An improved method for university building’s energy efficiency index using cluster approach

Energy consumption in commercial buildings is a main concern due to the increasing trend in energy consumption globally. For this reason, energy efficiency in buildings has now become an important subject of energy policies at all levels. Many methods have been proposed to provide an effective way for regularly monitoring the performance of energy consumption as well as to reduce energy usage. Energy Efficiency Index (EEI) is one of the energy consumption indicators that is widely used in the building sector for measuring energy performance. This index is generally measured based on the energy used per unit of building floor area. A low building EEI indicates a large energy saving potential for the building. The current method of determining EEI based on the floor area is not able to identify the optimum level of energy usage since it does not consider the number of occupants and effective time usage in the energy performance evaluation. This thesis proposes a new mathematical algorithm for the determination of a building’s EEI to accurately identify the energy performance of the building. Unlike the existing method for determining EEI, the proposed algorithm tracks the building performance by clustering the building according to room activities. The proposed algorithm is incorporated with a shifting method, retrofitting strategy and human behavioural practice to justify the parameters involved in the EEI configuration. A case study was carried out using a university building and results show that two elements with significant influence on EEI performance are the number of occupants in the room and operation hours. The usage of rooms with an appropriate number of occupants decreased the EEI to 52.66% averagely. In addition, by considering the effective time of load usage, the reduction of EEI occurred up to 33.3%. The proposed algorithm does not only provide an effective energy performance index, but is also able to track the optimum level of energy usage