**2. Database Presentation**

The database is a representative sample of both rural (7k households) and urban consumers (23k households). The database is a multiannual (2019–2021) recording of hourly energy use and is provided as Supplementary Material to this manuscript. Due to the volume of information to disseminate in this paper, we approach all the specifications and particularities of the database that are essential to this research. The urban households are located in cities in Bihor County, Romania, in the second climatological area, with an annual average temperature of 11.6 ◦C [15]. We present three charts specific to the yearly average urban database. Figure 2 shows the yearly consumption relative to 2020, the year for which we have the atypical consumption behavior that we targeted in our

STLF deployment. Figure 3 shows the weekday consumption and Figure 4 the seasonal profile consumption. For profiling reasons, Figure 2 presents only 2020 data, but Figures 3 and 4 statistically address all three years covered by the database. The rural households are located in Bihor County, Romania, in the third climatological area, with an annual average temperature of 9.6 ◦C [16]. In Figure 5 we can see a representative chart of the 2020 yearly consumption segmented into weekly loads starting on Sundays. Figures 6 and 7 show the statistics of the specific consumption of the rural household over weekdays and over each season.

Although weekly patterns are rare in nature, they are common in human activities, which is why we chose a 3D yearly chart; this chart contains essential information for classifying the consumption pattern.

The weekday pattern for urban residential consumers in Bihor County (RBCR) was relatively similar to the weekday patterns in Ireland, Hungary, Italy and UK, with the caveat that the household electric energy consumption was different [17]. With regard to the daily high peaks, the urban RBCR consumer was closer to the consumer profile from Hungary and Italy than that from Ireland and the UK [17].

**Figure 2.** Weekly load curve in 2020 for urban consumers.

**Figure 3.** Box and whiskers plots for days of the week in 2020 (urban).

**Figure 4.** Box and whiskers plots for hourly consumption in each season in 2020 (urban).

**Figure 5.** Weekly load curve in 2020 for rural consumers.

**Figure 6.** Box and whiskers plots for days of the week in 2020 (rural).

**Figure 7.** Box and whiskers plots for hourly consumption in each season in 2020 (rural).

In comparing the weekday consumption for rural and urban RBCR, the differences were due to the specific activities that take place in rural areas (Figure 8). While the urban consumption on weekdays (Monday to Friday) showed a bell shape, the rural chart showed a flattened reversed bell. While the most active days in terms of electric energy consumption seemed to take place on Wednesdays in the urban areas, in the rural ones the highest consumption was associated with Fridays [14].

**Figure 8.** Rural vs. urban weekly consumption patterns.

The weekend daily consumption pattern was relatively similar for urban and rural consumers in the regard that the consumption was lower on Saturday and higher on Sunday. The increase in Sunday consumption was bigger for the urban RBCRs. The same load profile was seen in all other countries' consumers that were analyzed [17] with a higher consumption on Sunday vs. Saturday [14].

A particularity of the rural RCBR is that it stands out of the large EU patterns identified in previous studies [17], with less consumption on Saturday relative to Friday. This particularity could be a good asset in forecasting and deploying power network resources.

With regard to the meteorological season's consumption pattern, we can see that the county is similar to the other EU countries covered so far in previous studies [17] with a good correlation with the day degree influence factor. The seasonal consumption pattern was more closely related to that of Italy and Hungary than that of Ireland and the UK [14].

The gap in summer consumption in seasonal analysis was steeper for the rural RBCR consumers (Figure 9). We associate this finding with a poor penetration of air-conditioning cooling devices in rural areas. In addition, in comparison to previous studies [17], we can see that this consumption was increased in the winter not due to electrical heating but mainly to the lower availability of natural light and the movement of activities indoors.

The differences in consumption patterns could be connected to energy-related education levels [18] and with poor market availability of smart meters and hourly billing for RBCRs.

In previous research [2] we found that identifying, analyzing and clustering consumer types can have a very good outcome in modeling and forecasting short-term and mediumterm power consumption. These findings have implications in assessing and developing commercial electric energy prices.

The database used in this study was extracted from a public national database [19].

### **3. Methodology**

Although most of the research on load forecasting is on advance forecasting techniques, decision-making revolves around classical forecasting methods: moving average, linear regression and multiple linear regression [20]. We assess three methods of forecasting for atypical consumption behavior: linear regression (LR), autoregressive integrated moving average (ARIMA) and Artificial Neural Network (ANN). Previous estimations performed with fuzzy forecasting methods gave high errors [21], so we did not include fuzzy forecasting in this study.

The first steps were digesting the database and preparing it for deeper understanding and ease of mathematical modeling. Influence factor databases were also added and filtered, including the weather database and the weekly and daily databases containing socio-cultural and economic activity milestones. The database was a fixed one, and none of the methods used was trained to update in real time with an expanding database.
