# Energy Demand Forecasting Scenarios for Buildings Using Six AI Models
## 1 Introduction
### Literature Review
[22] explored the use of Decision Trees to select the most suitable forecasting algorithm--either Artificial Neural Networks or K-Nearest Neighbors--for electricity consumption in an office building, based on five-minute data intervals. They evaluated the accuracy of these selections by analyzing various parameters, including sensor data. The findings indicate that Decision Tree depth influences selection accuracy, with K-Nearest Neighbors being favored during weekdays due to more reliable sensor data. Different modeling approaches for electricity energy use prediction were presented in this paper [23; 24; 25; 26; 27]. [PERSON] and [PERSON] [28] provided a comparison of three modeling techniques for electricity consumption forecasting in Hong Kong, including regression analysis, Decision Trees, and neural networks. The authors prove that both Decision Trees and neural networks represent feasible alternatives to the classical models of regression, especially in catching the energy consumption pattern over different seasons. Research showed that all generally comparable models yield slightly better performance when Decision Trees are applied during summer and neural networks in winter. The main factors influencing energy consumption found were household size, appliance ownership, and housing type. Considerable interest in energy management thus underlines the importance of various approaches to modeling. To forecast energy demand using linear regression, we must collect historical energy consumption data along with relevant predictors, fit a linear regression model to the training data and evaluate its performance using metrics like R-squared and Mean Squared Error and then use the model to make future predictions based on new input data [29; 30; 31; 32]. Table 1 summarizes the literature review on energy demand forecasting models. The research gap that was highlighted in the literature on energy demand forecasting identified several key issues. First, many models do not demonstrate real-time applicability, limiting their ability to respond immediately to changes in energy demand, which is necessary for operational HVAC management. Second, there is little consideration of different socio-economic variables--that will more broadly influence energy usage including HVAC usage and efficiency--in relation to energy demand forecasting. Third, we have not considered the potential advantage of simultaneously using multiple forecasting methods, which may improve the accuracy of forecasts, and produce a robust model that more accurately depicts energy demand and the complexities of the interactions between HVAC systems and energy demand. Closing these research gaps is significant for providing a more holistic approach, contributing to the development of more effective methodologies that can inform future energy planning and policy development and that can help to improve the performance of HVAC systems in the framework of contemporary energy management. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & \
\begin{tabular}{c} **Mathematical** \\ **Model** \\ \end{tabular} & **Purpose** & **Accuracy** \\ \hline \multirow{3}{*}{
\begin{tabular}{c} [PERSON] \\ and [PERSON] [8] \\ \end{tabular} } & \multirow{3}{*}{ANN, MLR} & To forecast residential energy demand up to 2030 based on & ANN and MLR models are at a similar level during the test period, though ANN shows sensitivity to recent economic fluctuations. \\  & & socioeconomic factors & ANN and MLR models are at a similar level during the test period, though ANN shows sensitivity to recent economic fluctuations. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of literature review.