*5.3. Comparative Analysis*

This section illustrates the comparison of appliances characterized by different energy classes. As described, the proposed algorithm is able to detect many features, and some of them have been selected in order to compare the performance of appliances: total number of cycles (recorded, extrapolated), percentage of consumption during peak hours, percentage of short cycles (recorded, extrapolated), percentage of long cycles (recorded, extrapolated), average energy consumption of short cycles and average energy consumption of long cycles.

Table 4 compares seven washing machines representing three energy classes (A, A++, and A+++) belonging to both datasets described in Section 3.1. Washing machines installed in homes EB-2 and NEB-4 are class A, those installed in homes EB-3, NEB-9, and NEB-10 are class A++, whereas homes EB-9 and NEB1 use washing machines of class A+++. Homes have a different number of residents, and they use the appliance in different manners. The model can determine the usage pattern of households in such different contexts. Furthermore, Table 4 presents the relevant quantities considered in the comparative analysis: the total number of cycles (*Tnθ*) that is subdivided into short and long cycles, the energy consumption during peak hours (PEC), and the percentage of short cycles (*Pθs*) and long cycles (*Pθ<sup>L</sup>* ). The washing machine installed in home EB-3 carried out the largest number of cycles (*Tnθ*) in both datasets, as confirmed by the energy consumption of both short cycles (*AECθs*) and long cycles (*AECθ<sup>L</sup>* ). As regards the energy consumption during peak hours, the washing machine in NEB-9 operated at 96% in this time slot, whereas the household of EB-2 showed a wiser behavior since he run his washing machine only for 28% of the time during peak hours. The percentage of short cycles for the washing machines of NEB-4 and NEB-1 is approximately the same even though the number of short cycles is 23 for the former and 52 for the latter, and the corresponding energy consumption during the analyzed period is 2.263 kWh and 3.264 kWh, respectively. Therefore, the average energy consumption in a single short cycle calculated for these appliances is consistent with their energy labels, i.e., class A and A+++ respectively.

**Table 4.** Comparative analysis on energy consumption for washing machines.


Similarly, the results obtained with seven dishwashers monitored in the two datasets are reported from Figures 13–15. Figure 13 depicts the total number of cycles (recorded and extrapolated) in both datasets. The model calculated the highest number of cycles for the dishwasher in EB-12, while the total number of cycles for dishwashers in EB-1, EB-9 and in NEB-1 are similar. Figure 14 illustrates the percentage of short and long cycles for the dishwashers. The highest number of long cycles occurred in EB-1 while dishwashers in NEB-1, NEB-4, NEB-9, and NEB-11 operated only short cycles. Figure 15 depicts the total energy consumption of long (orange bar) and short (green bar) cycles during the analyzed period. The dishwasher in EB-12 consumed more energy during long cycles. By comparing the usage of appliances with their energy labels, it can be seen that even though dishwasher in EB-9 run fewer cycles than the dishwasher in EB-12 (see Figure 13), they consumed a similar amount of energy (see Figure 15). This outcome is consistent with the energy class of those appliances, i.e., class D for the former and class A++ for the latter.

**Figure 13.** Energy recorded and extrapolated by the model for different dishwashers.

**Figure 14.** Percentage of short and long cycles for dishwashers.

**Figure 15.** Energy consumption in short and long cycles for dishwashers.

In order to test the model on other datasets found in the literature, we applied it on the GreenD Energy database [47], containing detailed information on energy consumption obtained through a measurement campaign in households in Austria and Italy (December 2013 to November 2014). The results show that our implemented algorithms can extract all patterns for the available appliances in the dataset, including disaggregation of energy consumption cycles occurred in close succession, which is one of the distinctive outcomes of the model, as shown in Figure 16.

