Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings最新文献_第2页

Estimation of building occupancy levels through environmental signals deconvolution 利用环境信号反卷积估计建筑物占用水平

Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings Pub Date : 2013-11-11 DOI: 10.1145/2528282.2528290

A. Ebadat, Giulio Bottegal, Damiano Varagnolo, B. Wahlberg, K. Johansson

{"title":"Estimation of building occupancy levels through environmental signals deconvolution","authors":"A. Ebadat, Giulio Bottegal, Damiano Varagnolo, B. Wahlberg, K. Johansson","doi":"10.1145/2528282.2528290","DOIUrl":"https://doi.org/10.1145/2528282.2528290","url":null,"abstract":"We address the problem of estimating the occupancy levels in rooms using the information available in standard HVAC systems. Instead of employing dedicated devices, we exploit the significant statistical correlations between the occupancy levels and the CO2 concentration, room temperature, and ventilation actuation signals in order to identify a dynamic model. The building occupancy estimation problem is formulated as a regularized deconvolution problem, where the estimated occupancy is the input that, when injected into the identified model, best explains the currently measured CO2 levels. Since occupancy levels are piecewise constant, the zero norm of occupancy is plugged into the cost function to penalize non-piecewise constant inputs. The problem then is seen as a particular case of fused-lasso estimator by relaxing the zero norm into the ℓ1 norm. We propose both online and offline estimators; the latter is shown to perform favorably compared to other data-based building occupancy estimators. Results on a real testbed show that the MSE of the proposed scheme, trained on a one-week-long dataset, is half the MSE of equivalent Neural Network (NN) or Support Vector Machine (SVM) estimation strategies.","PeriodicalId":184274,"journal":{"name":"Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127273032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 79

Reduce the Number of Sensors: Sensing Acoustic Emissions to Estimate Appliance Energy Usage 减少传感器的数量:感应声发射来估计家电的能源使用

Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings Pub Date : 2013-11-11 DOI: 10.1145/2528282.2528300

F. Englert, Irina Diaconita, A. Reinhardt, A. Alhamoud, Richard Meister, Lucas Backert, R. Steinmetz

{"title":"Reduce the Number of Sensors: Sensing Acoustic Emissions to Estimate Appliance Energy Usage","authors":"F. Englert, Irina Diaconita, A. Reinhardt, A. Alhamoud, Richard Meister, Lucas Backert, R. Steinmetz","doi":"10.1145/2528282.2528300","DOIUrl":"https://doi.org/10.1145/2528282.2528300","url":null,"abstract":"As a consequence of rising energy prices, manifold solutions to create user awareness for the unnecessary operation of electric appliances have emerged, e.g., real-time consumption displays or timer-based switchable wall outlets. A common attribute of these solutions is the need to buy and install additional hardware, although their acquisition costs often diminish the attainable savings. Furthermore these solutions only permit to retrieve accumulated figures of the energy consumption. Especially in households or office spaces with multiple persons, however, attributing electricity consumption to individuals provides enormous potential to determine possible savings. We therefore propose a system that allows to identify the energy demand incurred by a user's action based on audio recordings using smartphones. More precisely, we capture the user's ambient sounds and applying suitable filtering steps in order to determine the user's current activity. Our results indicate that our system is capable of detecting 16 typical household activities at an accuracy of 92%. By annotating the detectable household activities with information about typical energy consumptions, extracted from 950 real-world power consumption traces, a good estimate of the energy intensity of the users' lifestyles can be made. Our novel personalized energy monitoring system shows people their personal energy consumption, while maintaining their user comfort and relinquishing the need for additional hardware.","PeriodicalId":184274,"journal":{"name":"Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125984356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 15

TOSS: Thermal Occupancy Sensing System 热占用传感系统

Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings Pub Date : 2013-11-11 DOI: 10.1145/2528282.2534155

Varick L. Erickson, Alex Beltran, Daniel A. Winkler, N. P. Esfahani, John R. Lusby, Alberto Cerpa

引用次数: 10

EnergyTrack: Sensor-Driven Energy Use Analysis System EnergyTrack:传感器驱动的能源使用分析系统

Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings Pub Date : 2013-11-11 DOI: 10.1145/2528282.2528289

Deokwoo Jung, V. Krishna, Ngo Quang Minh Khiem, H. Nguyen, David K. Y. Yau

引用次数: 7

Disaggregating End Loads with Energy-Harvesting Sensors and Cloud Analytics 用能量收集传感器和云分析分解终端负载

Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings Pub Date : 2013-11-11 DOI: 10.1145/2528282.2534160

Bradford Campbell, S. DeBruin, Meghan Clark, P. Dutta

{"title":"Disaggregating End Loads with Energy-Harvesting Sensors and Cloud Analytics","authors":"Bradford Campbell, S. DeBruin, Meghan Clark, P. Dutta","doi":"10.1145/2528282.2534160","DOIUrl":"https://doi.org/10.1145/2528282.2534160","url":null,"abstract":"Obtaining a detailed breakdown of household energy consumption would allow occupants to better understand their energy usage patterns and identify opportunities for energy savings. Current solutions are too course-grained, too difficult to deploy, not networked, or offer poor coverage of hard to meter items, such as ceiling lights. To address these problems, we demonstrate a wirelessly networked, energy-harvesting power metering system that draws zero standby power and is power proportional to the load it is metering. The system is comprised of three different meters: one for plugged-in loads, one for panel-level circuits, and one for hard-to-sense loads, such as ceiling lights. Each meter harvests energy proportionally to the load it is measuring and powers a sensor node intermittently. Together, these sensors create multiple data streams which are aggregated by a receiver. When combined with a calibrated meter that measures total household power, our system can iteratively determine the contributions of each load to the total power usage, allowing users to gain a broad yet detailed view of their energy consumption and costs.","PeriodicalId":184274,"journal":{"name":"Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123891819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Occupancy-based heating control for residential buildings using environmental sensors 使用环境传感器的基于占用度的住宅供暖控制

Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings Pub Date : 2013-11-11 DOI: 10.1145/2528282.2528313

Dominic Wörner, Thomas von Bomhard, Felix Wortmann

引用次数: 7

Occupancy inferencing from non-intrusive data sources 从非侵入性数据源推断占用情况

Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings Pub Date : 2013-11-11 DOI: 10.1145/2528282.2528312

Kevin Ting, Richard Yu, M. Srivastava

引用次数: 4

Optimal Personal Comfort Management Using SPOT+ 使用 SPOT+ 实现最佳个人舒适度管理

Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings Pub Date : 2013-11-11 DOI: 10.1145/2528282.2528297

Peter Xiang Gao, S. Keshav

引用次数: 70

Online Learning for Personalized Room-Level Thermal Control: A Multi-Armed Bandit Framework 个性化房间级热控制的在线学习:一个多武装强盗框架

Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings Pub Date : 2013-11-11 DOI: 10.1145/2528282.2528296

Parisa Mansourifard, F. Jazizadeh, B. Krishnamachari, B. Becerik-Gerber

{"title":"Online Learning for Personalized Room-Level Thermal Control: A Multi-Armed Bandit Framework","authors":"Parisa Mansourifard, F. Jazizadeh, B. Krishnamachari, B. Becerik-Gerber","doi":"10.1145/2528282.2528296","DOIUrl":"https://doi.org/10.1145/2528282.2528296","url":null,"abstract":"We consider the problem of automatically learning the optimal thermal control in a room in order to maximize the expected average satisfaction among occupants providing stochastic feedback on their comfort through a participatory sensing application. Not assuming any prior knowledge or modeling of user comfort, we first apply the classic UCB1 online learning policy for multi-armed bandits (MAB), that combines exploration (testing out certain temperatures to understand better the user preferences) with exploitation (spending more time setting temperatures that maximize average-satisfaction) for the case when the total occupancy is constant. When occupancy is time-varying, the number of possible scenarios (i.e., which particular set of occupants are present in the room) becomes exponentially large, posing a combinatorial challenge. However, we show that LLR, a recently-developed combinatorial MAB online learning algorithm that requires recording and computation of only a polynomial number of quantities can be applied to this setting, yielding a regret (cumulative gap in average satisfaction with respect to a distribution aware genie) that grows only polynomially in the number of users, and logarithmically with time. This in turn indicates that difference in unit-time satisfaction obtained by the learning policy compared to the optimal tends to 0. We quantify the performance of these online learning algorithms using real data collected from users of a participatory sensing iPhone app in a multi-occupancy room in an office building in Southern California.","PeriodicalId":184274,"journal":{"name":"Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134271970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 14

EnergyTrack: Sensor-Driven Energy Use Analysis System EnergyTrack:传感器驱动的能源使用分析系统

Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings Pub Date : 2013-11-11 DOI: 10.1145/2528282.2534158

V. Krishna, Deokwoo Jung, Ngo Quang Minh Khiem, H. Nguyen, David K. Y. Yau

引用次数: 12