**5. Discussion**

As all the STLF literature states, a deep understanding of the consumers analyzed or of the database modeled is critical in forecasting under atypical consumer behavior [18,32] or under power load black swan events [26].

The analyzed database was composed of multiyear data from rural (7k households) and urban consumers (23k households) in Bihor County, Romania and we managed to identify the social, economic and weather influence factors. The database contained nonresidential consumers at levels that did not influence the residential patterns, including small and medium enterprises (SME).

During this study, we found large gaps between the unaltered STLF algorithm and algorithms adapted to atypical consumption or unforeseen events.

Previous studies [3–7] focused on algorithms for predicting outcomes with no historical database for a comparable situation. We tried to highlight the fact that for each unforeseen power load event or atypical behavior pattern, we should adapt the algorithm individually for each scenario. If for COVID-19 lockdown there was an easy fix, as the weekend historical consumption could be easily applied to all of the methods proposed, for other unforeseen events that cause atypical consumption behavior we need to identify scenarios and trends.

We plan to broaden our area of research by covering power load consumption unforeseen events at different layers of social, educational and economic levels and by analyzing different databases that encountered unforeseen events, e.g., the February 2021 Texas blackout, February 2014 Ukraine War, February 2020 market crash, 2021 global and European energy crisis, etc. As future research projects we aim to identify historical power load data that are related to unforeseen events and use them as a load forecast "library" for atypical consumption behavior as a cause of such events.

Regardless of the future conditions and state of the pandemic, its impact should be further studied in regard to the medium- and long-term load forecast (MTLF and LTLF), as COVID-19 more significantly affected other social and economic areas that have a relatively large impact on power consumption. Only working from home and online education, which prior to COVID-19 were merely exceptional cases, will become standard activities that might have a major impact on residential load consumption.

#### **6. Conclusions**

The three methods applied were deployed in a short period of time. The hardware configuration of the machine that generated the forecasts included 8 GB RAM, 500 GB SSD and an i5 Intel Processor. The LR and ARIMA methods were easy to use and adjust and could be modeled and tuned in Excel [14] (Microsoft Office 2019). For the ANN method R Statistics software's CRAN package was used. All analyzed methods output a good response to the influence factors with a retardation of one measurement or production data. We are certain that if a database of meteorological data collected from the immediate vicinity were available, we could increase the forecasting methods' accuracies by at least 0.5% (MAPE) for standard consumption data. The ANN method results, for this particular database, did not significantly improve after 100 epochs during the training process. The best results were obtained with seven hidden layers and 10 neurons in each hidden layer.

As we had available multiple metrics to evaluate our forecast, we used only MAPE because there was a significant difference between rural and urban household consumption (kWh) with an approximately 20% larger consumption in rural households.

The high reduction in MAPE of residential consumption on the first day of lockdown in Bihor County, Romania when the algorithm was adapted for the atypical consumption justifies the importance of this study; we had an overall MAPE = 4.0071% compared to the adapted algorithm MAPE = 1.4152%. This almost 3% difference in accuracy could have major economic effects on participants on the energy market. In [3] the results from the same country, but including a database that also contained commercial and industrial consumers, were relatively similar (MAPE) for the model adapted for pandemic effects, but our unadapted forecast method had a 2% to 3% higher MAPE. We conclude that residential consumers are highly sensitive to unforeseen events. We intend to focus our future research on a trigger algorithm that could automatically flag unforeseen events and atypical consumption behavior. This future focus may cover an algorithm for identifying an increase in information shared via social media related to energy consumption and its impact on the load curve, and use this to define the trigger to flag atypical or unforeseen power load consumption events. Also we intend to identify the influence of the level of education [33] and energy education in the power consumption response to atypical events.

**Supplementary Materials:** The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/en15010291/s1.

**Author Contributions:** Conceptualization, F.C.D. and C.H.; methodology, F.C.D. and C.S.; software, F.C.D.; validation, C.H. and G.B.; formal analysis, C.S.; investigation, F.C.D.; resources, C.H.; data curation, F.C.D.; writing—original draft preparation, F.C.D.; visualization, C.H., G.B. and C.S.; supervision, C.H.; funding acquisition, C.H. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** Not applicable.

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