Efficient electric heat pumps that replace fossil-fueled heating systems could significantly reduce greenhouse gas emissions. However, electric heat pumps can sharply increase electricity demand, causing high utility bills and stressing the power grid. Residential neighborhoods could see particularly high electricity demand during cold weather, when heat demand rises and heat pump efficiencies fall. This paper presents the development and field demonstration of a predictive control system for an air-to-air heat pump with backup electric resistance heat. The control system adjusts indoor temperature set-points based on weather forecasts, occupancy conditions, and data-driven models of the building and heating equipment. Field tests from January to March of 2023 in an occupied, all-electric, 208 m^2 detached single-family house in Indiana, USA, included outdoor temperatures as low as -15 C. On average over these tests, the control system reduced daily heating energy use by 19% (95% confidence interval: 13--24%), energy used for backup heat by 38%, and the frequency of using the highest stage (19 kW) of backup heat by 83%. Concurrent surveys of residents showed that the control system maintained satisfactory thermal comfort. The control system could reduce the house's total annual heating costs by about $300 (95% confidence interval: 23--34%). These real-world results could strengthen the case for deploying predictive home heating control, bringing the technology one step closer to reducing emissions, utility bills, and power grid impacts at scale.
A Multi-model and Multi-scenario Assessment of the Impact of Climate Change on the Heating and Cooling Load Components of an Archetypical Residential Room in Major Indian Cities
Personalized local heating neutralizing individual, spatial and temporal thermo-physiological variances in extreme cold environments
在本文中，我们调查的可行性，鲁棒性和优化引入个人舒适系统（PCS），设备，承诺在节能和舒适性的改善，到更广泛的环境。我们报告了一系列的实验室实验，系统地研究个性化加热在中和个人，空间和时间变化的热需求的影响。实验在-15摄氏度的人工气候室中进行，以模拟极端寒冷的环境。我们开发了一种20块20 * 20 cm 2发热布（分为9个区域）全面覆盖人体的发热衣。服装的表面温度可以独立控制，快速（20秒内），精确（1摄氏度内）和容易（通过平板电脑）高达45摄氏度。参与者被要求根据自己的喜好调节每个部分的表面温度，并收集他们的生理，心理和调节数据。我们发现，主动加热可以显着和稳定地提高热满意度。在自我调整阶段，总TSV和TCV分别提高了1.50和1.53。不同区段的优选加热表面温度变化很大。此外，即使是同一部分，参与者之间的个体差异也相当大。这种差异是通过局部加热功率观察到的，而在热感知投票中并不明显。换句话说，所有这些不同的差异都可以在个性化调整的灵活性下被中和。我们的研究重申了“自适应热舒适”的范式，并将促进以人为本的设计创新，以实现更高效的PCS。
Comparative Field Deployment of Reinforcement Learning and Model Predictive Control for Residential HVAC
College of Engineering, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213, USA；Wilton E. Scott Institute for Energy Innovation, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213, USA；Center for High Performance Buildings, Purdue University, 177 S Russell St, West Lafayette, IN 47907, USA；Trane Technologies, Residential R\&D Group, 6200 Troup Hwy, Tyler, TX 75707, USA；Bosch Center for Artificial Intelligence
