Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation最新文献

{"title":"City Scale Monitoring of On-Street Parking Violations with StreetHAWK","authors":"A. Ranjan, P. Misra, Arunchandar Vasan, S. Krishnakumar, A. Sivasubramaniam","doi":"10.1145/3360322.3360841","DOIUrl":"https://doi.org/10.1145/3360322.3360841","url":null,"abstract":"Unauthorized parking on city streets is a major contributor to traffic congestion and road accidents in developing nations. Due to the large scale and density of this problem, citywide (manual) monitoring of parking violations has not been effective with existing practices. To this end, we present StreetHAWK: an edge-centric, automated, real-time, privacy-preserving system; which leverages the rear camera of a dashboard mounted smartphone, and performs visual scene and location analytics to identify potential parking violations. We realize this system by overcoming the challenges of: (i) small object identification in various non-standard setups by extensive training on a deep learning based convolution detection model; (ii) limited violation assessment range of 15 m (a constraint of the phone's single camera unit) by augmenting it with a short-term historian and GPS for meeting the 100 m measurement violation guideline; and (iii) erroneous mobile scene analysis instances by lightweight filtering techniques that piggyback on the mobility of the camera and multi-modal sensing clues. The evaluation results obtained from real-world datasets show that StreetHAWK: (i) has three times higher accuracy in identifying small sized objects than other competing embedded detectors; and (ii) localizes these objects from a moving vehicle with a worst-case error of less than 5 m. On-the-road experiments show that StreetHAWK, running at a speed of 5 frames per second (FPS) on a typical Android smartphone, was able to detect (on an average) 80% of the parking violations.","PeriodicalId":128826,"journal":{"name":"Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127107510","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}

{"title":"OccuTherm","authors":"Jonathan Francis, Matias Quintana, Nadine von Frankenberg, Sirajum Munir, M. Berges","doi":"10.1145/3360322.3360858","DOIUrl":"https://doi.org/10.1145/3360322.3360858","url":null,"abstract":"Thermal comfort is a decisive factor for the well-being, productivity, and overall satisfaction of commercial building occupants. Many commercial building automation systems either use a fixed zone-wide temperature set-point for all occupants or they rely on extensive sensor deployments with frequent online interaction with occupants. This results in inadequate comfort levels or significant training effort from users, respectively. However, the increasing ubiquity of cheap, depth-based occupancy tracking systems has enabled an improvement in inferential capabilities. We propose the novel system OccuTherm to model thermal comfort of occupants. We conducted a laboratory study with 77 participants to collect data for the implementation of a thermal comfort model that derives thermal comfort using the human body shape. Based on the comparison with model baselines and ablations, we show that our approach infers thermal comfort of individuals with 60% accuracy when body shape information is taken into account; 6% more than state-of-the-art approaches. We make our code, mobile app, datasets, and models freely available.","PeriodicalId":128826,"journal":{"name":"Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121079921","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}

{"title":"Electrical Load Disaggregation using a two-stage deep learning approach","authors":"Spoorthy Paresh, N. Thokala, M. Chandra","doi":"10.1145/3360322.3361003","DOIUrl":"https://doi.org/10.1145/3360322.3361003","url":null,"abstract":"Electrical Load Disaggregation is an important area of research for demand-side energy management, especially in residential buildings or units. This problem has therefore received significant attention and especially in the context of high-sampled smart meter data, a range of deep-learning based algorithms exist in the literature. However more often than not, learning for these architectures incurs considerable computational costs as models for each appliance need to be learnt separately. Such models have also to be re-trained each time the data changes as the models get fixated to the given aggregate data, irrespective of the size of the latter. We address these problems in this paper by proposing a two-stage learning approach comprised of a) representational learning which learns patterns implicit in the aggregate data in the first stage and, b) a regression technique which uses these representations to regress with the individual appliance class labels. We observe that the proposed architecture is computationally simple which in turn makes it more flexible in handling changes in the smart meter data.","PeriodicalId":128826,"journal":{"name":"Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125341153","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}

