## 1 Introduction
Total energy consumption was considered more in residential buildings than in the commercial, government, and industrial buildings due to their greater number of units. [PERSON] et al. [17] reviewed machine learning and the Internet-of-Things to achieve smart and energy-efficient buildings. Various sensors, gateways, clouds, and network infrastructures were identified, and their role in smart buildings were discussed [17]. Various challenges, including huge data analysis, network services and its security, an efficient energy management system, easy visibility and accessibility, the type and nature of the controlling sensors, the effect of resident behavior, among others, were encountered when making the buildings smart and energy-efficient [17; 18; 19; 20; 21; 22; 23; 24]. [PERSON] and [PERSON] [25] explored the effectiveness of various retrofitting techniques that influence the energy consumption in an apartment building. Factors which contributed more towards the energy consumption included the requirement of hot water, as well as automation in the heating and cooling systems [25; 26; 27; 28]. [PERSON] et al. [29] studied the performance of wood materials for roofing with respect to its thermal efficiency. [PERSON] [30] investigated the factors that influence the energy consumption in a school building constructed in a hot climate region. A neural network model was used for optimizing the various factors. It was concluded that the input parameters showed a poor correlation for the school type and air conditioning capacity, while it exhibited a good correlation between the number of classrooms, the total area, and the number of students. Itwas reported that the air conditioning capacity is the most significant factor influencing the energy consumption [30]. [PERSON] et al. [31] studied the energy consumption management system and proposed measures for its improved efficiency. They utilized three models and algorithms for optimizing the energy loads. These algorithms were validated for various datasets and implemented to achieve a reduction in the energy costs, thereby leading to the improved efficiency of the system [31]. [PERSON] et al. [32] reviewed the energy-efficiency policies against climate change in Paraguay. Initially, various climate scenarios and their effects on buildings were discussed in the study. Several activities were suggested for improved energy efficiency against climate change so to address the encountered challenges [32]. [PERSON] et al. [33] conducted a validation study on two office buildings during the summer and winter seasons using energy simulations. It was reported that the proposed schemes could save energy up to 22%, and the study demonstrated the efficacy of data-driven smart building designs [33]. [PERSON] et al. [34] compared various rating systems for green buildings. The main strength, weaknesses, similarities, differences, and sustainability aspects among the various rating systems were identified, and economical aspects were discussed [34]. [PERSON] et al. [35] also reviewed the various rating tools for green buildings based on passive design techniques. [PERSON] et al. [36], using the DesignBuilder software, studied various configurations of windows on energy consumption. It was concluded that the thickness of glass has a direct influence on the cooling and heating requirements. Furthermore, double-pane windows showed the shortest return period [36]. [PERSON] et al. [37] explored the feasibility of using energy generation through facade-mounted photovoltaic chambers under various whether conditions. [PERSON] et al. [38] studied the effect of the LEED certification on the energy performance of a college building. It was reported that the building having a LEED certification showed improved performance and satisfaction due to improved internal environmental conditions. [PERSON] and [PERSON] [39] studied the energy performance of a hospital building using various algorithms, including artificial neural networks, an adaptive neuro-fuzzy inference system, and a long-short term memory.