Energy Cost Analysis of a Green Roof and Photovoltaics System on WPI's Future Recreation Center
Bibliographic record
Abstract
Green roofs are new roofing technology that use vegetation to cleanse the environment, and photovoltaics directly produce electricity from absorbed sunlight. This study determined the economic feasibility of installing photovoltaic and green roof technologies on the roof of the new recreation center at WPI and found an economic balance between the two technologies. Due to the cost benefit and pay back period analyses, the green roof system would be the more beneficial technology for the future recreation center at WPI. Kristopher Kellogg (Chemical Engineering), Arvind Srinivasan (Mechanical Engineering), Brian Tanguay 3 (Electrical and Computer Engineering), Andrea Tarbet (Biology/Biotechnology) Advisor: Professor Brian Savilonis (Mechanical Engineering) Conclusions/Recommendations Acknowledgments James Demetry, Prof. (Emeritus) of Electrical and Computer Engineering; Svetlana Nikitina, Adjunct Assistant Professor of English; Devin Oakes ,Peer Learning Assistant; Brian Savilonis, Prof. of Mechanical Engineering, Director of Thermo/Fluid Program; David Spanagel, Visiting Assistant Professor of History; and the staff of the Academic Technology Center: Kate Beverage, Instructional Technology Specialists; Jessica Caron, Instructional Technology Specialists; and Jim Monaco, Media Production Coordinator. References Bowles, Ian, Arleen O’donnell, Glenn Haas, and David Delorenzo. Massachusetts. Massachusetts Department of Environmental Protection. Massachusetts Nonpoint Source Management Plan. Jan. 2007. 24 Nov. 2007 . This article provided information regarding the Massachusetts Watershed Program and the criteria for funding. Jeffrey Brown And John Br. Latitude and Look-Up Latitude and Longitude. BCCA. 28 Nov. 2007 . This website provided the Latitude and Longitude for Worcester, MA and Toronto, CA Building-Integrated PV. Energy Ideas. 25 Nov. 2007. Washington State University. 24 Nov. 2007 . The Cost of PV with installation costs of a unit producing over 1 kWh Clark, Corrie, Peter Adriaens, and Brian F. Talbot. Green Roof Valuation: a Probabilistic Economic Analysis of Environmental Benefits. University of Michigan. 2006. 1-28. 24 Nov. 2007 . This paper compares the Net Present Value of a green roof on the campus of the University of Michigan with conventional roof. It also discusses how the reduction of emissions can be converted into health dollars. Kosareo, Lisa, and Robert Ries, comps. Comparative Environmental Lifecycle Assessment of Green Roofs. 12 June 2006. University of Pittsburgh. 7 Nov. 2007 . This study anaylzed a normal roof, intensive green roof, and extensive green roof in PA. Cycle Costing. Sandia. 2002. Sandia Nat. Laboratories. 24 Nov. 2007 . This source provided the equation for the Life Cycle Cost of the PV system Background Background cont. Photovaltaics The Recreation Center We measured the football field in the model and since, a football field is supposed to be 100 yards long, we set 28 cm to 100 yards. The main rectangular building had additions like the rectangular pyramid and a smaller rectangular prism. All the dimensions of the building were then approximated and the total volume as shown above was calculated. Scale: 100 yards ≡ 28 cm Conversion factor: 1 yard ≡ 3 feet Roof Calculations Area of the roof: 19cm*(100yards/28cm)*9cm*(100 yards/28cm) =1900yards/28*(3ft/1yard) * 900yards/28*(3ft/1yard) = 19630 ft2 ≈ 19000 ft2
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".