Air conditioning (AC) accounts for a critical portion of the global energy consumption. To improve its energy performance, it is important to fairly benchmark its energy performance and provide the evaluation feedback to users. However, this task has not been well tackled in the residential sector. In this paper, we propose a data-driven approach to fairly benchmark the AC energy performance of residential rooms. First, regression model is built for each benchmarked room so that its power consumption can be predicted given different weather conditions and AC settings. Then, all the rooms are clustered based on their areas and usual AC temperature set points. Lastly, within each cluster, rooms are benchmarked based on their predicted power consumption under uniform weather conditions and AC settings. A real-world case study was conducted with data collected from 44 residential rooms. Results show that the constructed regression models have an average prediction accuracy of 85.1% in cross-validation tests, and support vector regression with Gaussian kernel is the overall most suitable model structure for building the regression model. In the clustering step, 44 rooms are successfully clustered into seven clusters. By comparing the benchmarking scores generated by the proposed approach with two sets of scores computed from historical power consumption data, we demonstrate that the proposed approach is able to eliminate the influences of room areas, weather conditions, and AC settings on the benchmarking results. Therefore, the proposed benchmarking approach is valid and fair. As a by-product, the approach is also shown to be useful to investigate how room areas, weather conditions, and AC settings affect the AC power consumption of rooms in real life.
Bayesian Learning of Consumer Preferences for Residential Demand Response
V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences（俄罗斯科学院V.A.特拉佩兹尼科夫控制科学研究所）；Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究所）；National Research University Higher School of Economics（国家研究型高等经济学院）
This work investigates the extent to which phase change material (PCM) in the building's envelope can be used as an alternative to battery storage systems to increase self-consumption of rooftop solar photovoltaic (PV) generation. In particular, we explore the electricity cost-savings and increase in PV self-consumption that can be achieved by using PCMs and the operation of the heating, ventilation, and air conditioning (HVAC) system optimised by a home energy management system (HEMS). In more detail, we consider a HEMS with an HVAC system, rooftop PV, and a PCM layer integrated into the buil…
AtomBench: A Benchmarking Framework for Generative Crystal Reconstruction Models in Conventional Superconductors
Department of Physics and Astronomy, West Virginia University（物理与天文学系，西弗吉尼亚大学）；Department of Materials Science and Engineering, Whiting School of Engineering, The Johns Hopkins University（材料科学与工程系，韦廷工程学院，约翰霍普金斯大学）
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
Heating, ventilating, and air-conditioning (HVAC) systems consume a large amount of energy in residential houses and buildings. Effective energy management of HVAC is a cost-effective way to improve energy efficiency and reduce the energy cost of residential users. This work develops a novel distributed method for the residential transactive energy system that enables multiple users to interactively optimize their energy management of HVAC systems and behind-the-meter batteries. Specifically, this method effectively reduces the cost of smart homes by employing energy trading among users to lev…
In this paper, we consider the problem of controlling power consumption dynamics of residential heating, ventilation and air conditioning (HVAC) units so that they follow the grid-side power specifications. In order to do so, we design a novel dynamical energy controller which ensures regulation of the cumulative effects of power imbalances. For this, we derive a novel energy-based model that relates the HVAC physics-based dynamics to both real and reactive power balance at the point of interconnection with the grid. In contrast to several other approaches in the literature, we show that a lim…
