# Off-Grid Power Plant Load Management System Applied in a Rural Area of Africa
## 3 System Overview
### Routine Analysis  
Our method is applied to manage the power usage of an off-grid power plant built in a rural village in Tanzania. This supplies the power usage of the whole area. After our practical investigation, the development of this village is relatively slow, and there is a large gap between the rich and the poor. Only a few families have basic amenities, such as lighting and speaker. Most families do not yet have electricity and lack a relatively basic understanding of electricity. Therefore, the supply to this village was powered by two sets of \(150\) W \(\times\) 32 ea solar panels, equivalent to a total power of 9.6 kW. The energy generated by the solar panels is stored in two sets of \(200\) ah \(\times\) 16 ea. batteries through two controllers. Figure 2a shows the five-day power plant load. According to our previous study, while the power usage data of a residential community can effectively reflect the living routine of residents, there are  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Advantages** & **Limitations** \\ \hline \hline \multirow{2}{*}{Regression model} & Anomaly detection and power usage prediction & Low prediction and detection accuracy \\ \cline{2-3}  & Supervised & High detection accuracy & High training cost \\ \cline{2-3}  & Unsupervised & No training cost & Weak interpretability \\ \cline{2-3}  & Semi-supervised & High detection accuracy and low training cost & The relationship between labeled data and unlabeled data need to be known \\ \hline \hline \multirow{2}{*}{This work} & Anomaly detection and future power usage prediction; \multicolumn{2}{c}{High detection accuracy} & Relatively high training cost \\ \cline{2-3}  & & & \\ \end{tabular}
\end{table}
Table 1: Summary of power consumption anomaly detection works.  
Figure 1: The framework for the proposed power management system.  
also tremendous irregular vibrations in the data series that reflect the diversity of residents' lives [5]. Routine analysis is proposed based on the features of the time series.  
The residential load series changes dynamically over time. To better analyze load series characteristics from the perspective of a stationary state, routine analysis first calculates the x-order difference of the target series and employs the augmented Dickey-Fuller (ADF) to test whether the transformed x-order series is stationary [18]. Generally, x increases from 1 [19].  
According to the ADF results, the first-order difference of the series (i.e., x = 1) is stationary, as shown in Figure 2b. Here, the period when the power usage of residents tends to be stable, is from 10 p.m. to 6 a.m., which can be considered a routine of residents in the village. During this period, most residents tend to sleep, so the effect of random events on power consumption is small. The anomaly detection rule in this period should be more rigorous than that in other periods. Therefore, in this study, routine analysis identifies this period as the non-vibration period. According to the results of routine analysis, the detection parameter is set to a variable value with time.