1 / 5

Optimizing Thermostat Settings in Residential Buildings Analysis

Explore adjusted thermostat settings in residential buildings for optimal energy efficiency. Proposed regression model adjustments for heating zones and insulation variables. Compare simplicity versus accuracy in data analysis results.

thor
Download Presentation

Optimizing Thermostat Settings in Residential Buildings Analysis

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Extra Slides to Accompany:SEEM 94 Calibration to Single Family RBSA DataAnalysis and proposed actions Regional Technical Forum May 21, 2013

  2. “Calibrated” Thermostat Settings with Case Sample Sizes

  3. “Calibrated” T-stat Settings with Billing and Adjusted SEEM Heating Use

  4. Regression Alternatives • Remove the Heating Zone 2 Coefficient since its p-value (0.24) is greater than 0.05. • This would reduce (slightly) the adjustment factor for heating zone 1 and increase it (to ~5%) for heating zone 2. • Refer to Slide 28 in main presentation. • Separate the ceiling/wall insulation variable into 2 separate variables that are continuous up to u= 0.15, then hold constant if u > 0.15. • This would cause a new interaction term. • This would cause an infinite number of temperature settings for houses with u values less than 0.15. • Examples (Gas/HP, Heating Zone 1) • Ceiling Insulation • R38 std ceiling  adjustment factor = 107.2% • R49 std ceiling  adjustment factor = 108.3% • Wall Insulation • R19 2x6 STD  adjustment factor = 120.0% • R21 2x6 STD + R5 Foam  adjustment factor = 133.0% While the second model fits the data better, its added complexity may not justify its use. The first model is reasonably accurate and much simpler, so staff recommends using it.

  5. Alternative Regression Results (Option B) Adjusted R-square = 0.24

More Related