Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation最新文献_第4页

Interactive Building Metadata Normalization 交互式建筑元数据规范化

Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation Pub Date : 2019-11-13 DOI: 10.1145/3360322.3360990

Jason Koh, Kuo-Kuang Liang, Yiming Yang, Dezhi Hong, Yuvraj Agarwal, Rajesh E. Gupta

引用次数: 1

SemIoTic 符号

Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation Pub Date : 2019-11-13 DOI: 10.1145/3360322.3360996

Sumaya Almanee, Georgios Bouloukakis, Daokun Jiang, Sameera Ghayyur, Dhrubajyoti Ghosh, Peeyush Gupta, Yiming Lin, Sharad Mehrotra, Primal Pappachan, Eun-Jeong Shin, N. Venkatasubramanian, Guoxi Wang, Roberto Yus

引用次数: 2

Smart Surface Classification for Accessible Routing through Built Environment: A Crowd-sourced Approach 建筑环境中可达路由的智能表面分类:一种众包方法

Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation Pub Date : 2019-11-13 DOI: 10.1145/3360322.3360863

Md Osman Gani, V. Raychoudhury, Janick Edinger, Valeria Mokrenko, Zheng Cao, Ce Zhang

{"title":"Smart Surface Classification for Accessible Routing through Built Environment: A Crowd-sourced Approach","authors":"Md Osman Gani, V. Raychoudhury, Janick Edinger, Valeria Mokrenko, Zheng Cao, Ce Zhang","doi":"10.1145/3360322.3360863","DOIUrl":"https://doi.org/10.1145/3360322.3360863","url":null,"abstract":"In order to provide individuals with restricted mobility the opportunity to travel more efficiently, various systems have proposed modeling techniques and routing algorithms that handle accessible navigation through the built environment which is otherwise dotted with mobility barriers. Such systems use data gathered from smartphone sensors or crowd-sourcing to pinpoint the location of the barriers as well as the facilities, such as crosswalks with traffic signals or access ramps to curbs. Though the previous works have identified the type of surface and incline to be important features to determine accessibility, no extensive empirical research exists on how these parameters affect navigation. In order to address this problem, we propose to build a novel system called WheelShare, which uses machine learning to classify surfaces into accessible or otherwise and uses that knowledge to generate accessible routes for wheelchair users. We have trained our system with accelerometer and gyroscope data obtained from 26 different surfaces found frequently in indoor and outdoor environments across Europe and USA. More data is collected by the system through crowd-sourcing based contribution from interested users. Our evaluation shows that WheelShare can achieve an accuracy of up to 96% in identifying surfaces in one of the 5 different accessibility classes. Overall, WheelShare is a novel, scalable and data-centric approach to objectively identify the accessible features of a surface and can generate end-to-end routes for wheelchair users using frequently updated crowd-sourced information.","PeriodicalId":128826,"journal":{"name":"Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","volume":"12 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":"130377219","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: 10

Predicting Impact of Cooling Set-Point Change on Demand Reduction in Real-time 实时预测冷却设定值变化对需求减少的影响

Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation Pub Date : 2019-11-13 DOI: 10.1145/3360322.3361005

Manasa Lingamallu, V. Garg

{"title":"Predicting Impact of Cooling Set-Point Change on Demand Reduction in Real-time","authors":"Manasa Lingamallu, V. Garg","doi":"10.1145/3360322.3361005","DOIUrl":"https://doi.org/10.1145/3360322.3361005","url":null,"abstract":"Based on recent strategies in peak demand reduction for HVAC systems, simple measures like increasing cooling set-point temperatures serves as an effective Demand Response (DR). Majority of the past studies in demand response focus majorly on developing strategies that reduce peak demand and on demand side energy management to optimize energy consumption with the help of renewable energy resources. Research in estimating the potential of DR programs is required and is gaining momentum. It is essential to develop reliable estimation models that can be applied in real-time. We therefore focus on developing a model that predicts the impact of change in HVAC set-point temperature on cooling energy demand. During model evaluation, we made an observation that after a DR event when the set-points are back to normal schedule, sudden and rapid peaks occur in the demand while it is ramping up as set-point temperatures are reduced. For buildings which have a prescribed demand limit, these peaks cause huge demand penalty. We further propose a strategy to enable a stable ramping up process.","PeriodicalId":128826,"journal":{"name":"Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","volume":"480 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":"133748279","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

Machine Learning-based Irrigation Control Optimization 基于机器学习的灌溉控制优化

Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation Pub Date : 2019-11-13 DOI: 10.1145/3360322.3360854

