Profiling and forecasting daily energy use with monthly utility-data regression models.(Report): An article from: ASHRAE Transactions

This digital document is an article from ASHRAE Transactions, published by American Society of Heating, Refrigerating, and Air-Conditioning Engineers, Inc. on July 1, 2010. The length of the article is 4366 words. The page length shown above is based on a typical 300-word page. The article is delivered in HTML format and is available immediately after purchase. You can view it with any web browser.

From the author: The generality of these case study results was investigated in this paper by comparing outdoor air based regression models of simulated daily and monthly energy use. The results indicate that the variation in energy use caused by variable solar radiation, outdoor air humidity, and heat loss to the ground is larger at the daily time scale than the monthly time scale. However, these drivers are sufficiently correlated with outdoor air temperature so that the overall predictive ability of outdoor air temperature based models is still quite good. In addition, the results in this paper indicate that although building energy use is driven by factors that change on the sub-hourly time scale, these effects are fairly evenly distributed over time; thus, models based on longer time scale data can accurately characterize a building's energy use.

Citation Details
Title: Profiling and forecasting daily energy use with monthly utility-data regression models.(Report)
Author: Kevin Carpenter
Publication:ASHRAE Transactions (Magazine/Journal)
Date: July 1, 2010
Publisher: American Society of Heating, Refrigerating, and Air-Conditioning Engineers, Inc.
Volume: 116 Issue: 2 Page: 639(13)

Article Type: Report

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