Inverse estimation of the urban heat island using district-scale building energy calibration

Over the past decade, building energy modelling research has increasingly focused on urban-scale models. The shortcomings of analyzing urban buildings in isolation are well known and far from negligible (mainly, the inability to account for urban heat island and shading from neighboring obstructions). The aim of this paper is to assess the impact of the urban context via urban-scale modelling and inverse parameter estimation (calibration) using metered energy consumption of each building. We describe an automated calibration method for 58 buildings in a representative downtown district of Abu Dhabi. This district has undergone a detailed energy audit and a large amount of data about the building envelopes, cooling loads and electricity consumption has been collected from 2008 to 2010. In our models, buildings are subdivided in up to three use types documented in the audit (Residential, Office and Retail). Since it is well known that, due to the urban heat island effect, the urban ambient air temperature can differ significantly from the reference rural air temperature used in most building simulations, the calibration procedure will also estimate this differential together with unknown building parameters. The main contribution of the paper is to demonstrate that the proposed district-scale calibration is, in average, more accurate than individual building calibration and informs not only on buildings but also on the outdoor environment. The calibration was performed using Genetic Algorithm, reaching an average building MAPE of 25.24%. For the district as a whole, a MAPE of 12.01% was achieved. The estimation of Urban Heat Island intensity revealed a daily maximum of 5.6 degrees C and an average daily differential of 3 degrees C for a typical day, showing the relevance to consider it for any building energy simulation. (C) 2017 The Authors. Published by Elsevier Ltd.