Navigating multi-dimensional results from large parametric building simulation studies
co-authored with Ivan Korolija and Ljiljana Marjanovic-Halburd
Advances in computing in recent years allow for many thousands of building energy simulations to be computed in the... more
Advances in computing in recent years allow for many thousands of building energy simulations to be computed in the time previously required for a single simulation run. Software tools exist that allow for a single input file to be modified in a number of different ways to generate thousands of self-similar input files which can then be automatically simulated. The problem with this approach is not the simulation time but the time and effort required for the analysis of the vast set of results generated.
Large, multi-dimensional result sets cannot be easily visualised as a whole. One approach is to view the results as a non-linear, interactive document in which only a small part of the results is viewed at any one time. With the addition of simple navigation to select the next sample to view, this approach allows the analyst to easily browse the large result set. More concretely, a one-dimensional sample (a selection of simulations which vary in only one aspect) can be selected from the dataset and visualised as a simple bar chart. Simple rules can then be applied to identify a collection of similar, one-dimensional samples for navigation.
To examine this approach, a prototype tool was developed as a web-based application. The basis for this tool was a multi-parameter simulation study of office building energy consumption including 1,440 individual simulations varying across six dimensions including four building types, five building fabrics, three percentages of glazing, the inclusion of daylight control, two glazing types and six HVAC system types (including building load calculations). The tool included a basic report comparing a one-dimensional sample of results and a detailed report showing time series results for an individual case. Navigation panels allowed for simple traversal of the results set and to move between the two reports. The tool was found to be very useful for navigating the multi-dimensional data and the method is generic enough to be transferable to similar datasets.
Fuel cells for micro-combined heat and power generation
published in Energy and Environmental Science
co-authored with A. Hawkes, D. Brett and N. Brandon
Micro-combined heat and power (CHP) holds great potential for lowering energy cost and CO2 emissions in the... more Micro-combined heat and power (CHP) holds great potential for lowering energy cost and CO2 emissions in the residential housing sector. Of the various micro-CHP technologies, fuel cells, and in particular solid oxide fuel cells, show great promise due to their high electrical efficiency and resulting low heat-to-power ratio that is better suited to residential applications. However, fuel cells are still under development and the capital cost of units available today remains high. This paper looks at the technological aspects and operating modes of fuel cells relevant to micro-CHP as well as examining the state of commercial development, life cycle issues and the techno-economics of fuel cells for micro-CHP at the residential scale.
A Microsimulation Model of Urban Energy Use: Modelling Residential Space Heating Demand in ILUTE
Chingcuanco, F. and Miller, E.J. (2011), Computers, Environment and Urban Systems doi:10.1016/j.compenvurbsys.2011.11.005
Presented at the 12th International Conference on Computers in Urban Planning and Urban Management, July 2011, Lake Louise, Alberta;
Rapid urbanization, climate change and energy security warrant a more detailed understanding of how cities today... more
Rapid urbanization, climate change and energy security warrant a more detailed understanding of how cities today consume energy. Agent-based, integrated microsimulation models of urban systems provide an excellent platform to accomplish this task, as they can capture both the short- and long-term decisions of firms and households which directly affect urban energy consumption. This paper presents the current effort towards developing an urban energy model for the Integrated Land Use, Transportation, Environment (ILUTE) modelling system.
As a first step, a model for the residential space heating system evolution of the Greater Toronto-Hamilton Area was developed. A bottom-up approach, where individual uses are aggregated, was then employed to estimate the region’s space heating demand. Conventional bottom-up methodologies often suffer from insensitivity to either technological or behavioural factors. It is argued that coupling a discrete choice model with building energy simulation software solves this problem. A joint logit model of heating fuel and equipment choice was developed and estimated using Toronto household microdata. The HOT2000 software was then used to compute individual dwelling unit space heating use. The entire residential energy analysis was performed in tandem with the housing market and demographic evolution processes. This allows the endogenous formation of the required inputs as well as adherence to the core ILUTE framework of integrated modelling.
This residential space heating model is a first step towards a comprehensive urban energy end-use model. Further steps include developing similar models for other residential end-uses, such electricity and hot water consumption, as well as extensions to the commercial and transportation sectors. The entire effort aims to introduce an alternate methodology to modelling urban energy consumption that takes advantage of agent-based microsimulation to enhance and address issues with current approaches.
96 views
Seen by:535 views
Seen by: and 13 more15 views
Seen by:
