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Article history: Available online xxx Keywords: Urban heat island Modeling CFD Remote sensing Meso-scale Urban canopy model Q3 a b s t r a c tThe elevated air temperature of a city, urban heat island (UHI), increases the heat and pollution-related mortality, reduces the habitats' comfort and elevates the mean and peak energy demand of buildings. To countermeasure this unwanted phenomenon, a series of strategies and policies have been proposed and adapted to the cities. Various types of models are developed to evaluate the effectiveness of such strategies in addition to predict the UHI. This paper explains the compatibility of each type of model suitable for various objectives and scales of UHI studies. The recent studies, mainly from 2013 to 2015, are further categorized and summarized in accordance with their context of study.

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 Comprehensive review and evaluation of 28 housing stock energy models (HSEMs), and their underlying data sources, that have been developed to inform UK housing stock decarbonisation policy.  Evaluation criteria include: predictive accuracy, predictive sensitivity to design parameters, versatility, computational efficiency, the reproducibility of predictions and software usability as well as the models' transparency (how open they are) and modularity  Current HSEMs are lacking in transparency and modularity, they are limited in their scope and employ simplistic models that limit their utility; in particular, relating to the modelling of heat flow and of household behaviours.  There is a need for an open-source and modular dynamic HSEM platform that addresses current limitations, can be readily updated as new calibration data is released and be readily extended by the modelling community at large.

The ever-increasing demand for heating in different sectors, along with more preventative regulations on greenhouse emissions, has forced different countries to seek new alternatives to heat buildings such as district heating system (DHS). Although rudiments of DHSs can be observed over the centuries, it was not widely implemented until last two decades when the DHS became a strategy to design more energy-efficient way of heating the buildings. This paper suggests a new approach in categorizing DHSs based on their geographical location, scale, heat density, and end-user demand. Furthermore, this paper reviews system and component modeling approaches with a focus on DHS load prediction. Main limitations of the existing methods are also addressed and discussed with a comprehensive review of the recent studies. Finally, the state of the art in optimization of the different DHSs has been reviewed and categorized based on their objective functions and the techniques used for solving optimization problems (deterministic and heuristic).