Energy prediction techniques for large-scale buildings towards a sustainable built environment: A review

Abstract

Building energy prediction techniques are the primary tool for moving towards sustainable built environ-ments. Energy prediction models play irreplaceable roles in making energy policy and the development of the building sector. This paper presents a comprehensive review of the prevailing prediction techniques used in large-scale building energy applications under different scopes and different archetypes, includ-ing black-box, white-box, and grey-box based methods. Additionally, the advantages and disadvantages of the applications of those approaches are compared and discussed in the context of large-scale build-ings. The review results show that prediction techniques have addressed a variety of large-scale building energy related-applications, such as energy consumption forecasting and prediction, energy consumption profiling, energy mapping and benchmarking of buildings. However, there are still some research gaps that require more attention such as the inclusion of occupant behavior in white-box based models and the explicit representation of end-uses in black-box based models. Significantly, this review concludes with a few key tasks for modification of the current prediction approach framework, which can help with forecasting future energy use changes of specific buildings during the retrofit process or inclusion of renewable energy technology. This would assist in developing an appropriate strategy for the sustainabil-ity of the built environment. (c) 2020 Elsevier B.V. All rights reserved.