# Energy Demand Forecasting Scenarios for Buildings Using Six AI Models
## 4 Conclusions  
The evaluation of the LUCIA building was based on a nzEB Building concept; as a consequence, its HVAC system had to be highly evolved in balancing energy consumption and environmental impacts with indoor comfort. The box plots present the median HVAC demand close to 100 kWh/m\({}^{2}\). Predictions made by different AI models demonstrate high values, with some even over 200 kWh/m\({}^{2}\). This deviation shows that the models generally agreed with the real values, yet there are further refinements needed for extreme demand. The performance of the FUMEM building heating system is highlighted given the stable temperatures it achieves during colder months with minimal fluctuation, showing effective design and energy economy. The heating demand boxplot gives an actual value of about 5 kWh/m\({}^{2}\) with a low variability forecast, reinforcing how reliable the system is. The EII building also shows strong performance metrics for its cooling and heating systems, with box plots indicating that the outputs are quite stable even in fluctuating temperatures. Analysis through scatter plots of predicted versus actual performances of HVACs for all buildings further reinforces the efficacy of AI models--the ANN and Autoencoder models--especially in the forecast of energy demands. These forecast figures are expected to be achieved within the next three years, peaking in colder and warmer months of heating and cooling, respectively. This underlines the importance of an accurate energy management strategy; hence, advanced modeling techniques have become highly relevant in optimizing HVAC, heating and cooling performance in modern buildings with sustainability goals. Maximum peak energy demand forecasts for a three-year horizon highlight diverse building needs. For the Nearly Zero Energy Building (nZEB) LUCIA, heating demand is projected to peak around kWh/m\({}^{2}\), while cooling is expected to reach 300 kWh/m\({}^{2}\). The FUMEM building's heating consumption is anticipated to peak at approximately 70 kWh/m\({}^{2}\). The EII Building's forecasts indicate cooling demands peaking near 70 kWh/m\({}^{2}\) and heating demands reaching significant peaks close to 120 kWh/m\({}^{2}\). These projections underscore the varying energy requirements across different building types and seasons. Energy demand models presently can capture trends related to various geometries and a range of weather data, but they have issues accurately modeling extremes, especially concerning prediction deviations in buildings, which suggest that they need to be improved or that the data were not accurate enough to account for peaks. Going forward, more robust models should include real-time weather forecasts and detailed occupant behavior modeling to improve responsive demand modeling and predictive models. The models can also be improved with datasets capturing internal heat gains, real-time HVAC sensor data, and energy price data for a more robust energy management solution. Conceptualization, K.M.S., A.O.E., F.J.R.-M. and J.M.R.-H.; methodology, K.M.S., A.O.E., F.J.R.-M. and J.M.R.-H.; software, K.M.S., A.O.E., F.J.R.-M. and J.M.R.-H.; validation, K.M.S. ; formal analysis, K.M.S., A.O.E., F.J.R.-M. and J.M.R.-H.; investigation, K.M.S., A.O.E., F.J.R.-M. and J.M.R.-H.; resources, K.M.S., A.O.E., [PERSON] and [PERSON]; data curation K.M.S., A.O.E., [PERSON] and [PERSON]; writing--original draft preparation, K.M.S. ; writing--review and editing, K.M.S., A.O.E., [PERSON] and [PERSON]; visualization, K.M.S., A.O.E., F.J.R. [PERSON] and [PERSON]; supervision, A.O.E. and [PERSON]H.; project administration, [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript. This research received no external funding. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data presented in this study are available on request from the corresponding author.