The Effect of Occupant in Energy Consumption Prediction via Multiple Linear Regression Model in an Educational Building

• Published in: SMART GRID AND RENEWABLE ENERGY SYSTEMS, ICRCE 2024
• Main Authors: Mustapa, Rijalul Fahmi; Nordin, Atiqah Hamizah Mohd; Hairuddin, Muhammad Asraf; Mahadan, Mohd Ezwan; Dahlan, N. Y.; Yassin, Ihsan Mohd
• Format: Proceedings Paper
• Language: English
• Published: SPRINGER-VERLAG SINGAPORE PTE LTD 2024
• Subjects: Science & Technology - Other Topics; Energy & Fuels
• Online Access: https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001313717500010

Energy consumption prediction is crucial for effective energy conservation measures. Accurate predictions rely on a model that correlates energy consumption with associated independent variables. Different spaces within an educational building are occupied by various occupants. Additionally, the loads in different areas of an educational building can affect energy consumption by the occupants themselves. Therefore, the objective of this study is to predict energy consumption in an educational building by modeling energy consumption in relation to occupants in various spaces within the building. Multiple linear regression will be employed as the modeling technique, with five types of occupants and outdoor temperature serving as the independent variables. The results indicate that a model including four occupants and outdoor temperature achieves the highest prediction accuracy. Consequently, it can be conclude that occupants in different spaces have a significant impact on energy consumption prediction. However, it's worth noting that the model incorporating five occupants and outdoor temperature yields lower prediction accuracy, suggesting certain limitations of the model.