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.
Highlights
The U.S Energy Information Administration (EIA) estimates that the total U.S natural gas consumption was about 32% in 2019 of total energy consumption
Results are reported to (1) assess the value of smart WiFi thermostat derived information in the form of residence power spectra bins in improving the prediction of monthly energy consumption; and (2) demonstrate the potential of employing the developed model to improve the accuracy of energy savings predictions and the ability to resolve smaller percentage savings from energy system upgrades in residences
A number of the frequency bins are deemed more important than energy characteristic features such as the attic and features such as theimportantly, attic and wall these features can be wall
Summary
The U.S Energy Information Administration (EIA) estimates that the total U.S natural gas consumption was about 32% in 2019 of total energy consumption. As a recent analysis by Stanford University documents, getting to 100% clean and renewable energy by 2050 requires a substantial reduction in energy demand (59%) [3]. Essential in this process, as never before, is the ability to measure savings in order to validate the myriad of energy efficiency experiments which must be conducted. The most cost-effective energy reduction must learn from all actions. This is only possible if the means to estimate savings is certain
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