# Energy Demand Forecasting Scenarios for Buildings Using Six AI Models
## 1 Introduction
### Contributions  
This paper aims to create a reliable energy demand forecasting system for Spain by employing six advanced machine learning algorithms to identify the most effective model for predicting energy consumption patterns. To determine the most suitable approach for current demand trends, the study evaluates the performance of Artificial Neural Networks, Random Forest, XGBoost, Radial Basis Network, Autoencoder, and Decision Trees using historical energy data. The selected model will then be utilized to project energy demand for the next three years based on input data and will provide practical recommendations to energy providers regarding infrastructure planning and resource management. The main contributions of this paper can be summarized as follows:  
* The aim of research is to develop a reliable energy demand forecasting system for Spain. * Six advanced machine learning algorithms--Artificial Neural Networks, Random Forest, XGBoost, Radial Basis Network, Autoencoder, and Decision Trees--are evaluated. \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & \begin{tabular}{c} **Mathematical** \\ **Model** \\ \end{tabular} & **Purpose** & **Accuracy** \\ \hline [PERSON] and [PERSON] [9] & ANN & \begin{tabular}{c} To predict heating energy \\ requirements and \\ improve building \\ design efficiency \\ \end{tabular} & \begin{tabular}{c} The ANN model achieved a high prediction \\ accuracy with a deviation of 3.43\% and a success \\ rate of 94.8–98.5\% for estimating building \\ energy needs. \\ \end{tabular} \\ [PERSON] et al. [13] & Random Forest & \begin{tabular}{c} To enhance accuracy of \\ household energy \\ demand forecasts using \\ socio-economic and \\ meteorological data \\ \end{tabular} & \begin{tabular}{c} The Random Forest model in a Big Data \\ architecture achieves high-resolution energy \\ demand forecasts (weekly, daily, hourly) with \\ consistent accuracy, though prediction error \\ increases over longer time gaps. \\ \end{tabular} \\ [PERSON] et al. [17] & XGBoost & \begin{tabular}{c} To develop a unified \\ model for energy \\ consumption and peak \\ power demand \\ \end{tabular} & \begin{tabular}{c} The XGBoost \\ consumption and peak \\ and consumption data, outperforming \\ state-of-the-art methods. \\ \end{tabular} \\ Ghods and Kalantar [18] & \begin{tabular}{c} Radial Basis \\ Function Neural \\ Networks (RBFN) \\ \end{tabular} & \begin{tabular}{c} To forecast long-term \\ peak demand and \\ improve reliability of \\ load forecasts \\ \end{tabular} & \begin{tabular}{c} The RBFN model predicts Iran’s peak load growth \\ (37,138 MW to 45,749 MW, 2007-2011) with 5.35\% annual growth, driven primarily by \\ economic factors. \\ \end{tabular} \\ [PERSON] et al. [21] & Autoencoders & \begin{tabular}{c} To detect anomalies in \\ building energy data \\ without labeled data \\ \end{tabular} & \begin{tabular}{c} The Autoencoder-based ensemble method enables \\ unsupervised anomaly detection in building \\ energy data with interpretable scores (0–1), \\ identifying faults, inefficiencies, and atypical \\ events while reducing preprocessing needs \\ through robust feature extraction. \\ \end{tabular} \\ [PERSON] et al. [22] & Decision Trees & \
\begin{tabular}{c} To select the most \\ suitable forecasting \\ algorithm for \\ electricity consumption \\ \end{tabular} &
\begin{tabular}{c} This study optimizes 5 min building energy \\ forecasts by using Decision Trees to dynamically \\ switch between ANN and k-NN, achieving \\ near-100\% accuracy on weekdays (Mon-Fri) while \\ sensor data validate KNN as the preferred choice. \\ Decision Trees (RASE: 0.15) surpass ANN (0.17) and regression (0.18) in summer by prioritizing flat \\ size, household size, and AC use (59\% load), while winter models converge (RASE: 0.16–0.18) with \\ housing type and appliances as dominant factors. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}