Yixing Chen

Although research on occupancy detection has been extensive, most studies have focused on improving detection accuracy, while the application of occupancy models to device control remains limited. This paper presented an occupant-centric control (OCC) of split air conditioners, personal desktop fans, and lights based on the Wi-Fi probe technology, Internet of Things (IoT), and time-of-use (TOU) electricity rates. Firstly, a machine-learning model based on Wi-Fi signals for occupancy detection was developed. The occupancy detection model integrated convolutional neural networks, gradient boosting classifier, and random forest (CNN-GBC-RF) models to identify four types of occupant statuses: arrival, leave, stay, and outside. Subsequently, the occupancy status and TOU rates information were integrated with IoT-enabled devices to dynamically control the space split air conditioners, personal desktop fans, and personal lights. This study implemented two experimental scenarios: a baseline scenario with fixed device operations and an OCC scenario. The OCC scenario dynamically adjusted device operation based on real-time occupancy status, TOU electricity rates, desktop illuminances, and dry-bulb air temperatures. Finally, a 16-day experiment was conducted in a multi-occupant office room to evaluate both occupancy detection model performance and the effectiveness of the proposed OCC-based scenario. Subjective questionnaires were collected to evaluate the thermal comfort under different scenarios. The results showed that the CNN-GBC-RF model had an overall detection accuracy of 97.0%. Additionally, the OCC-based scenario achieved a relative reduction of 39.9%, a relative reduction of 41.6% compared to the baseline scenario. The thermal comfort of the occupants under the OCC scenario was close to the baseline scenario. The results indicated that the proposed OCC-based control scenario contributed to improving energy efficiency while maintaining occupant thermal comfort.

To face the global energy crisis, the requirement of energy transition and sustainable development has emphasized the importance of controlling building energy management systems. Reinforcement learning (RL) has shown notable energy-saving potential in the optimal control of heating, ventilation, and air-conditioning (HVAC) systems. However, the coupling of the algorithms and environments limits the cross-scenario application. This paper develops chiller plant models in OpenAI Gym environments to evaluate different RL algorithms for optimizing condenser water loop control. A shopping mall in Changsha, China, was selected as the case study building. First, an energy simulation model in EnergyPlus was generated using AutoBPS. Then, the OpenAI Gym chiller plant system model was developed and validated by comparing it with the EnergyPlus simulation results. Moreover, two RL algorithms, Deep-Q-Network (DQN) and Double Deep-Q-Network (DDQN), were deployed to control the condenser water flow rate and approach temperature of cooling towers in the RL environment. Finally, the optimization performance of DQN across three climate zones was evaluated using the AutoBPS-Gym toolkit. The findings indicated that during the cooling season in a shopping mall in Changsha, the DQN control method resulted in energy savings of 14.16% for the cooling water system, whereas the DDQN method achieved savings of 14.01%. Using the average control values from DQN, the EnergyPlus simulation recorded an energy-saving rate of 10.42% compared to the baseline. Furthermore, implementing the DQN algorithm across three different climatic zones led to an average energy savings of 4.0%, highlighting the toolkit’s ability to effectively utilize RL for optimal control in various environmental contexts.

With the increasing demand for electricity, it is causing a growing burden on the power grid. In order to alleviate the pressure on the power system, a series of demand response (DR) strategies have emerged. This paper studied the DR potential and energy flexibility on city-scale building clusters under pre-cooling combined with temperature reset. This study firstly selected 18 types of buildings, each containing three construction years as prototype buildings, to represent the 228,539 buildings in Shenzhen. Then several pre-cooling strategies were developed, and after comparative analysis, the optimal strategy was obtained and applied to the entire Shenzhen building cluster, with simulation and analysis conducted for the nine administrative districts. Among them, this paper used AutoBPS-DR and added pre-cooling code based on the Ruby language to automatically generate building models with DR strategies and finally simulated the energy consumption results by EnergyPlus. The results showed that a pre-cooling duration of 0.5 h and a change of 2 °C in both pre-cooling temperature and reset temperature was the optimal strategy. Under this strategy, small and medium prototype buildings can achieve better results, with a maximum load reduction of 23.89 W/m² and a reduction rate of 56.82%. In the simulation results of the building cluster, Guangming District showed the best results. Finally, the peak electricity reduction amount and reduction rate of the entire building cluster were calculated to be 0.007 kWh/m² and 21.87%, respectively, with the maximum cost saving and saving percentage of 0.081 CNY/m² and 15.05%, respectively. From this, it can be seen that the Shenzhen building cluster had shown considerable DR potential under the pre-cooling strategy.

In the energy-saving retrofit of existing buildings, investors are particularly concerned about the energy-saving performance of exterior windows and the payback period of additional costs. This study evaluates representative cities in four different climate zones in China to simulate the energy consumption of large office buildings after replacing different glass windows and conducting energy-saving and economic feasibility assessments. The research method includes the following steps: First, a baseline model of large office buildings in four cities was established using AutoBPS and OpenStudio. Then, the baseline and retrofit models of replacing glass windows were simulated using the EnergyPlus V9.3.0 to obtain multiple hourly energy consumption results. The commercial electricity and gas prices in the four cities were adjusted to calculate the total cost within 20 years after replacing different types of windows. Using the discounted payback period (DPP), net present value (NPV), and profitability index (PI) as evaluation indicators, a feasibility analysis was conducted in the four regions to evaluate the economic feasibility of replacing building windows. The simulation results show that considering economic feasibility and meeting energy-saving standards, it is more economical to choose windows with moderate U-value and SHGC value in the four regions than to choose windows with the smallest U-value and SHGC value, and that both energy savings and economic benefits are closely related to building age, with older buildings (especially those in Changsha and Shenzhen) showing greater benefits. Furthermore, the optimal window types in the four cities determined in this study can recover the investment cost within the window life, with Harbin (SC), Beijing (C), Changsha (HC), and Shenzhen (HW) with the payback period of 6.60, 15.66, 10.16, and 11.42 years, respectively. The research model established in this study provides a useful evaluation path for selecting windows for the energy-saving retrofit of large office buildings in cities in different climate zones and provides data support for the decision making of energy-saving retrofit investors.