# Smart Grid Data Management in a Heterogeneous Environment with a Hybrid Load Forecasting Model
## 7 Conclusions  
The current grid system in developing countries still has many limitations, such as manual data collection, leading to data uncertainty. Moreover, the dataset obtained from MOELC could only access the governorate level in the distribution section. Currently, the cluster information is not available to the Iraqi MOELC. Our PIAS was presented to perform different roles, such as data acquisition, data federation, data management, data visualization, data analytics, and load forecasting. This system considers the potential use of smart meters in the future, where the cost will be very high without prior planning or preparing an advanced system to deal with the massive digital transformation and big data. Moreover, big data analytics has a prospective opportunity to solve many challenges, especially in developing countries. Meanwhile, the integration between data federation and big data analytics in smart grids (SG) could boost growth in the energy sector and reduce expenses, time, and effort, and enhance and develop knowledge in this sector. The PIAS is designed on top of a series of operations that preceded it, such as data quality of heterogeneous data collected from different sources. Different techniques based on real datasets were used to verify and validate the PIAS system. The first presented case study confirmed that the proposed system can be used to overcome challenges such as heterogeneous data acquisition and data federation. The second presented case study introduced a novel hybrid load forecasting model using fuzzy C-means clustering before applying ARIMA and the Gradient-Boosted Tree Learner model (FCM-ARIMA-GBTL). The proposed model improves the load forecasting performance in terms of Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) rather than when using the ARIMA or GBTL model \begin{table}
\begin{tabular}{c c c c} \hline \hline  &
\begin{tabular}{c} **Proposed Model** \\ **(ARIMA-GBTL)** \\ \end{tabular} & **ARIMA** & **GBTL** \\ \hline \hline Cluster 2 & & & \\ \hline MAPE & 0.725948449 & 2.22781236 & 2.675913816 \\ \hline MAE & 0.000863562 & 0.008580184 & 0.009404522 \\ \hline RMSE & 0.001135038 & 0.010676258 & 0.01142717 \\ \hline Cluster 3 & & & \\ \hline MAPE & 0.391079094 & 2.21031944 & 1.746144331 \\ \hline MAE & 0.000506608 & 0.009794827 & 0.010266868 \\ \hline RMSE & 0.000657717 & 0.011536968 & 0.012405158 \\ \hline Cluster 4 & & & \\ \hline MAPE & 0.319013042 & 2.019749556 & 6.070942807 \\ \hline MAE & 0.00073605 & 0.010264597 & 0.011093827 \\ \hline RMSE & 0.000970941 & 0.01241723 & 0.013213787 \\ \hline Cluster 5 & & & \\ \hline MAPE & 0.551668786 & 2.668732358 & 6.894689216 \\ \hline MAE & 0.000929249 & 0.008082002 & 0.008988616 \\ \hline RMSE & 0.001226214 & 0.009605795 & 0.010734888 \\ \hline Cluster 6 & & & \\ \hline MAPE & 0.412275984 & 1.925889028 & 2.076377338 \\ \hline MAE & 0.000967922 & 0.009308634 & 0.009529595 \\ \hline RMSE & 0.00125275 & 0.011249835 & 0.011664366 \\ \hline Cluster 7 & & & \\ \hline MAPE & 0.112522215 & 1.089237343 & 1.144672198 \\ \hline MAE & 0.00042932 & 0.005786312 & 0.005946305 \\ \hline RMSE & 0.000661391 & 0.008060203 & 0.008308765 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: _Cont._alone. Additionally, this study can be expanded in the future to override many other challenges such as reducing power loss and monitoring loss and thefts between generation, transportation, and distribution lines. Although maintaining simplicity is an advantage in a time-series dataset, it is also possible to perform many other artificial intelligence models such as Recurrent Neural Network (RNN), long short-term memory (LSTM), and Support Vector Machine (SVM). Conceptualization, R.N. ; Methodology, A.A., N.F.A. and A.A.-S.; Software, A.A.; Supervision, N.F.A. and A.A.-S.; Writing--review and editing, N.F.A., A.A.-S. and A.H.M. All authors have read and agreed to the published version of the manuscript.