**Figure 16.** Comparison of energy consumption cycles occurred in close succession for the washing machine.

#### *5.4. Feedbacks to the Consumer in DHOMUS Platform*

The implemented model has been integrated in the DHOMUS platform in order to provide users with feedback on the electrical consumption and to compare their energy habits and patterns with other users participating in the experimentation, and with benchmarks as well. The platform provides a web interface for user feedback to encourage their virtuous and conscious behaviours. Moreover, a monthly report presented in Figure 17 contains summary of the calculations carried out by model and some suggestions to promote energy saving. In particular, the monthly report shows the detailed results obtained from the model and related to consumption during peak hours and to the shares of short cycles ("cicli brevi" in Italian) and of long cycles ("cicli lunghi" in Italian). Furthermore, tips are provided to the users, e.g., those reported in Figure 18 for the washing machine (infographics are in Italian since the DHOMUS platform is native for Italian users), which shows a comparison with similar users. Moreover, tips are provided to assess the impact of virtuous behaviours, e.g., the reduction of the number of cycles by using the appliance at full load in order to reduce water consumption as well. On the other hand, Figure 18 shows the comparison of the average consumption with simular customers: washing machine, refrigerator, and dishwasher are compared in a bar graph on the left side of the illustration. The orange bar reflects the average consumption of other users, while the blue bar represents the average consumption of the specific user.

**Figure 17.** Feedback on the DHOMUS platform regarding the use of washing machine (in Italian).

**Figure 18.** Section of the DHOMUS dashboard reporting the user's consumption vs. average consumption of other similar users (in Italian).

#### **6. Conclusions**

In this paper, a method for the detection of energy-consumption patterns from household appliances data for a smart home platform is described. The model is quite simple, computationally not intensive, but effective in detecting patterns, such as the number of times the appliance is on, off, in standby mode, start time, end time, etc. The method can handle both cyclic and noncyclic appliances. For cyclic appliances, by using descriptive data-mining techniques, the model provides quantitative information on the total number of cycles and the disaggregation of cycles in close succession. Moreover, energy consumption computation over customized time periods and disaggregation of aggregated cycles is a distinguishing feature of the proposed model. The model has been applied on a set of appliances for training and for parameter calibration. Validation has been done with a different dataset, demonstrating that the model can efficiently detect energy consumption and usage patterns. Results from two cyclic appliances (washing machine, dishwasher) are presented to show how the model manages energy-consumption patterns, disaggregates independent washing cycles, and performs monthly statistics. Based on these findings, the implemented model has been integrated into DHOMUS, an IoT platform developed by ENEA, to offer users feedback on their consumption patterns and enable them to effectively compare their energy habits with those of similar customers.

The proposed model can be used with sufficient accuracy for all kinds of household appliances. However, the current release of the model can only handle batch processing data with 15-min granularity. Furthermore, in the case of high-frequency data sets (e.g., with 1-s data sampling), a frequency conversion algorithm is required before applying our developed algorithm, because the current version of the model automatically imports dataset where data have been already aggregated every 15 min by the IoT devices. Moreover, the current version of the model cannot be used in a real-time processing environment; the latter feature has been selected among future developments of the numerical code.

**Author Contributions:** Conceptualization, M.C. and S.R.; methodology, M.C.; software, M.C. and A.H.; validation, M.C. and A.H.; formal analysis, M.C.; investigation, A.H.; resources, S.R. and V.R.; data curation, S.R.; writing—original draft preparation, A.H.; writing—review and editing, M.C. and V.R.; visualization, S.R.; supervision, M.C.; project administration, S.R.; funding acquisition, S.R. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research and the APC was funded by the "Electrical System Research" Programme Agreement 2019–2021 between ENEA and the Italian Ministry of Ecological Transition, funding number I34I19005780001.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy restrictions.

**Acknowledgments:** We thank the anonymous reviewers for providing helpful feedback.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Abbreviations and Symbols**

The following abbreviations and symbols are used in this manuscript:


#### **Appendix A**






**Table A2.** Details of Dataset B.