{"title":"Beyond a House of Sticks: Formalizing Metadata Tags with Brick","authors":"Gabe Fierro, Jason Koh, Yuvraj Agarwal, Rajesh K. Gupta, D. Culler","doi":"10.1145/3360322.3360862","DOIUrl":"https://doi.org/10.1145/3360322.3360862","url":null,"abstract":"Current efforts establishing semantic metadata standards for the built environment span academia [3], industry [1] and standards bodies [2, 28]. For these standards to be effective, they must be clearly defined and easily extensible, encourage consistency in their usage, and integrate cleanly with existing industrial standards, such as BACnet. There is a natural tension between informal tag-based systems that rely upon idiom and convention for meaning, and formal ontologies amenable to automated tooling. We present a qualitative analysis of Project Haystack [1], a popular tagging system for building metadata, and identify a family of inherent interpretability and consistency issues in the tagging model that stem from its lack of a formal definition. To address these issues, we present the design and implementation of the Brick+ ontology, a drop-in replacement for Brick [3] with clear formal semantics that enables the inference of a valid Brick model from an informal Haystack model, and demonstrate this inference across five Haystack models.","PeriodicalId":128826,"journal":{"name":"Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125654143","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}

{"title":"Electricity Consumption Data Sets: Pitfalls and Opportunities","authors":"Christoph Klemenjak, A. Reinhardt, Lucas Pereira, S. Makonin, M. Berges, W. Elmenreich","doi":"10.1145/3360322.3360867","DOIUrl":"https://doi.org/10.1145/3360322.3360867","url":null,"abstract":"Real-world data sets are crucial to develop and test signal processing and machine learning algorithms to solve energy-related problems. Their scope and data resolution is, however, often limited to the means required to fulfill the experimenters' objectives and moreover governed by personal experience, budgetary and time constraints, and the availability of equipment. As a result, numerous differences between data sets can be observed, e.g., regarding their sampling rates, the number of sensors deployed, their amplitude resolutions, storage formats, or the availability and extent of ground-truth annotations. This heterogeneity poses a significant problem for researchers intending to comparatively use data sets because of the required data conversion, re-sampling, and adaptation steps. In short, there is a lack of widely agreed best practices for designing, deploying, and operating electrical data collection systems. We address this limitation by dissecting the collection methodologies used in existing data sets. By offering recommendations for data collection, data storage, and data provision, we intend to foster the creation of data sets with increased usability and comparability, and thus a greater benefit to the community.","PeriodicalId":128826,"journal":{"name":"Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128355660","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}

{"title":"Solar-TK: A Solar Modeling and Forecasting Toolkit","authors":"Noman Bashir, Dong Chen, David E. Irwin, P. Shenoy","doi":"10.1145/3360322.3361006","DOIUrl":"https://doi.org/10.1145/3360322.3361006","url":null,"abstract":"There has been significant prior work on solar performance modeling and forecasting that infers a site's current and future solar generation based on different factors including a site's location, time, weather, and physical attributes. Unfortunately, much of the prior work is not accessible to researchers, either because it has not been implemented and released as open-source, is too complex and time-consuming to re-implement, or requires access to proprietary data sources. To address the problem, we present Solar-TK, a data-driven toolkit for solar performance modeling and forecasting that is simple, extensible, and publicly accessible. Solar-TK's simple approach models and forecasts a site's solar output given only its location and a small amount of historical generation data. We plan to publicly release Solar-TK as open-source to enable research that requires realistic solar models and forecasts, and to serve as a baseline for comparing new solar modeling and forecasting techniques.","PeriodicalId":128826,"journal":{"name":"Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128569425","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}

{"title":"Future Industrial Kitchen: Challenges and Opportunities","authors":"Lucas Pereira, Vitor Aguiar, Fábio Vasconcelos","doi":"10.1145/3360322.3360872","DOIUrl":"https://doi.org/10.1145/3360322.3360872","url":null,"abstract":"Large amounts of electricity, water, and food are used every day in Industrial Kitchens (IK). Still, very little attention has been devoted by the research community to this potential source of resource over-consumption. This abstract paper builds on the deployment of sensing technology in three IKs to present the main challenges and potential research directions towards more sustainable IKs.","PeriodicalId":128826,"journal":{"name":"Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121507145","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}

