A considerable amount of the oil used in Saudi Arabia is consumed to generate power. In an effort to minimize such consumption, the government of Saudi Arabia has established an ambitious part of the 2030 Vision that includes 96 strategic objectives. In recent years, the advancement in information technology has made it possible to develop accurate prediction models of energy consumption for constructed facilities, which can assist with establishing plans to reduce future energy consumption. This research proposes a regression-based model for predicting the energy consumption of one of the most significant energy-consuming types of facilities in Saudi Arabia: schools. The model was developed utilizing 350 actual data points of energy consumption gathered from schools operating in the eastern province of Saudi Arabia. The factors affecting consumption of energy were identified from two sources. The first was a review of the literature, and the second was interviews with local experts. A sensitivity analysis indicated that the most important factors (i.e., inputs/independent variables) affecting energy consumption (i.e., output) were AC capacity and building age. An investigation of the correlations among the independent variables revealed that the highest correlation of 0.864 was found between the total built area and total roof area. The developed model was validated using 35 new data points of school buildings across the eastern province of Saudi Arabia. The results show that the model predicted the energy consumption of school buildings with an accuracy higher than 90%. The findings of this study will assist schools and maintenance managers in effectively managing such facilities by allowing them to allocate the required budget in advance. Also, the predictions can be used economic lifecycle analysis.

沙特阿拉伯使用的大量石油被消耗以发电。为了尽量减少此类消费，沙特阿拉伯政府已制定了2096年愿景的宏伟目标，其中包括96个战略目标。近年来，随着信息技术的发展，有可能为建筑设施开发准确的能耗预测模型，从而有助于制定减少未来能耗的计划。这项研究提出了一种基于回归的模型，用于预测沙特阿拉伯最重要的能耗类型之一的设施（学校）的能耗。该模型是利用从东部沙特阿拉伯省运营的学校收集的350个能耗的实际数据点开发的。从两个方面确定了影响能源消耗的因素。第一个是对文献的评论，第二个是对当地专家的采访。敏感性分析表明，影响能耗（即输出）的最重要因素（即输入/独立变量）是交流电容量和建筑年龄。对独立变量之间的相关性进行的研究表明，在总建筑面积和总屋顶面积之间发现最高的相关性为0.864。使用东部沙特阿拉伯全省的35个新的学校建筑数据点对开发的模型进行了验证。结果表明，该模型对学校建筑能耗的预测精度高于90％。这项研究的结果将允许学校和维修经理提前分配所需的预算，从而帮助他们有效地管理此类设施。同样，可以将预测用于经济生命周期分析。