Using Smart-WiFi Thermostat Data to Improve Prediction of Residential Energy Consumption and Estimation of Savings

Abdulrahman Alanezi, Kevin P. Hallinan and Rodwan Elhashmi
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Abdulrahman Alanezi: Department of Mechanical & Aerospace Engineering, University of Dayton, Dayton, OH 45469-0238, USA
Kevin P. Hallinan: Department of Mechanical & Aerospace Engineering, University of Dayton, Dayton, OH 45469-0238, USA
Rodwan Elhashmi: Department of Mechanical & Aerospace Engineering, University of Dayton, Dayton, OH 45469-0238, USA

Energies, 2021, vol. 14, issue 1, 1-16

Abstract: Energy savings based upon use of smart WiFi thermostats ranging from 10 to 15% have been documented, as new features such as geofencing have been added. Here, a new benefit of smart WiFi thermostats is identified and investigated; namely, as a tool to improve the estimation accuracy of residential energy consumption and, as a result, estimation of energy savings from energy system upgrades, when only monthly energy consumption is metered. This is made possible from the higher sampling frequency of smart WiFi thermostats. In this study, collected smart WiFi data are combined with outdoor temperature data and known residential geometrical and energy characteristics. Most importantly, unique power spectra are developed for over 100 individual residences from the measured thermostat indoor temperature in each and used as a predictor in the training of a singular machine learning models to predict consumption in any residence. The best model yielded a percentage mean absolute error (MAE) for monthly gas consumption ±8.6%. Applied to two residences to which attic insulation was added, the resolvable energy savings percentage is shown to be approximately 5% for any residence, representing an improvement in the ASHRAE recommended approach for estimating savings from whole-building energy consumption that is deemed incapable at best of resolving savings less than 10% of total consumption. The approach posited thus offers value to utility-wide energy savings measurement and verification.

Keywords: smart WiFi thermostats; machine learning; residential; energy consumption; energy savings (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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