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Full-Text Articles in Mechanical Engineering
Utilizing Machine Learning Models To Estimate Energy Savings From An Industrial Energy System, Eva Mclaughlin, Jun-Ki Choi
Utilizing Machine Learning Models To Estimate Energy Savings From An Industrial Energy System, Eva Mclaughlin, Jun-Ki Choi
Mechanical and Aerospace Engineering Faculty Publications
Energy audits are an important part of reducing energy usage, costs, and carbon emissions, but there have been discrepancies in the quality of audits depending upon the auditor, which can negatively affect the impacts and credibility of the energy assessment. In this paper, historical energy auditing data from a U.S. Department of Energy sponsored research program was gathered and analyzed with a machine-learning algorithm to predict demand savings from a compressed air system assessment recommendation in industrial manufacturing facilities. Different energy auditors calculate savings for repairing leaks in compressed air systems in various ways, so the energy demand savings have …
Smart Wifi Thermostat-Enabled Thermal Comfort Control In Residences, Robert Lou, Kevin P. Hallinan, Kefan Huang, Timothy Reissman
Smart Wifi Thermostat-Enabled Thermal Comfort Control In Residences, Robert Lou, Kevin P. Hallinan, Kefan Huang, Timothy Reissman
Mechanical and Aerospace Engineering Faculty Publications
The present research leverages prior works to automatically estimate wall and ceiling R-values using a combination of a smart WiFi thermostat, building geometry, and historical energy consumption data to improve the calculation of the mean radiant temperature (MRT), which is integral to the determination of thermal comfort in buildings. To assess the potential of this approach for realizing energy savings in any residence, machine learning predictive models of indoor temperature and humidity, based upon a nonlinear autoregressive exogenous model (NARX), were developed. The developed models were used to calculate the temperature and humidity set-points needed to achieve minimum thermal comfort …
Predicting Industrial Building Energy Consumption With Statistical And Machine-Learning Models Informed By Physical System Parameters, Sean Kapp, Jun-Ki Choi, Taehoon Hong
Predicting Industrial Building Energy Consumption With Statistical And Machine-Learning Models Informed By Physical System Parameters, Sean Kapp, Jun-Ki Choi, Taehoon Hong
Mechanical and Aerospace Engineering Faculty Publications
The industrial sector consumes about one-third of global energy, making them a frequent target for energy use reduction. Variation in energy usage is observed with weather conditions, as space conditioning needs to change seasonally, and with production, energy-using equipment is directly tied to production rate. Previous models were based on engineering analyses of equipment and relied on site-specific details. Others consisted of single -variable regressors that did not capture all contributions to energy consumption. New modeling techniques could be applied to rectify these weaknesses. Applying data from 45 different manufacturing plants obtained from industrial energy audits, a supervised machine-learning model …