## 1 Introduction
A data-driven approach learns from data to predict output. Machine learning, a data-driven approach, extracts actionable insight from training data through statistical technique [31]. Deep learning model also learns hidden representation by applying a representation learning method on preprocessed raw data [32]. Various data-driven approaches, such as, Random Forest (RF) [33], Light Gradient Boosting Machine (LightGBM) [34], Automated Machine Learning (AutoML) [35], and Artificial Neural Network (ANN) [36] are employed to predict energy consumption of buildings. However, a data-driven approach can predict misleading output due to biased or erroneous training dataset [37]. On the other hand, a knowledge-driven approach obtains a predictive output based on knowledge, which is represented by rules [38]. This approach is constituted by an expert system consisting of two components: a knowledge base and an inference engine. The knowledge base represents knowledge of the relevant domain or application area, and the inference engine performs reasoning over this knowledge base to infer predictive output. Examples of knowledge-driven approaches include the Belief Rule-Based Expert System (BRBES), MYCIN [39], and fuzzy logic. A fuzzy logic algorithm was proposedby [40] to predict short-term energy consumption in individual buildings. Their proposed fuzzy logic system comprises fuzzification, rule definition, inference, and defuzzification steps, enabling effective day-to-day consumption predictions. Another study combined a fuzzy rule-based prediction system with multi-objective evolutionary optimization to predict residential building energy consumption [41]. The BRBES was employed by [37] to predict energy consumption of buildings in a transparent manner. They trained the BRBES with evolutionary optimization using supervised labeled data to improve accuracy. A knowledge-driven approach is not dependent on training data [37]. Its knowledge base is formulated by experts with relevant domain knowledge. Hence, a knowledge-driven approach is free from the risk of bias or errors in a training dataset [37]. In contrast to an ad hoc training dataset, property managers are more inclined to believe a prediction output that is based on knowledge pertaining to the energy consumption domain [37]. Moreover, domain knowledge can capture the complex interplay among underlying physical and operational factors of a building, which influence its energy use [42]. Such factors include building design, occupancy patterns, HVAC (Heating, Ventilation, and Air Conditioning) systems, and external environmental conditions. Therefore, we opt for the knowledge-driven approach to infer the prediction of energy consumption of buildings in this paper. Moreover, predictive energy consumption output can become erroneous due to wrong input data caused by human error or blank input data caused by human ignorance, resulting in uncertainties [43]. Such uncertainties are caused by imprecise or incomplete human knowledge [44; 45]. Therefore, these uncertainties have to be addressed properly to uphold the prediction accuracy. The belief rule basis of the BRBES, formulated based on a belief structure, can capture uncertainties and nonlinear causal relationships [46]. This rule base of the BRBES, formulated by domain experts, represents knowledge of the energy consumption domain. In case of unavailability of some or all of the input data, the BRBES updates belief degrees of its rule base [47]. THe BRBES obtains its predictive output by applying Evidential Reasoning (ER) on the rule base [8; 48]. Thus, the BRBES outperforms other knowledge-driven approaches in terms of handling uncertainties, especially those caused by ignorance [47]. Hence, as knowledge-driven approach, we employ the BRBES in this research to obtain predictive energy consumption output. By representing energy consumption domain knowledge through a rule base, the BRBES offers higher explainability [37]. However, it has lower accuracy compared to conventional machine learning models.