

**Table 2.** Input data for the model.


The module detects the dwelling and the appliance provided as input, and if it cannot find the combination "house, appliance", then it provides an error message and ends as shown in Figure 4.

The function "smart home" (module 2 in Figure 4) is automatically called if the dwelling and the appliance are found in the dataset. This function sorts the imported energy data over time and removes any duplicate; moreover, it verifies the unit of measurement of the energy quantities and, if necessary, converts from kWh to Wh. The module then produces a table-formatted array containing the input quantities sorted through time, checked, and with required units.

The function "appliances" (module 3 in Figure 4) automatically receives the following inputs from the previous function: B\_name is the acronym of the dwelling (e.g., "EB-1"), B\_appl and appl\_name are the abbreviation and label of the appliance, respectively, and they are used to plot and save the results, cyc\_on is a boolean variable that indicates whether the appliance has a cyclic operation or not, and t\_cdz\_i is the table array produced by the "smart home" function.

**Figure 4.** Flow chart of the numerical model.

Furthermore, the "appliance" module performs data analysis and data mining. First, it provides a set of operating features that are used to calculate the appliance standby energy and to setup customised daytime periods. Additional parameters are adopted for the study of individual cycles if the appliance has a cyclic operation:


These parameters depend on the type of appliance, and therefore they require calibration. The module creates a table with energy consumption and corresponding datetime, which starts on the first day of the first month and ends on the last day of the last month recorded in the imported dataset. This operation is useful to manage full months, especially

to account for new sensors and for relying on a comparable data structure because sensors may be connected to different appliances in different moments.
