Airport agencies spend millions of dollars to remove ice and snow from airport pavement surfaces to achieve accessible, safe, and sustainable operations during the winter. Electrically conductive concrete (ECON) based heated pavement system (HPS) has gained attention as a promising alternative technology for preventing snow and ice accumulation by maintaining pavement surface temperatures above the freezing point. The objective of this study was to demonstrate the world’s first full-scale ECON-based HPS at a U.S. airport. Two ECON slabs were designed and constructed in the General Aviation (GA) apron at the Des Moines International Airport (DSM), Iowa in 2016. Systematic design components were identified, and construction procedures were developed and implemented for ECON-based HPS. Using collected sensor data, the performance of the constructed and remotely-operated ECON slabs was evaluated under real weather conditions at DSM in the 2016–2017 winter season. The results demonstrate that ECON-based HPS have promising deicing and anti-icing capacities, promising to provide uniform heat distribution and prevent snow and ice accumulations on the entire area of application under various winter weather conditions, including extreme cold weather (i.e., arctic blasts).

In the U.S., the heating, ventilation, and air conditioning (HVAC) system is generally the largest electricity-consuming end-use in a residential building. However, homeowners are less likely to have their HVAC system serviced regularly, thus inefficiencies in operation are also more likely to occur. To address this challenge, this research works towards a non-intrusive data-driven assessment method using building assessors’ data, HVAC electricity demand data, and outdoor environmental data. Building assessors’ data is first used to estimate the HVAC system size, then estimate the electricity demand curve of the HVAC system. A comparison of the proposed electricity demand curve development method demonstrates strong agreement with physics-based HVAC model results. An HVAC efficiency rating is then proposed, which compares the model-predicted and actual performance data to define whether an HVAC system is operating as expected. As a case study, detailed data for 39 occupied, conditioned residential buildings in Austin, Texas, was used demonstrating the identification of the presence of potential HVAC inefficiencies. The results prove beneficial for utilities to help target residential HVAC systems in need of service or energy efficiency upgrades, as well as for homeowners as a continuous assessment tool for HVAC performance.

This research investigates the demand response potential of four electric grid-connected large residential appliances, including refrigerators, clothes washers, clothes dryers, and dishwashers. Field-collected energy use data from these applinaces in 326 residential buildings is collected for a 1-year period and analyzed. Uncertainty analysis is based on the Monte Carlo simulation method, showing the different possible ranges of demand response for each appliance. Clothes dryers are found to have the strongest demand response potential, due in part to their high power demand when on. However this high peak reduction occurs in the afternoon and evening only, as dryers are found to be used very little in the night and morning. Refrigerators provide the second strongest demand response potential, in part due to the nearly 100% of residential buildings that have one or more of them, and the higher predictability of their electricity demand behavior. In addition, unlike dryers, refrigerators have a similar demnad response potential at all times of the day. Clothes washers and dishwasher were found to have the least demand response potential. Results of this work are intended to provide updated information on power demand and time of use of appliances and a methodology for assessment of demand response and peak load reduction of smart applinaces on a house-by-house or community scale.