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1. PV-EV Integrated Home Energy Management Considering Residential Occupant Behaviors

   https://doi.org/10.3390/su132413826  

   Liu, Xuebo; Wu, Yingying; Wu, Hongyu (December 2021, Sustainability)

   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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2. Enhancing HVAC energy management through multi-zone occupant-centric approach: A multi-agent deep reinforcement learning solution

   https://doi.org/10.1016/j.enbuild.2023.113770  

   Liu, Xuebo; Wu, Yingying; Wu, Hongyu (January 2024, Energy and Buildings)

   Occupant-centric HVAC control places a premium on factors including thermal comfort and electricity cost to guarantee occupant satisfaction. Traditional approaches, reliant on static models for occupant behaviors, fall short in capturing intra-day behavioral variations, resulting in imprecise thermal comfort evaluations and suboptimal HVAC energy management, especially in multi-zone systems with diverse occupant profiles. To address this issue, this paper proposes a novel occupant-centric multi-zone HVAC control approach that intelligently schedules cooling and heating setpoints using Multi-agent Deep Reinforcement Learning (MADRL). This approach systematically takes into account stochastic occupant behavior models, such as dynamic clothing insulation adjustments, metabolic rates, and occupancy patterns. Simulation results demonstrate the efficacy of the proposed approach. Comparative case studies show that the proposed MADRL-based, occupant-centric HVAC control reduces electricity costs by 51.09% compared to rule-based approaches and 4.34% compared to single-agent DRL while maintaining multi-zonal thermal comfort for occupants. 

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3. A kirigami-enabled electrochromic wearable variable-emittance device for energy-efficient adaptive personal thermoregulation

   https://doi.org/10.1093/pnasnexus/pgad165  

   Chen, Ting-Hsuan; Hong, Yaoye; Fu, Ching-Tai; Nandi, Ankita; Xie, Wanrong; Yin, Jie; Hsu, Po-Chun (June 2023, PNAS Nexus)

   Derek Abbott (Ed.)

   For centuries, people have put effort to improve the thermal performance of clothing to adapt to varying temperatures. However, most clothing we wear today only offers a single-mode insulation. The adoption of active thermal management devices, such as resistive heaters, Peltier coolers, and water recirculation, is limited by their excessive energy consumption and form factor for long-term, continuous, and personalized thermal comfort. In this paper, we developed a wearable variable-emittance (WeaVE) device, enabling the tunable radiative heat transfer coefficient to fill the missing gap between thermoregulation energy efficiency and controllability. WeaVE is an electrically driven, kirigami-enabled electrochromic thin-film device that can effectively tune the midinfrared thermal radiation heat loss of the human body. The kirigami design provides stretchability and conformal deformation under various modes and exhibits excellent mechanical stability after 1,000 cycles. The electronic control enables programmable personalized thermoregulation. With less than 5.58 mJ/cm2 energy input per switching, WeaVE provides 4.9°C expansion of the thermal comfort zone, which is equivalent to a continuous power input of 33.9 W/m2. This nonvolatile characteristic substantially decreases the required energy while maintaining the on-demand controllability, thereby providing vast opportunities for the next generation of smart personal thermal managing fabrics and wearable technologies. 

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4. Designing a Consumer Framework for Social Products Within a Gamified Smart Home Context

   https://doi.org/10.1007/978-3-030-78092-0_29  

   Méndez, Juana Isabel; Ponce, Pedro; Miranda, Othoniel; Pérez, Citlaly; Cruz, Ana Paula; Peffer, Therese; Meier, Alan; McDaniel, Troy; Molina, Arturo (July 2021, 23rd International Conference on Human-Computer Interaction (HCII 2021) - Universal Access in Human-Computer Interaction. Design Methods and User Experience)

   The most effective strategy for homes to save energy is by decreasing their electricity consumption. Home Energy Management Systems connect appliances that improve households’ energy performance to thermal comfort. These systems need to take into account human behavior regarding saving energy and thermal comfort. This paper proposes a three-step framework that integrates the Smart Residential Load Simulator (SRLS), Adaptive-Network based on Fuzzy Inference System (ANFIS), and a gamification structure to develop an interface designed to reduce energy consumption without losing thermal comfort. Finally, a gamified mock-up for mobile devices is displayed for a household with high energy consumption levels and a temperature setpoint of 23 ℃. This proposal integrates the concept of social products to empower the interaction between devices and end-users. 

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5. Energy Co-simulation of the Hybrid Cooling Control with Synthetic Thermal Preference Distributions

   Lu, S.; Zhiang, Z.; Hameen, E. C.; Lartigue, B.; Karaguzel, O. (January 2020, Symposium on Simulation in Architecture and Urban Design (SIMAUD 2020))

   Thermal comfort and energy efficiency are always the two most significant objectives in HVAC operations. However, for conventional HVAC systems, the pursuit of high energy efficiency may be at the expense of satisfactory thermal comfort. Therefore, even if centralized HVAC systems nowadays have higher energy efficiency than before in office buildings, most of them cannot adapt the dynamic occupant behaviors or individual thermal comfort. In order to realize high energy efficiency while still maintain satisfactory thermal environment for occupants indoors, the integrated hybrid HVAC system has been developed for years such as task-ambient conditioning system. Moreover, the occupant-based HVAC control system such as human- in-the-loop has also been investigated so that the system can be adaptive based on occupant behaviors. However, most of research related to personalized air-conditioning system only focuses on field-study with limited scale (i.e. only one office room), this paper has proposed a co- simulation model in energyplus to simulate the hybrid cooling system with synthetic thermal comfort distributions based on global comfort database I&II. An optimization framework on cooling set-point is proposed with the objective of energy performance and the constraints of thermal comfort distribution developed by unsupervised Gaussian mixture model (GMM) clustering and kernel density estimation (KDE). The co-simulation results have illustrated that with the proposed optimization algorithm and the hybrid cooling system, HVAC demand power has decreased 5.3% on average with at least 90% of occupants feeling satisfied. 

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