Publication Type

Journal Article

Version

publishedVersion

Publication Date

3-2022

Abstract

In recent years, the availability of larger amounts of energy data and advanced machine learning algorithms has created a surge in building energy prediction research. However, one of the variables in energy prediction models, occupant behavior, is crucial for prediction performance but hard-to-measure or time-consuming to collect from each building. This study proposes an approach that utilizes the search volume of topics (e.g., education or Microsoft Excel) on the Google Trends platform as a proxy of occupant behavior and use of buildings. Linear correlations were first examined to explore the relationship between energy meter data and Google Trends search terms to infer building occupancy. Prediction errors before and after the inclusion of the trends of these terms were compared and analyzed based on the ASHRAE Great Energy Predictor III (GEPIII) competition dataset. The results show that highly correlated Google Trends data can effectively reduce the overall RMSLE error for a subset of the buildings to the level of the GEPIII competition’s top five winning teams’ performance. In particular, the RMSLE error reduction during public holidays and days with site-specific schedules are respectively reduced by 20–30% and 2–5%. These results show the potential of using Google Trends to improve energy prediction for a portion of the building stock by automatically identifying site-specific and holiday schedules.

Keywords

Google Trends, Machine learning, Kaggle competition, Model error reduction, Building energy prediction, energy model

Discipline

Energy Policy | Engineering

Research Areas

Integrative Research Areas

Publication

Applied Energy

Volume

310

First Page

1

Last Page

10

ISSN

0306-2619

Identifier

10.1016/J.APENERGY.2021.118343

Publisher

Elsevier

Additional URL

https://doi.org/10.1016/j.apenergy.2021.118343

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