Abstract
<div>A surrogate model was developed for a detached archetypal home in Toronto, ON. EnergyPlus was used to perform 1500 simulations within a design space defined by 23 input parameters with ranges based on field study data. Elastic net regression was used to create a surrogate model to predict annual energy use and to perform embedded feature selection. An analysis comparing house size to model performance found that including both small and large homes did not decrease the model accuracy. The final regression model predicted energy use with an average R<sup>2</sup> of 0.946 and MAPE of 6.1% using nested cross- validation. A case study predicted actual annual energy use of two homes in Toronto within 10% error of utility bill data. A preliminary optimization analysis found that several weeks of simulation time could be saved and more optimal solutions could be discovered compared to a brute-force forward stepwise selection optimization.</div>
Highlights
The Pocket community [15] partnered with Toronto Archetype Project (TAP) in 2016 to allow researchers to use the neighbourhood as an example for archetype classification and net-zero community energy planning
Jermyn determined that 45% of 33,570 single family homes in Toronto were represented by the century home archetype [10]
The surrogate model predicted annual energy use. This was compared to the average annual energy use over two years from the utility bills
Summary
Residential buildings account for 17% of the secondary site energy use in Canada [1]. Deep retrofits that involve extensive renovations of the building’s systems must be undertaken These retrofits can have substantial costs, determining the combination of retrofit solutions that minimize cost and maximize energy and GHG reductions is essential to incentivize these changes. In 2014 Jermyn and Richman [10] developed archetypes for century detached, century semi, and wartime homes, and performed brute-force optimization to determine the most cost-effective retrofit solution to meet a specific energy performance target. Jermyn’s methodology for archetype development [10] followed three phases which have been simplified as (1) determine archetypes, (2) collect characteristic housing data to build and calibrate baseline energy models, and (3) develop retrofit strategies and associated costs. Jermyn collected data for geometry, envelope constructions, airtightness, internal gains, and HVAC systems This data was averaged and input into an energy model to create a baseline archetype model.
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