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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A Scalable Contract Based Approach for Integrating Building Flexibility to Energy Grids
By arbitraging among consumer comfort margins, buildings energy consumption can be changed by providing flexibility to grids. To manipulate the buildings energy consumption, a new contract-based approach to for multi-zone building heating, ventilation and air-conditioning (HVAC) systems is proposed. The approach includes the real-time markets by changing buildings optimal consumption pattern based on triggers sent by the aggregator. Also to decrease the energy consumption of buildings, the user is allowed to select the time-slots and rewards are provided to the user for aggregating flexibility. The aggregator bundles flexibility from the buildings at different time-slots and sells in real-time markets. The idea in aggregator's problem is to maximize aggregator's profits by selling flexibility in real-time markets (RTM) while ensuring the provisioning of flexibility from the buildings through incentives. To address this problem, we formulate it as a distributed optimization problem and then provide a method to solve it which provides good scalability, a requirement for large commercial buildings with multiple zones to participate in RTM. We illustrate the scalability and performance of the contract-based approach and solution technique in a building with 200 zones. Also, user participation based on their time-preferences is included in the proposed optimization. Finally, a scalable technique is shown which can be adopted in existing building automation systems.
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- Award ID(s):
- 1646612
- NSF-PAR ID:
- 10213776
- Date Published:
- Journal Name:
- 2019 18th European Control Conference (ECC)
- Page Range / eLocation ID:
- 2394-2399
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.23919/ECC.2019.8796173
