## 4 Model Development  
\"Neuraldesigner\" neural network software [31] was utilized for the development of the prediction model for the consumption of the energy in school facilities in Eastern Province in Saudi Arabia. An ANN is considered an alternative approach to the traditional regression analysis [16]. This technique has a high potential to be used in the construction field in the future, as recent research has demonstrated [18]. A neural network uses multiple processors working at the same time in parallel to get a result (in a way that simulates human neurons). It consists of three layers, where each layer consists of nodes or neurons and these nodes are highly interconnected with the nodes in the previous layer and the node in the next layer, and each of these nodes has its own knowledge, including rules that it has learned by itself.  
For developing the energy prediction model, a systematic search was applied to select the best NN architecture that represents the problem of predicting energy consumption from many different network models with different structures. As presented in Figure 1, the systematic search was the process of redoing the building process of the NN until satisfactory results were achieved. The factors influencing the energy consumption of school buildings were defined as \"input data\" (model-independent variables), while the total energy consumption was defined as \"output variables\" (model-dependent variables). Three main steps were required to build the proposed energy prediction model in \"Neuraldesigner.\" They were the preparation of the required data, the machine learning, and then the production of the model, as presented in Figure 2. The process of the model development is described in detail, which can be summarized as follows:  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Identified Factor** & **Action** \\ \hline Location & Considered in the study \\ Age of building & Considered in the study \\ Building envelope system & Not considered since it is common in all schools \\ Number of occupants & Considered in the study (see below) \\ Area of building & Considered in the study \\ Glazing systems & Not considered since it is common in all schools \\ Thermal insulation roofs and walls & Not considered since it is common in all schools \\ Energy-efficient lighting & Not considered since it is common in all schools \\ Proper shading & Not considered since schools are not surrounded by other buildings \\ Weather conditions & Not considered since the study focused only on Eastern Province \\  & which has the same climate zone throughout \\ Air-conditioning systems & Considered in the study \\ Cooking appliances & Not considered \\
**Factors Suggested by the School Board and Not Included Above** \\ Number of floors & Considered in the study \\ Total air-conditioned area & Considered in the study \\ Total roof area & Considered in the study \\ Type of school & Considered in the study \\ Number of students & Considered in the study \\ Number of staff & Considered in the study \\ Number of classrooms & Considered in the study \\ Operating hours per year & Not considered since it is common in all schools \\ Lighting fixtures & Not considered since it is common in all schools \\ Water pump 3 HP & Not considered since it is common in all schools \\ Fire pump 15 HP & Not considered since it is common in all schools \\ \hline \hline \end{tabular}
\end{table}
Table 4: Identified factors that influence the energy consumption of school buildings.  
* Data collection and analysis
* Data filtering
* Building of the initial model, where this phase consisted of the following sub-steps:
* setting the network architecture
* model learning (training)
* model selection
* model testing
* model validation.