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To promote energy-efficient operations in residential and office buildings, non-intrusive load monitoring (NILM) techniques have been proposed to infer the fine-grained power consumption and usage patterns of appliances from power-line measurement data. Fine-grained monitoring of everyday appliances (such as toasters and coffee makers) can not only promote energy-efficient building operations, but also provide unique insights into the context and activities of individuals. Current building-level NILM techniques are unable to identify the consumption characteristics of relatively low-load appliances, whereas smart-plug based solutions incur significant deployment and maintenance costs. In this paper, we investigate an intermediate architecture, where smart circuit breakers provide measurements of aggregate power consumption at room (or section) level granularity. We then investigate techniques to identify the usage and energy consumption of individual appliances from such measurements. We first develop a novel correlation-based approach called CBPA to identify individual appliances based on both their unique transient and steady-state power signatures. While promising, CBPA fails when the set of candidate appliances is too large. To further improve the accuracy of appliance level usage estimation, we then propose a hybrid system called AARPA, which uses mobile sensing to first infer high-level activities of daily living (ADLs), and then uses knowledge of such ADLs to effectively reduce the set of candidate appliances that potentially contribute to the aggregate readings at any point. We evaluate two variants of this algorithm, and show, using real-life data traces gathered from 10 domestic users, that our fusion of mobile and power-line sensing is very promising: it identified all devices that were used in each data trace, and it identified the usage duration and energy consumption of low-load consumer appliances with 87% accuracy.
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Non-Intrusive Air Leakage Detection in Residential Homes
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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- Award ID(s):
- 1544687
- PAR ID:
- 10073172
- Date Published:
- Journal Name:
- ICDCN '18 Proceedings of the 19th International Conference on Distributed Computing and Networking
- Page Range / eLocation ID:
- 1 to 10
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Providing itemized energy consumption in a utility bill is becoming a priority, and perhaps a business practice in the near term. In recent times, a multitude of systems have been developed such as smart plugs, smart circuit breakers etc., for non-intrusive load monitoring (NILM). They are integrated either with the smart meters or at the plug-levels to footprint appliance-level energy consumption patterns in an entire home environment While deploying the existing technologies in a single home is feasible, scaling these technological advancements across thousands of homes in a region is not realized yet. This is primarily due to the cost, deployment complexity, and intrusive nature associated with these types of real deployment. Motivated by these shortcomings, in this paper we investigate the first step to address scalable disaggregation by proposing a disaggregation mechanism that works on a large dataset to accurately deconstruct the cumulative signals. We propose an iterative noise separation based approach to perform energy disaggregation using sparse coding based methodologies which work at the single ingress point of a home, i.e., at the meter level. We performed a ranked iterative signal removal methodology that effectively isolates appliances' individual signal waveform as noise on an aggregate energy datasets with moderate granularity (1 min). We performed experiments on real dataset and obtained approximately 94% energy disaggregation, i.e., disaggregated appliance-wise signal estimation accuracy.more » « less
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Abstract As regional grids increase penetrations of variable renewable electricity (VRE) sources, demand-side management (DSM) presents an opportunity to reduce electricity-related emissions by shifting consumption patterns in a way that leverages the large diurnal fluctuations in the emissions intensity of the electricity fleet. Here we explore residential precooling, a type of DSM designed to shift the timing of air-conditioning (AC) loads from high-demand periods to periods earlier in the day, as a strategy to reduce peak period demand, CO2emissions, and residential electricity costs in the grid operated by the California Independent System Operator (CAISO). CAISO provides an interesting case study because it generally has high solar generation during the day that is replaced by fast-ramping natural gas generators when it drops off suddenly in the early evening. Hence, CAISO moves from a fleet of generators that are primarily clean and cheap to a generation fleet that is disproportionately emissions-intensive and expensive over a short period of time, creating an attractive opportunity for precooling. We use EnergyPlus to simulate 480 distinct precooling schedules for four single-family homes across California’s 16 building climate zones. We find that precooling a house during summer months in the climate zone characterizing Downtown Los Angeles can reduce peak period electricity consumption by 1–4 kWh d−1and cooling-related CO2emissions by as much as 0.3 kg CO2 d−1depending on single-family home design. We report results across climate zone and single-family home design and show that precooling can be used to achieve simultaneous reductions in emissions, residential electricity costs, and peak period electricity consumption for a variety of single-family homes and locations across California.more » « less
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Leakages in water distribution networks (WDNs) are estimated to globally cost 39 billion USD per year and cause water and revenue losses, infrastructure degradation, and other cascading effects. Their impacts can be prevented and mitigated with prompt identification and accurate leak localization. In this work, we propose the leakage identification and localization algorithm (LILA), a pressure-based algorithm for data-driven leakage identification and model-based localization in WDNs. First, LILA identifies potential leakages via semi-supervised linear regression of pairwise sensor pressure data and provides the location of their nearest sensors. Second, LILA locates leaky pipes relying on an initial set of candidate pipes and a simulation-based optimization framework with iterative linear and mixed-integer linear programming. LILA is tested on data from the L-Town network devised for the Battle of Leakage Detection and Isolation Methods. Results show that LILA can identify all leakages included in the data set and locate them within a maximum distance of 374 m from their real location. Abrupt leakages are identified immediately or within 2 h, while more time is required to raise alarms on incipient leakages.more » « less
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Indoor air quality (IAQ) is crucial for the health, well-being, and productivity of office occupants. IAQ is strongly influenced by occupancy and the operational mode of the heating, ventilation, and air conditioning (HVAC) system. This study investigates the spatiotemporal variations in ozone (O3) and carbon dioxide (CO2) concentrations throughout the HVAC system of a LEED-certified office building. A four-month field measurement campaign was conducted at the Ray W. Herrick Laboratories, employing an automated multi-point sampling system to monitor O3 and CO2 at eight locations throughout the HVAC system. The objectives of this study are to characterize the spatiotemporal distribution of these gases under different ventilation modes and occupancy levels, and to identify O3 loss mechanisms in the office and its HVAC system. Spatiotemporal variations in O3 and CO2 concentrations were observed throughout the HVAC system. Results indicate that outdoor air exchange rates (AERs) significantly impact indoor O3 levels, with higher AERs resulting in increased indoor O3 but reduced CO2 concentrations. Measurements reveal that HVAC filters and ducts contribute to O3 loss, with up to 18% O3 removal observed in the longest HVAC duct segment. Additionally, occupancy influences O3 deposition onto human skin and clothing surfaces. This research underscores the limitations of ventilation standards that focus only on CO2, highlighting the need for ventilation strategies that consider the effects of occupancy and outdoor AERs on different gases. By integrating multi-point gas sampling into building automation systems, more effective control strategies can be developed to enhance IAQ and occupant health while reducing energy consumption.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3154273.3154345
