## 2 Literature Review
### Application of Neural Networks (NNs) for Predicting Energy Consumption  
Due to the advancement of machine learning and information technologies, novel computer application tools have recently advanced in construction and building management. In different fields, artificial neural networks (ANNs) have received attention as one of the more attractive utilized tools for developing prediction models [10; 15]. The accuracy of the anticipated energy consumption is an important consideration with any method of estimation [17]. Lately, an alternative tool for predicting consumption of energy was established by using a new type of tools, namely, artificial neural networks (ANNs), which evolved based on artificial intelligence and has high potential to be used in the construction field, as recent research demonstrated [18], and it is anticipated to have a greater performance compared to the traditional regression analysis [15]. A literature review suggested that neural networks have been intensively adopted in the development of forecasting energy consumption models (see, e.g., References [16; 17; 19; 20; 21; 22], as it can provide more accurate results compared with regression and simulation-based models [15]. [PERSON] et al. [17] introduced a prediction model for predicting the electricity demand of CDGI-ARFRISOL-CIESOL Bioclimatic Buildings using neural network. The paper is one of few papers that adopts the use of an NN for developing an energy consumption model for short-period forecasts. Similarly, [PERSON] et al. [16] developed an ANN-based prediction model for the consumption ofenergy for electricity in Lebanon by considering only the weather conditions. In China, [PERSON] et al. [23] introduced a model to calculate the increasing trend in energy consumption. Another application of ANN is the prediction of the heat load and the total carbon emissions (see, e.g., [PERSON] et al. [19]). To enhance their ANN performance, [PERSON] et al. [20] proposed the incorporation of ANN with statistical processes, information criteria, cross-validation, and a backpropagation algorithm in their ANN to improve the accuracy at predicting building energy consumption. [PERSON] et al. [24] proposed the amalgamation of stacked autoencoders (SAEs) and an extreme learning machine for developing a prediction model for energy consumption. The model provided better backward propagation neural network (BPNN) and vector-regression-based models. [PERSON] et al. [25] investigated the main causes of the discrepancy between the actual performance of constructed buildings and the measured performance at the design stage. The study revealed that unrealistic inputs regarding user behavior and building management in building energy models are the main causes of such discrepancies. In Saudi Arabia, few studies are reported in the literature for predicting energy consumption and/or its associated cost. [PERSON] and [PERSON] [10] introduced a novel conceptual system to predict the cost of energy in residential buildings in Saudi Arabia. The proposed system is a combination of four models: a building information module (BIM), an ANN, a graphical user interface (GUI), and a database model (DBM). The system was applied to 25 houses, 28 apartments, and 62 villas using 10 input variables and one output, which was the predicted energy cost. The system succeeded in achieving a 78% accuracy for predicting the energy cost. The authors recommended that future studies include roof designs and building materials. Furthermore, the authors mentioned that one of the difficulties they faced was the lack of completed design information and specifications for residential buildings in Saudi Arabia. [PERSON] et al. [15] introduced a prediction model for electricity consumption in Saudi Arabia. The only factors or elements accounted for was the weather conditions and economic indices. The model predicted the consumption at a national level and did not consider the energy consumption at a building facility level, which is more complex due to the involvement of many factors.