# Fault Prediction and Reconfiguration Optimization in Smart Grids: AI-Driven Approach
## 3 Data Source Search, Analysis, Purge, and Selection
### SustDataED--Dataset Analysis  
The SustDataED dataset [36] was created as part of the SINAIS research project, aimed at providing ecological feedback to users to promote sustainable energy behavior and greater use of renewable energy sources. This dataset encompasses five years of energy consumption and production data from 50 households in the city of Funchal (Madeira, Portugal), divided into three different deployments. This implies there are no measurements from 50 households simultaneously throughout the five years of the project but separately in three groups.  
Figure 1 depicts the three aforementioned deployments. Since we were working with consumption and generation measurements that were partially correlated with weather and temporal conditions, we selected the time period with the highest number of households deployed simultaneously. In our case, we chose the first deployment, in which measurements from up to 23 households were available simultaneously.  
The data available in SustDataED are quite varied. Table 2 lists the different types of measurements included in the dataset. However, not all of them were relevant for this study. For instance, a high level of granularity, such as power event measurements or user event  
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**Name** & **Location** & **Residences** & **Period (Days)** & **Resolution** & **Parameters** \\ \hline _AMPds2_[28] & Vancouver & \multirow{2}{*}{1} & \multirow{2}{*}{730} & \multirow{2}{*}{60 s} & \multirow{2}{*}{I, V, P, S, F, pf} \\  & (Canada) & & & & \\ \hline _BLUED_[29] & Pittsburgh & \multirow{2}{*}{1} & \multirow{2}{*}{8} & \multirow{2}{*}{83.33 μs} & I, V, switch \\  & (USA) & & & & events \\ \hline _ECO_[30] & Switzerland & 6 & 244 & 1 s & P \\ \hline _GREEND_[31] & Italy and Austria & \multirow{2}{*}{9} & \multirow{2}{*}{310} & \multirow{2}{*}{1 s} & \multirow{2}{*}{P} \\ \cline{1-1} _HUE_[32] & British & & & & \\ \cline{1-1} _HUE_[32] & Columbia, Canada & & & & \\ \hline _iAWE_[33] & New Delhi & \multirow{2}{*}{1} & \multirow{2}{*}{73} & \multirow{2}{*}{1 s} & \multirow{2}{*}{V, I, P, S} \\  & (India) & & & & \\ \hline _REDD_[34] & Boston (USA) & 6 & 119 & 66.66 μs & I, V, P \\ \hline _Smart_*[35] & Massachusetts & \multirow{2}{*}{3} & \multirow{2}{*}{90} & \multirow{2}{*}{60 s} & \multirow{2}{*}{P, S, V, I} \\  & (USA) & & & & \\ \hline _SustDataED_ & Madeira & \multirow{2}{*}{50} & \multirow{2}{*}{1144} & \multirow{2}{*}{60 s} & \multirow{2}{*}{I, V, P, Q, S} \\  & (Portugal) & & & & \\ \hline _UK-DALE_[37] & UK & 5 & 499 & 62.5 μs & P, switch state \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of public power consumption datasets.  
measurements, was not required for our analysis. As shown in Table 2, the measurements related to energy production were uncertain. At first glance, SustDataED appeared suitable because it provided real photovoltaic production measurements. However, these data were reported as aggregate values and pertained to a photovoltaic system that supplied all the households, with no information about the system's dimensions, as they were not specified. It was also unclear whether the size of the photovoltaic system increased over time, making household-level analysis more complex.