Development of a Modeling Software Tool to Optimize Energy Performance of Medium-Size Office Buildings at the Early Design Stage
Bibliographic record
Abstract
Building energy modeling is an important tool for low-energy building design. An energy modeling tool is used to estimate energy consumption, peak heating and cooling load for sizing mechanical equipment, and to demonstrate compliance with building codes and standards. Modeling tools are designed to evaluate building performance associated with a set of building specifications but it is difficult to predict building loads at the early design stage because most design features have not yet been specified. In addition, the designer must have experience and insight regarding the design features that most strongly influence building performance. \nIn this thesis, a new modeling tool, entitled Excel-Based Load Model (EBLM), was developed to aid designers in the early design stage to estimate building loads, and to size/specify the building components that most strongly affect the overall building performance. EBLM is an open source tool that uses Excel as the calculation engine, creating the advantage that users may modify the program according to their individual interests or project needs. \nEBLM is a single zone modeling tool that consists of three parts: inputs, load calculations, and outputs. The load calculations use the conduction time series (CTS) and radiant time series (RTS) methods to account for the thermal storage effect that delays cooling load. \n One of the unique features of EBLM is the ability to model slat-type operable shading systems. Users can specify one of three shading control strategies. Results can be presented in hourly, monthly, or annual format. The program also outputs the percentage of building loads for each building component in figures and tables. \nThe EBLM has been validated with the commonly used eQUEST model; the difference is about 8% for the sum of all building loads. \nEBLM was also used to perform a series of simulations to examine the influence of building components that are considered to be important. The major conclusions were: \n•\tHigh performance building specifications significantly reduce building loads, and energy-efficient mechanical equipment could further reduce energy consumption. \n•\tAn outdoor operable shading system can be used to effectively block excess solar gain and to help reduce cooling load in summer months, but can also be operated to allow solar gain and to reduce heating load in winter months. \n•\tUsing an outdoor temperature shading control strategy, low solar heat gain coefficient (SHGC) windows with an outdoor operable shading system have better energy performance than high SHGC windows with outdoor operable shading system in Toronto. \n•\tAs found in many studies, the window-to-wall ratio (WWR) has a significant influence on building performance. Lower WWR can minimize both the conductive heat transfer and alleviate excess solar gain. \n•\tLowering the WWR from 40% to 30% has a similar effect on energy use as deploying outdoor operable shading system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".