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A research team at the Carnegie Mellon University Center for Building Performance and Diagnostics has developed a Smart Surfaces Guidebook for city policymakers to use as a decision-making guide. Rapid urbanization is replacing natural land with dark, impervious surfaces. This has led to dire urban consequences including rising temperatures and stormwater deluge, resulting in significantly higher energy costs, greater stormwater damage, and associated health and comfort impacts. These issues can be mitigated using smart surfaces, those with high reflectivity and permeability, which can achieve sustainable and regenerative cities. The current literature on the benefits of urban surfaces is very segmented, focusing on either one specific surface type or one property of surfaces. A smart surface taxonomy with correlated heat and water metrics has been developed to fill this gap. A range of city surfaces in three broad categories - roofs, streets and sidewalks, and parking lots - have been identified with various levels of reflectivity, and permeability. The development of a smart surface taxonomy with quantified benefits for mitigating or adapting to climate change will be critical for decision-makers to make informed decisions on city surface choices. The Guidebook includes real-world case studies that had applied Smart Surfaces strategies, an introduction to the Smart Surfaces Taxonomy with appealing graphics in surface details, and various Smart Surfaces investment scenarios for policymakers to consider.

Rapid urbanization is replacing natural land with dark, impervious surfaces. This has led to dire urban consequences including rising temperatures and stormwater deluge, resulting in significantly higher energy costs, greater stormwater damage, and associated health and comfort impacts. These issues can be mitigated using smart surfaces, those with high reflectivity and permeability, which can achieve sustainable and regenerative cities. The current literature on the benefits of urban surfaces is very segmented, focusing on either one specific surface type or one property of surfaces. A smart surface taxonomy with correlated heat, and water metrics has been developed to fill this gap. A range of city surfaces in three broad categories - roofs, streets and sidewalks, and parking lots - have been identified with various levels of reflectivity, permeability. Through literature review, the taxonomy reveals surface temperatures that range from 29.7°C for a green roof to 74.3°C for a black roof. Also, the taxonomy reveals Rainfall retention potential ranging from 1.27 mm for impervious pavement to 86.4 mm for bioswales. The development of a smart surface taxonomy with quantified benefits for mitigating or adapting to climate change will be critical for decision-makers to make informed decisions on city surface choices.

This paper explores the role of international partnerships to facilitate low-energy building design, construction, and operations. We present the strategic approach, joint research and development outcomes, and implementation activities of a unique U.S.-India program on buildings energy efficiency, the Center for Building Energy Research and Development. We discuss the collaboration successes in both countries despite their dissimilar building contexts, implementation challenges and opportunities. We highlight a range of R&D outcomes, such as novel tools and technologies developed and tested by the joint teams, with their technical energy savings potential, as well as results of capacity building and technology demonstrations. A deep-dive into key new scientific methods around building energy monitoring and benchmarking that could have a significant impact on high-performanceof buildings in both countries is also provided. Finally, in addition to joint R&D successes, pathways to deployment, and lessons learned are discussed as key takeaways.

Recently mobile Indoor Environmental Quality (IEQ) evaluation has gained increasing interest. Hundreds of mobile apps have been developed in the market which run on smartphones and tablet PCs to support simplified IEQ assessment. Simplified IEQ tools that combine simple measurement instruments with user surveys can provide a statistically significant insight into IEQ conditions at a fraction of the cost of complex field instrumentation, while still providing the first tier of evaluation critical to field evaluation of indoor environments. We pilot tested simplified IEQ toolkit with the comparison of the robust sensors. Six simplified thermal, air, visual and acoustic sensors were tested in the post occupancy evaluation in the office building in Pittsburgh, PA. For a comparison, National Environmental Assessment Toolkit (NEAT) developed by Center for Building Performance and Diagnostics (CBPD) at Carnegie Mellon University (CMU) and Brüel & Kjær 2250 acoustic meter were utilized. The result showed that there’s no significant difference in CO2, air temperature, and acoustic level. However, the relative humidity (%) and illuminance level (lx) measurements from the simplified IEQ assessment were not adequate in terms of sensor accuracy and consistency.