{"title":"Sequential Learning with Active Partial Labeling for Building Metadata","authors":"Lu Lin, Zheng Luo, Dezhi Hong, Hongning Wang","doi":"10.1145/3360322.3360866","DOIUrl":"https://doi.org/10.1145/3360322.3360866","url":null,"abstract":"Modern buildings are instrumented with thousands of sensing and control points. The ability to automatically extract the physical context of each point, e.g., the type, location, and relationship with other points, is the key to enabling building analytics at scale. However, this process is costly as it usually requires domain expertise with a deep understanding of the building system and its point naming scheme. In this study, we aim to reduce the human effort required for mapping sensors to their context, i.e., metadata mapping. We formulate the problem as a sequential labeling process and use the conditional random field to exploit the regular and dependent structures observed in the metadata. We develop a suite of active learning strategies to adaptively select the most informative subsequences in point names for human labeling, which significantly reduces the inputs from domain experts. We evaluated our approach on three different buildings and observed encouraging performance in metadata mapping from the proposed solution.","PeriodicalId":128826,"journal":{"name":"Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114984896","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}

{"title":"Building HVAC Scheduling Using Reinforcement Learning via Neural Network Based Model Approximation","authors":"Chi Zhang, S. Kuppannagari, R. Kannan, V. Prasanna","doi":"10.1145/3360322.3360861","DOIUrl":"https://doi.org/10.1145/3360322.3360861","url":null,"abstract":"Buildings sector is one of the major consumers of energy in the United States. The buildings HVAC (Heating, Ventilation, and Air Conditioning) systems, whose functionality is to maintain thermal comfort and indoor air quality (IAQ), account for almost half of the energy consumed by the buildings. Thus, intelligent scheduling of the building HVAC system has the potential for tremendous energy and cost savings while ensuring that the control objectives (thermal comfort, air quality) are satisfied. Traditionally, rule-based and model-based approaches such as linear-quadratic regulator (LQR) have been used for scheduling HVAC. However, the system complexity of HVAC and the dynamism in the building environment limit the accuracy, efficiency and robustness of such methods. Recently, several works have focused on model-free deep reinforcement learning based techniques such as Deep Q-Network (DQN). Such methods require extensive interactions with the environment. Thus, they are impractical to implement in real systems due to low sample efficiency. Safety-aware exploration is another challenge in real systems since certain actions at particular states may result in catastrophic outcomes. To address these issues and challenges, we propose a modelbased reinforcement learning approach that learns the system dynamics using a neural network. Then, we adopt Model Predictive Control (MPC) using the learned system dynamics to perform control with random-sampling shooting method. To ensure safe exploration, we limit the actions within safe range and the maximum absolute change of actions according to prior knowledge. We evaluate our ideas through simulation using widely adopted EnergyPlus tool on a case study consisting of a two zone data-center. Experiments show that the average deviation of the trajectories sampled from the learned dynamics and the ground truth is below 20%. Compared with baseline approaches, we reduce the total energy consumption by 17.1% ~ 21.8%. Compared with model-free reinforcement learning approach, we reduce the required number of training steps to converge by 10x.","PeriodicalId":128826,"journal":{"name":"Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","volume":"1996 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131140703","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}

{"title":"One Size Does Not Fit All: Multi-Scale, Cascaded RNNs for Radar Classification","authors":"Dhrubojyoti Roy, S. Srivastava, Aditya Kusupati, Pranshu Jain, M. Varma, A. Arora","doi":"10.1145/3360322.3360860","DOIUrl":"https://doi.org/10.1145/3360322.3360860","url":null,"abstract":"Edge sensing with micro-power pulse-Doppler radars is an emergent domain in monitoring and surveillance with several smart city applications. Existing solutions for the clutter versus multi-source radar classification task are limited in terms of either accuracy or efficiency, and in some cases, struggle with a trade-off between false alarms and recall of sources. We find that this problem can be resolved by learning the classifier across multiple time-scales. We propose a multi-scale, cascaded recurrent neural network architecture, MSC-RNN, comprised of an efficient multi-instance learning (MIL) Recurrent Neural Network (RNN) for clutter discrimination at a lower tier, and a more complex RNN classifier for source classification at the upper tier. By controlling the invocation of the upper RNN with the help of the lower tier conditionally, MSC-RNN achieves an overall accuracy of 0.972. Our approach holistically improves the accuracy and per-class recalls over machine learning models suitable for radar inferencing. Notably, we outperform cross-domain handcrafted feature engineering with purely time-domain deep feature learning, while also being up to ~3X more efficient than a competitive solution.","PeriodicalId":128826,"journal":{"name":"Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","volume":"12 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123793724","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}