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The exponential growth of cities in India due to urbanization resulted in increased use of non-renewable energy resources to meet the essential power requirements of urban built environment. It is essential for urban planners to provide innovative solutions in context of urban energy simulation based on virtual 3D city models. The recent 3D geoinformation science studies are insufficient in providing optimal solutions because of lack of emerging concepts and integrated softwares. Presently 3D GIS data can be generated into various LODs (Levels of Detail) depending upon the application requirement and input data used. There are various 3D GIS softwares like Google SketchUp, ESRI CityEngine etc., which are being used mostly for data creation especially for boundary representation for geometry abstraction without semantic information. The 3D GIS data conversion from native format into City Geography Markup Language (CityGML) enhances it by providing information both at geometric and at semantic level in interoperable format. A building information model of Geoinformatics department building in IIRS campus is created using Google SketchUp and exported to energy modelling program in gbXML schema. The present investigation explores the semantic characteristics of developed CityGML model for solar thermal and photovoltaic energy production potential assessment based on building semantic components. The amount of solar irradiation incident on bounding features and also illumination obtained through openings of building is quantized using SunCast and RadianceIES application of IESVE Software, respectively. The simulated energy data are integrated with building semantic features and stored in open-source PostGIS RDBMS to address basic semantic queries.

Different associated properties of city models like building geometries, building energy systems, building end uses, and building occupant behavior are usually saved in different data formats and are obtained from different data sources. Experience has shown that the integration of these data sets for the purpose of energy simulation on city scale is often cumbersome and error prone. A new application domain extension for CityGML has been developed in order to integrate energy-related figures of buildings, thermal volumes, and facades with their geometric descriptions. These energy-related figures can be parameters or results of energy simulations. The applicability of the new application domain extension has been demonstrated for heating energy demand calculation.

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A good framework for the quantification and decomposition of uncertainties in dynamic building performance simulation should: (i) simulate the principle deterministic processes influencing heat flows and the stochastic perturbations to them, (ii) quantify and decompose the total uncertainty into its respective sources, and the interactions between them, and (iii) achieve this in a computationally efficient manner. In this paper we introduce a new framework which, for the first time, does just that. We present the detailed development of this framework for emulating the mean and the variance in the response of a stochastic building performance simulator (EnergyPlus co-simulated with a multi agent stochastic simulator called No-MASS), for heating and cooling load predictions. We demonstrate and evaluate the effectiveness of these emulators, applied to a monozone office building. With a range of 25-50 kW h/m 2 , the epistemic uncertainty due to envelope parameters dominates over aleatory uncertainty relating to occupants' interactions, which ranges from 6-8 kW h/m 2 , for heating loads. The converse is observed for cooling loads, which vary by just 3 kW h/m 2 for envelope parameters, compared with 8-22 kW h/m 2 for their aleatory counterparts. This is due to the larger stimuli provoking occupants' interactions. Sensitivity indices corroborate this result, with wall insulation thickness (0.97) and occupants' behaviours (0.83) having the highest impacts on heating and cooling load predictions respectively. This new emulator framework (including training and subsequent deployment) achieves a factor of c.30 reduction in the total computational budget, whilst overwhelmingly maintaining predictions within a 95% confidence interval, and successfully decomposing prediction uncertainties.

Two-Dimensional Geographic Information Science (2D GIS) development has reached its highest level in terms of acquisition, processing, analysis and presentation techniques.Further development in 2D GIS is restricted due to its 2D abstraction of real world objects which are having third dimension (3D) in practical world. The abstraction of 3D real world objects is of extreme importance for user applications to address issues related to infrastructure development, entertainment, tourism, sustainable management of cultural sites and to tackle effects of various social and environmental factors. Hence, formulation of mechanism to model 3D real world objects and its phenomena especially related to urban segment from data acquisition and analysis perspective is essential. The 3D GIS data acquisition techniques such as Satellite Photogrammetry, LIDAR data processing, Building structure extraction algorithms, Close-Range Photogrammetry and total station survey contribute significantly towards generation of 3D digital models. Most of 3D GIS analysis largely depends upon 3D data structure and on data acquisition mechanism in particular. The structured way of data acquisition facilitates encoding of same into common information model which further aids in complex GIS analysis. Therefore, this paper proposes a structured mechanism for data acquisition in context of hierarchical framework of Level-of-Detail (LoD).

Different associated properties of city models like building geometries, building energy systems, building end uses, and building occupant behavior are usually saved in different data formats and are obtained from different data sources. Experience has shown that the integration of these data sets for the purpose of energy simulation on city scale is often cumbersome and error prone. A new application domain extension for CityGML has been developed in order to integrate energy-related figures of buildings, thermal volumes, and facades with their geometric descriptions. These energy-related figures can be parameters or results of energy simulations. The applicability of the new application domain extension has been demonstrated for heating energy demand calculation.