Plug load energy consumption represents up to 40% of the total energy consumption in efficient buildings. Occupant behavior has a huge impact on plug load energy consumption. Studies have shown that up to 40% plug load energy savings can be achieved through users’ engagement in sustainable behaviors. To save energy from plug loads, either a company can replace their appliances with more efficient ones, or an occupant can change his/her behavior to control them more efficiently. Existing metrics such as Energy Use Intensity (EUI) cannot sufficiently evaluate behavioral impacts on energy use. To address this issue, a set of Metrics of Sustainability is developed for an Intelligent Dashboard for Occupant (ID-O) to integrate both company policy and individual behavior towards sustainable practices in office spaces. Through the ID-O, occupants can control their appliances and monitor energy consumption. The Metrics include (1) appliance selection, (2) effectiveness of occupant behavior, and (3) intensity of useful energy consumption. Occupancy status is one important input factor for the effectiveness determination. An occupancy detection method is proposed by mining the office appliance energy consumption data. The Metrics of Sustainability and method for occupancy detection are tested at two office buildings in Pittsburgh, PA. The results suggest that, (1) the Metrics are novel, practical and unbiased to evaluate occupants’ behavioral impact on plug load energy; (2) 23% of energy savings were achieved by integrating the Metrics of Sustainability with the ID-O during the study period; and (3) the occupancy detection method is feasible for use in office spaces.

This paper presents how a hydraulic system can be properly modeled for hydraulic balancing, knowledge of flow distribution, coupled simulation, and evaluation of control, etc. It focuses on water-based heating and cooling systems, which generally have high energy efficiency in design but could perform poorly in reality due to the undersensing condition and strong thermal-hydraulic coupling. The study introduces the motivation, presents the simplified modeling methodology, and illustrates the model and simulating structure. A preliminary evaluation of the method is conducted with two simple simulations. The proposed “node-branch-state” modeling approach could be easily modified, expanded and integrated into a detailed thermal model. The paper concludes with some discussions on future work.

School facilities where both students and teachers struggle with such issues as noise, poor indoor air quality, poor lighting and even physical security concerns is unlikely to be conducive for learning and teaching. The total deferred maintenance of schools in the United States was estimated at $254.6 billion in 2008. With over 94,000 public elementary, middle and high schools being attended by more than 50 million students, there is need to implement an effective method for estimating the adequate amount of investment for facility maintenance. Earlier methodologies were evaluated and a new plant value model was developed. The model also introduces a commensurate increase in annual budgets to address maintenance backlog, as well as strategies for setting maintenance priorities. Finally, to effectively maintain building conditions, appropriate custodial and maintenance staffing is analyzed for school facilities. Establishing appropriate annual maintenance budgets for school buildings, including the resources necessary to address accumulated maintenance backlog is critical for upgrading school facilities to adequate conditions for ensuring the health and performance of US teachers and students.

Model predictive control (MPC) has been studied in the building science realm for about three decades. However, the following two aspects of the building control have not been studied thoroughly in MPC research. One is the impact of the mixed-mode cooling system on the active heating ventilation and air conditioning (HVAC) energy consumption, and the other is the differences of individual thermal comfort preference and its impact on energy. This paper proposes an occupant-oriented mixed-mode EnergyPlus predictive control system to optimize HVAC energy consumption while meeting the individual thermal comfort preference. A web-based dashboard is implemented in the test-bed building for three months to collect individual thermal comfort preference data. The data analysis results suggest that occupants have various tolerances and preferences about thermal comfort. The simulation results show that, during one week of a typical swing season, the mixed-mode system further reduces the active HVAC energy consumption, and the diversified occupant thermal comfort preference has significant impact on HVAC energy consumption.

Model predictive control (MPC) has been studied in the building science realm for about three decades. However, the following two aspects of the building control have not been studied thoroughly in MPC research. One is the impact of the mixed-mode cooling system on the active heating ventilation and air conditioning (HVAC) energy consumption, and the other is the differences of individual thermal comfort preference and its impact on energy. This paper proposes an occupant-oriented mixed-mode EnergyPlus predictive control system to optimize HVAC energy consumption while meeting the individual thermal comfort preference. A web-based dashboard is implemented in the test-bed building for three months to collect individual thermal comfort preference data. The data analysis results suggest that occupants have various tolerances and preferences about thermal comfort. The simulation results show that, during one week of a typical swing season, the mixed-mode system further reduces the active HVAC energy consumption, and the diversified occupant thermal comfort preference has significant impact on HVAC energy consumption.