Air leakages pose a major problem in both residential and commercial buildings. They increase the utility bill and result in excessive usage of Heating Ventilation and Air Conditioning (HVAC) systems, which impacts the environment and causes discomfort to residents. Repairing air leakages in a building is an expensive and time intensive task. Even detecting the leakages can require extensive professional testing. In this paper, we propose a method to identify the leaky homes from a set, provided their energy consumption data is accessible from residential smart meters. In the first phase, we employ a Non-Intrusive Load Monitoring (NILM) technique to disaggregate the HVAC data from total power consumption for several homes. We propose a recurrent neural network and a denoising autoencoder based approach to identify the 'ON' and 'OFF' cycles of the HVACs and their overall usages. We categorize the typical HVAC consumption of about 200 homes and any probable insulation and leakage problems using the Air Changes per Hour at 50 Pa (ACH50) metric in the Dataport datasets. We perform our proposed NILM analysis on different granularities of smart meter data such as 1 min, 15 mins, and 1 hour to observe its effect on classifying the leaky homes. Our results show that disaggregation can be used to identify the residential air-conditioning, at 1 min granularity which in turn helps us to identify the leaky potential homes, with an accuracy of 86%.
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WattScale: A Data-driven Approach for Energy Efficiency Analytics of Buildings at Scale
Buildings consume over 40% of the total energy in modern societies, and improving their energy efficiency can significantly reduce our energy footprint. In this article, we present WattScale, a data-driven approach to identify the least energy-efficient buildings from a large population of buildings in a city or a region. Unlike previous methods such as least-squares that use point estimates, WattScale uses Bayesian inference to capture the stochasticity in the daily energy usage by estimating the distribution of parameters that affect a building. Further, it compares them with similar homes in a given population. WattScale also incorporates a fault detection algorithm to identify the underlying causes of energy inefficiency. We validate our approach using ground truth data from different geographical locations, which showcases its applicability in various settings. WattScale has two execution modes—(i) individual and (ii) region-based, which we highlight using two case studies. For the individual execution mode, we present results from a city containing >10,000 buildings and show that more than half of the buildings are inefficient in one way or another indicating a significant potential from energy improvement measures. Additionally, we provide probable cause of inefficiency and find that 41%, 23.73%, and 0.51% homes have poor building envelope, heating, and cooling system faults, respectively. For the region-based execution mode, we show that WattScale can be extended to millions of homes in the U.S. due to the recent availability of representative energy datasets.
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- NSF-PAR ID:
- 10289974
- Date Published:
- Journal Name:
- ACM/IMS Transactions on Data Science
- Volume:
- 2
- Issue:
- 1
- ISSN:
- 2691-1922
- Page Range / eLocation ID:
- 1 to 25
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract With over half of the world’s population living in cities, there is mounting evidence indicating that investments in urban sustainability can deliver high returns on socioeconomic and environmental fronts. Current scholarship on urban agriculture (UA) reports a wide range of benefits which have been shown to vary with the scale and type of benefit examined. Notably, most city-scale studies do not align benefits of UA with locally meaningful goals. We fill this gap by conducting a city-scale analysis for Phoenix, the fifth largest city in the USA by population, and evaluate these benefits based on their ability to contribute to select desired outcomes specified in Phoenix’s 2050 Sustainability Goals: the elimination of food deserts, provision of green open space, and energy and CO2emissions savings from buildings. We consider three types of surfaces for UA deployment—undeveloped vacant lots, flat rooftops, and building façades—and find that the existing building stock provides 71% of available UA space in the study area. The estimated total food supply from UA is 183 000 tons per year, providing local produce in all existing food deserts of Phoenix, and meeting 90% of current annual consumption of fresh produce based on national per capita consumption patterns. UA would also add green open space and reduce by 60% the number of block groups underserved by public parks. Rooftop deployment of UA could reduce energy use in buildings and has the potential to displace more than 50 000 tons of CO2per year. Our work highlights the importance of combining a data-driven framework with local information to address place-based sustainability goals and can be used as a template for city-scale evaluations of UA in alternate settings.
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3406961
