# Energy Demand Forecasting Scenarios for Buildings Using Six AI Models
## 1 Introduction
### Literature Review  
[PERSON] and [PERSON] [8] developed predictive models for energy demand in the residential sector of the United States using Artificial Neural Networks (ANNs) and multiple linear regression (MLR). They sought to forecast future energy consumption trends up to 2030 based on various socio-economic factors. The study also compared the effectiveness of ANN and MLR models in capturing energy demand dynamics. It aimed to provide insights for energy planning and policy-making. [PERSON] and [PERSON] [9] studied the prediction of heating energy requirements for buildings using Artificial Neural Networks. This paper discussed key driving factors such as orientation, insulation thickness, and transparency ratio using a backpropagation neural network to depict energy demands. It addresses the precision of ANN forecasts compared to the traditional method and, at the same time, mentions that it has effectiveness in providing reliable energy consumption forecasts. It finally tries to outline the role of ANN in promoting energy efficiency in building design. ANNs demonstrate potential as an effective instrument to improve building design's energy efficiency [10; 11; 12]. [PERSON] et al. [13] developed a predictive model for household energy demand using the Random Forest algorithm within a Big Data framework. They sought to combine socioeconomic and meteorological data to enhance the accuracy of energy consumption forecasts at different temporal resolutions (weekly, daily, and hourly). The study emphasized the importance of socioeconomic factors in understanding consumer behavior and aimed to provide a scalable solution for real-time energy management in smart grid systems. Improving the accuracy of energy demand forecasts and, consequently, enabling resource conservation and well-informed energy planning promotes socioeconomic segmentation [14; 15; 16]. [PERSON] et al. [17] proposed the development of a unified long-term forecasting model for energy consumption and peak power demand utilizing the Sequential-XGBoost algorithm. This study addressed the complexities of conducting separate forecasts by integrating macroeconomic and climatic factors, thereby enhancing accuracy. The study also explored different sequential configurations to optimize predictions for both energy and peak demand. Ultimately, it aimed to improve power system planning and management through more effective forecasting methodologies. [PERSON] and [PERSON] [18] discussed the use of Radial Basis Function Neural Networks (RBFNs) for long-term peak demand forecasting in Iran, targeting predictions from 2007 to 2011. The study emphasized the impact of economic factors over weather conditions in long-term forecasting, highlighting the importance of variables like Gross Domestic Product (GDP), population, and electricity prices. The study demonstrated that RBFNs can provide more accurate predictions compared to traditional methods, with an average annual load growth rate of about 5.35%. Overall, it aimed to improve the reliability of load forecasts for better power system planning. Long-term forecasting is mostly influenced by economic factors,while short-term forecasting is predominantly influenced by weather occurrences [19; 20]. [PERSON] et al. [21] investigated the use of Autoencoders for unsupervised anomaly detection in building energy data, addressing the challenges of identifying operational faults without labeled data. They proposed an ensemble method combining various Autoencoder architectures and training schemes, emphasizing their ability to capture intrinsic characteristics of energy consumption patterns. The study evaluated performance through reconstruction residuals and the quality of high-level features extracted. The results indicate that Autoencoders can effectively identify anomalies, offering a data-driven solution for building energy management. [PERSON] et al.