A. Murthy, Curtis E. Green, R. Stoleru, S. Bhunia, C. Swanson, Theodora Chaspari

{"title":"Machine Learning-based Irrigation Control Optimization","authors":"A. Murthy, Curtis E. Green, R. Stoleru, S. Bhunia, C. Swanson, Theodora Chaspari","doi":"10.1145/3360322.3360854","DOIUrl":"https://doi.org/10.1145/3360322.3360854","url":null,"abstract":"Irrigation schedules on traditional irrigation controllers tend to disperse too much water by design and cause runoff, which results in wastage of water and pollution of water sources. Previous attempts at tackling this problem either used expensive sensors or ignored site-specific factors. In this paper, we propose Weather-aware Runoff Prevention Irrigation Control (WaRPIC), a low-cost, practical solution that optimally applies water, while preventing runoff for each sprinkler zone. WaRPIC involves homeowner-assisted data collection on the landscape. The gathered data is used to build site-specific machine learning models that can accurately predict the Maximum Allowable Runtime (MAR) for each sprinkler zone given weather data obtained from the nearest weather station. We have also developed a low-cost module that can retrofit irrigation controllers in order to modify its irrigation schedule. We built a neural network-based model that predicts the MAR for any set of antecedent conditions. The model's prediction is compared with a state-of-the-art irrigation controller and the volume of water wasted by WaRPIC is only 2.6% of that of the state-of-the-art. We have deployed our modules at residences and estimate that the average homeowner can save 38,826 gallons of water over the course of May-Oct 2019, resulting in savings of $192.","PeriodicalId":128826,"journal":{"name":"Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","volume":"8 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":"133913487","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: 13

Reduced state space and cost function in reinforcement learning for demand response control of multiple EV charging stations 基于强化学习的多充电站需求响应控制的简化状态空间和成本函数

Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation Pub Date : 2019-11-13 DOI: 10.1145/3360322.3360992

Manu Lahariya, Nasrin Sadeghianpourhamami, Chris Develder

引用次数: 7

An Improved API and User Experience for the Mortar Testbed 改进的迫击炮试验台API和用户体验

Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation Pub Date : 2019-11-13 DOI: 10.1145/3360322.3361008

Gabe Fierro, D. Culler

引用次数: 0

RemedioT RemedioT

Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation Pub Date : 2019-11-13 DOI: 10.1145/3360322.3360837

Renju Liu, Ziqi Wang, L. Garcia, M. Srivastava

{"title":"RemedioT","authors":"Renju Liu, Ziqi Wang, L. Garcia, M. Srivastava","doi":"10.1145/3360322.3360837","DOIUrl":"https://doi.org/10.1145/3360322.3360837","url":null,"abstract":"The increasing complexity and ubiquity of using IoT devices exacerbate the existing programming challenges in smart environments such as smart homes, smart buildings, and smart cities. Recent works have focused on detecting conflicts for the safety and utility of IoT applications, but they usually do not emphasize any means for conflict resolution other than just reporting the conflict to the application user and blocking the conflicting behavior. We propose RemedIoT, a remedial action 1 framework for resolving Internet-of-Things conflicts. The RemedIoT framework uses state of the art techniques to detect if a conflict exists in a given set of distributed IoT applications with respect to a set of policies, i.e., rules that define the allowable and restricted state-space transitions of devices. For each identified conflict, RemedIoT will suggest a set of remedial actions to the user by leveraging RemedIoT's programming abstractions. These programming abstractions enable different realizations of an IoT module while safely providing the same level of utility, e.g., if an air-conditioner application that is used to implement a cooling module conflicts with a CO2 monitor application that requires ventilation at home, a non-conflicting smart fan application will be suggested to the user. We evaluate RemedIoT on Samsung SmartThings applications and IFTTT applets and show that for 102 detected conflicts across 74 sample applications with 11 policies, RemedIoT is able to remediate ~ 80% of the conflicts found in the environment, which would normally be blocked by prior solutions. We further demonstrate the efficacy and scalability of our approach for smart city environments.","PeriodicalId":128826,"journal":{"name":"Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","volume":"39 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":"132305179","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: 20

On Electrical Load Disaggregation using Recurrent Neural Networks 基于递归神经网络的电力负荷分解研究

Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation Pub Date : 2019-11-13 DOI: 10.1145/3360322.3361002

R. Gopu, Anusha Gudimallam, Vishnu Brindavanam, N. Thokala, M. Chandra

{"title":"On Electrical Load Disaggregation using Recurrent Neural Networks","authors":"R. Gopu, Anusha Gudimallam, Vishnu Brindavanam, N. Thokala, M. Chandra","doi":"10.1145/3360322.3361002","DOIUrl":"https://doi.org/10.1145/3360322.3361002","url":null,"abstract":"In a residential setting, Load disaggregation (LD) is about obtaining appliance-specific operational details in terms of time and power consumption by processing aggregate power consumption data. The disaggregated load information helps utilities to categorize customers based on their usage patterns, facilitating optimal demand response design. Further, LD helps customers to know about their energy-consuming behavior, which is beneficial in reducing the consumption. To be able to provide appliance-specific consumption patterns for aforementioned goals, apart from accurate load identification, estimates of energy consumption of appliances of interest are necessary. In short, it is essential to cull out operational waveform of each of the requisite appliance. Towards this end, very few results have been reported in the literature related to estimating the operational wave-forms, even for large power consuming appliances. In this work, we address this problem using a deep-learning architecture with Recurrent Neural Networks (RNN) variants like Long-Short Term Memory networks (LSTM) and Generalized Recurrent Unit networks (GRU). In addition, a simple but effective technique in pre-processing of the aggregated data is proposed and implemented to identify and reconstruct the consumption pattern of low-power consuming appliances like Refrigerator.","PeriodicalId":128826,"journal":{"name":"Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","volume":"26 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":"115321212","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: 3

Enforcing accountability in Smart built-in IoT environment using MUD 使用MUD在智能内置物联网环境中加强问责制

Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation Pub Date : 2019-11-13 DOI: 10.1145/3360322.3361004

Poonam Yadav, Vadim Safronov, R. Mortier

引用次数: 11