An energy and water resource demand estimation model for multi-family housing complexes in Korea

This paper proposes and develops a residential energy and resource consumption estimation model in the context of multi-family residential housing in Korea using a multi-layer perceptron (MLP) neural network. Eight indicators are introduced which affect the energy and water resource usage characteristics of Korean residential complexes. The proposed model precisely estimated the electricity, gas energy and water consumption for each examined residential complex. In terms of validation, the results showed the highest level of agreement with actually collected datasets. The model shows promising prospects in providing necessary estimations, not only for optimally scaling and sizing energy- and water-related infrastructures, but also to promote reliable energy and resource savings through greenhouse gas (GHG) reduction planning in multi-family housing complexes. The model could also be of use in framing guidelines for the better planning of national or regional energy and resource policies and for forming a foundation of decision-making with definite references regarding the facility management of each apartment complex to enhance the energy and resource use efficiency at these locations.