With the growing trove of publicly available building energy data, there are now ample opportunities to apply machine learning methods for prediction of building energy performance. In this study, we test different predictive modeling approaches for estimating Energy Use Intensity (EUI) for US commercial office buildings and the individual energy end-uses of HVAC, plug loads, and lighting, based on the latest Commercial Building Energy Consumption Survey (CBECS) 2012 microdata. After preliminary statistical analysis, six regression or machine learning techniques are applied and compared for prediction performance. Among all candidates, Support Vector Machine and Random Forest demonstrate both accuracy and stability. However, machine learning algorithms are better than the linear regression only to a limited extent, with on average 10–15% lower prediction errors for Total EUI prediction. Conversely, linear regression models slightly outperform machine learning methods in estimating Plug Loads EUI. These mixed results suggest careful consideration in applying advanced predictive algorithms to the CBECS dataset. Individual variable importance was tested using Random Forest, with the top 10 predictors differing for the total and sub-system EUIs. The analysis demonstrates that, for the techniques applied, the variables reported in CBECS have inadequate predictive power to map actual energy consumption. Filling information gaps in areas such as occupant behavior, power management, building thermal performance, and their interactions may help to improve predictive modeling.
Energy and Buildings – Elsevier
Published: Mar 15, 2018
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 12 million articles from more than
10,000 peer-reviewed journals.
All for just $49/month
Read as many articles as you need. Full articles with original layout, charts and figures. Read online, from anywhere.
Keep up with your field with Personalized Recommendations and Follow Journals to get automatic updates.
It’s easy to organize your research with our built-in tools.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera