# Energy Demand Forecasting Scenarios for Buildings Using Six AI Models
## 3 Results and Discussions
### Actual vs. Prediction Energy (kWh/m\({}^{2}\))  
The LUCIA Building built as a Net Zero Energy Building (nZEB) is going to have an energy-efficient HVAC system, balancing energy consumption versus environmental impact requirements. The box plot in Figure 4a describes points ranging from very little up to very extensive. The building guarantees adequate indoor comfort levels at minimum energy usage. The variance in data indicates periods of increased demand, which may correspond to seasonal variations or occupancy levels. The figures show the positive value is heating and negative value is cooling. The box plot for HVAC demand shows a median close to 100 kWh/m\({}^{2}\) for actual demand, with predictions from various models (ANN, RF, XG, etc.) displaying significant variability, with some even reaching above 200 kWh/m\({}^{2}\). Outliers are present, indicating occasional extreme demands, but overall, the predictions tend to cluster around the actual values. In Figure 4b, the box plot represents the heating performance of FUTEM buildings. It demonstrates the performance of the heating system. It has clear implications on the ability of this heating system to maintain certain temperatures during cold months with very little fluctuation, pointing to its effectiveness at regulating temperatures. Most central tendencies of heating data reflect how good design can meet an occupant's needs of energy. It also handles the aspect of energy economy concern, whereby total energy costs are reduced as one part into the building's systems. In the heating demand box plot, the actual values are centered around 5 kWh/m\({}^{2}\), with most prediction models showing similar median values. The variability is low, indicating consistent heating demands, while outliers suggest occasional spikes that the models may struggle to predict accurately. In the EII building, the cooling system's performance is depicted through a box plot that illustrates temperature management during warmer months, as shown in Figure 4c. The data reveal a stable cooling output, ensuring a comfortable indoor environment despite external heat fluctuations. The range of values indicates periods of increased demand, likely to coincide with peak occupancy or extreme weather conditions. This cooling system is designed to efficiently utilize energy, contributing to the building's overall sustainability objectives and occupant satisfaction. The box plot for cooling demand indicates that actual values cluster around 55 kWh/m\({}^{2}\), with some outliers above 90 kWh/m\({}^{2}\). The heating performance of the EII building is similarly illustrated through a box plot in Figure 4d, showcasing its capability to maintain comfortable indoor temperatures during colder periods. The tight clustering of data points suggests effective thermal management, with the system responding well to changes in external conditions. The heating data reflect a commitment to energy efficiency, aligning with modern sustainability practices. This ensures that the building remainsa comfortable space for occupants while minimizing energy consumption and environmental impact. In the heating demand box plot, actual values are concentrated around 3 kWh/m\({}^{2}\). Figure 5a displays the probable scatter plot showing predicted vs. actual HVAC performance for the LUCIA building designed to work like a Nearly Zero Energy Building (nZEB). The close alignment of the points to the diagonal line indicates that the prediction models (like ANN, RF, etc.) are effective in estimating actual energy use, showing high accuracy for HVAC demand. The models nearest to the actual values for the LUCIA building HVAC (nZEB) are the ANN and RF models, as indicated by their points being closely aligned with the diagonal line in the scatter plot. In Figure 5b, the focus here is on the heating demand of FUHEM buildings.