Hourly occupant clothing decisions in residential HVAC energy management

Liu, Xuebo; Wu, Yingying; Zhang, Haifeng; Wu, Hongyu

doi:10.1016/j.jobe.2021.102708

Cited by 7 publications

(6 citation statements)

References 24 publications

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“…LSTM is one type of recurrent neural networks that have superior time-series predictions for short-term weather conditions. The details of how to integrate LSTM into the HEMS procedure are provided in [30,38].…”

Section: Mpc-based Hems Simulation Frameworkmentioning

confidence: 99%

“…The results here are consistent with those in the authors' prior work, which takes into account the donning and doffing into HEMS for both genders in different seasons (but without PV, EV, or EV SOC concern). The result in [30] showed that if the occupant follows the optimal clothing decisions produced, 53.8% and 29.8% of daily electricity cost savings can be achieved for a summer-male scenario and a winter-female scenario, respectively. More simulation results can be found in [30].…”

Section: Proposed Hems Simulationmentioning

confidence: 99%

“…The result in [30] showed that if the occupant follows the optimal clothing decisions produced, 53.8% and 29.8% of daily electricity cost savings can be achieved for a summer-male scenario and a winter-female scenario, respectively. More simulation results can be found in [30]. Figure 11 shows the EV simulation results by the proposed HEMS.…”

Section: Proposed Hems Simulationmentioning

confidence: 99%

“…Such adjustments can improve home demand flexibility and thus enable a higher energy cost saving. One relevant study that explicitly considers clothing behaviors in PMV in the context of home energy management is the authors' previous work [30], which is first-of-its-kind in modeling the clothing behaviors as controllable variables to optimally adjust the clothing levels for occupants. The authors believe that the hourly or subhourly clothing adjustment should be fully captured and optimized in the residential home energy management to model HVAC systems realistically.…”

Section: Introductionmentioning

confidence: 99%

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PV-EV Integrated Home Energy Management Considering Residential Occupant Behaviors

Liu

2021

Sustainability

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Rooftop photovoltaics (PV) and electrical vehicles (EV) have become more economically viable to residential customers. Most existing home energy management systems (HEMS) only focus on the residential occupants’ thermal comfort in terms of indoor temperature and humidity while neglecting their other behaviors or concerns. This paper aims to integrate residential PV and EVs into the HEMS in an occupant-centric manner while taking into account the occupants’ thermal comfort, clothing behaviors, and concerns on the state-of-charge (SOC) of EVs. A stochastic adaptive dynamic programming (ADP) model was proposed to optimally determine the setpoints of heating, ventilation, air conditioning (HVAC), occupant’s clothing decisions, and the EV’s charge/discharge schedule while considering uncertainties in the outside temperature, PV generation, and EV’s arrival SOC. The nonlinear and nonconvex thermal comfort model, EV SOC concern model, and clothing behavior model were holistically embedded in the ADP-HEMS model. A model predictive control framework was further proposed to simulate a residential house under the time of use tariff, such that it continually updates with optimal appliance schedules decisions passed to the house model. Cosimulations were carried out to compare the proposed HEMS with a baseline model that represents the current operational practice. The result shows that the proposed HEMS can reduce the energy cost by 68.5% while retaining the most comfortable thermal level and negligible EV SOC concerns considering the occupant’s behaviors.

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Section: Mpc-based Hems Simulation Frameworkmentioning

confidence: 99%

Section: Proposed Hems Simulationmentioning

confidence: 99%

Section: Proposed Hems Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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PV-EV Integrated Home Energy Management Considering Residential Occupant Behaviors

Liu

2021

Sustainability

Self Cite

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“…In past decades, ML techniques have been utilized to solve the classification and regression problems [7], leading to many noteworthy applications such as wind speed prediction [8], temperature forecasting [9], solar irradiance forecasting [10], train arriving time prediction [11], construction strength prediction [12], speech recognition [13],…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Enabled 3D Body Measurement Estimation Using Hybrid Feature Selection and Bayesian Search

Liu

2022

Applied Sciences

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The 3D body scan technology has recently innovated the way of measuring human bodies and generated a large volume of body measurements. However, one inherent issue that plagues the use of the resultant database is the missing data usually caused by using automatic data extractions from the 3D body scans. Tedious extra efforts have to be made to manually fill the missing data for various applications. To tackle this problem, this paper proposes a machine learning (ML)-based approach for 3D body measurement estimation while considering the measurement (feature) importance. The proposed approach selects the most critical features to reduce the algorithm input and to improve the ML method performance. In addition, a Bayesian search is further used in fine-tuning the hyperparameters to minimize the mean square error. Two distinct ML methods, i.e., Random Forest and XGBoost, are used and tested on a real-world dataset that contains 3D body scans of 212 participants in the Kansas-Missouri area of the United States. The results show the effectiveness of the proposed methods with roughly 3% of Mean Absolute Percentage Errors in estimating the missing data. The two ML methods with the proposed hybrid feature selection and the Baysian search are comprehensively compared. The comparative results suggest that the Random Forest method performs better than the XGBoost counterpart in filling missing 3D body measurements.

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Impact of model predictive control-enabled home energy management on large-scale distribution systems with photovoltaics

Pratt

Munankarmi

et al. 2022

Advances in Applied Energy

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Hourly occupant clothing decisions in residential HVAC energy management

Cited by 7 publications

References 24 publications

PV-EV Integrated Home Energy Management Considering Residential Occupant Behaviors

PV-EV Integrated Home Energy Management Considering Residential Occupant Behaviors

Machine Learning Enabled 3D Body Measurement Estimation Using Hybrid Feature Selection and Bayesian Search

Impact of model predictive control-enabled home energy management on large-scale distribution systems with photovoltaics

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