# Smart Grid Data Management in a Heterogeneous Environment with a Hybrid Load Forecasting Model
## 5 Results and Discussion (Case Study 1)
### Case Study 1: Data Management
#### 5.1.3 Data Visualization  
The PIAS visualization shows the data after they were analyzed and compared with the hypothesis to testify their validity [65]. Moreover, the application's visualization displays the result in graphs and tables that summarize the lengthy information in a compact form that is easy to be understood by the user. The information displayed includes the data about the climate of various geographic locations of Iraq. The climatic information consists of the data about rainfall, temperature, and humidity in various Iraq cities. The visualization dynamics enable the desired data to be retrieved by the user to perform a descriptive analysis [65]. Therefore, visualization is a critical feature that enables easy and fast analysis of huge power consumption and climatic data. The dynamic chart below displays how the data are presented by visualization in an intelligible format. Microsoft's Power BI tools have been used in the proposed model as interactive visualizations and business intelligence tools to represent the digital data after passing all the previous operations such as the organization and cleaning processes. Figure 8 shows the geographical distribution of different parameters such as population, distribution of rainfall, average maximum temperature, and the average reading value for different regions of Iraq. The general average was calculated and represented in a circle representing the different regions and governortates from the north, center, and south. The bigger the circle, the higher the rates (population, rainfall, etc.) are distributed on the interactive map. Figure 7: Update Account Information on PIAS. The temperature and the amount of rainfall that affect the energy consumption rate in different seasons can be extracted through the map, where the northern governorates record lower energy consumption than their central or southern counterparts, with a higher average temperature and a lower precipitation rate despite some northern governorates having a higher population. This can considerably help the Iraqi Ministry of Electricity to effectively supply and plan the power generation as well as give information on expected power consumption. Additionally, putting this information together gives a more in-depth insight into decision making in the energy sector. In the available dataset, the GPS information for each account is not provided. The map can be more dynamic and detailed if GPS information is available. Another approach to review and analyze the obtained data is the interactive view. A dynamic analysis of different cities is a highly significant feature of the proposed system, where the decision maker can view the analytical data for several different regions from one dynamic view. With dynamic analysis, the authority can view various climatic conditions and power consumption information simultaneously in graphs. It will allow them to analyze various regions' energy conditions accurately. Moreover, it will also enable the ministry to accurately analyze the energy demand while making an effective future plan to meet rising power demands. Figure 9 shows an example of a dynamic analysis of different Iraqi regions. The analysis of the data obtained in dynamic analysis supports the research hypotheses reviewed in a previous section; as shown by the dynamic interfaces below, the rate of energy consumption decreased when we are facing a decrease in temperatures and vice versa during different seasons of the year, and this is evident in the ninth-month readings. Although the reduction is minimally affected by the rate of consumption, it is expected to continue with the decrease in temperatures in the winter season. Figure 8: Distribution of Population Density, Average of Rainfall Intensity, An Average of Maximum Temperature, and Consumption Read Value Over Different Iraqi Region. To promote a deeper understanding of the strength of the data visualization and analyzed results, a dynamic analysis of three major cities of Iraq, Ninevch (Mosul City), Baghdad, and Basrah, was performed.