Proceedings of the 5th Conference on Systems for Built Environments最新文献_第2页

Helios: a programmable software-defined solar module Helios:一个可编程软件定义的太阳能模块

Proceedings of the 5th Conference on Systems for Built Environments Pub Date : 2018-11-07 DOI: 10.1145/3276774.3276783

Noman Bashir, David E. Irwin, P. Shenoy

{"title":"Helios: a programmable software-defined solar module","authors":"Noman Bashir, David E. Irwin, P. Shenoy","doi":"10.1145/3276774.3276783","DOIUrl":"https://doi.org/10.1145/3276774.3276783","url":null,"abstract":"The declining cost and rising penetration of solar energy is poised to fundamentally impact grid operations, as utilities must continuously offset, potentially rapid and increasingly large, power fluctuations from highly distributed and \"uncontrollable\" solar sites to maintain the instantaneous balance between electricity's supply and demand. Prior work proposes to address the problem by designing various policies that actively control solar power to optimize grid operations. However, these policies implicitly assume the presence of \"smart\" solar modules capable of regulating solar output based on various algorithms. Unfortunately, implementing such algorithms is currently not possible, as smart inverters embed only a small number of operating modes and are not programmable. To address the problem, this paper presents the design and implementation of a software-defined solar module, called Helios. Helios exposes a high-level programmatic interface to a DC-DC power optimizer, which enables sotware to remotely control a solar module's power output in real time between zero and its current maximum, as dictated by the Sun's position and weather. Unlike current smart inverters, Helios focuses on enabling direct programmatic control of real solar power capable of implementing a wide range of control policies, rather than a few highly-specific operating modes. We evaluate Helios' performance, including its latency, energy usage, and flexibility. For the latter, we implement and evaluate a wide range of solar control algorithms both in the lab, using a solar emulator and programmable load, and outdoors.","PeriodicalId":294697,"journal":{"name":"Proceedings of the 5th Conference on Systems for Built Environments","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130700682","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: 2

Efficient integration of smart appliances for demand response programs 为需求响应程序高效集成智能家电

Proceedings of the 5th Conference on Systems for Built Environments Pub Date : 2018-11-07 DOI: 10.1145/3276774.3276787

M. Afzalan, F. Jazizadeh

{"title":"Efficient integration of smart appliances for demand response programs","authors":"M. Afzalan, F. Jazizadeh","doi":"10.1145/3276774.3276787","DOIUrl":"https://doi.org/10.1145/3276774.3276787","url":null,"abstract":"Power utilities rely on Demand Response (DR) programs in order to shave the peak load at critical times, when there is an excessive demand. In the context of automation, DR programs are categorized as manual or automated. With the emergence of home energy management (HEM) systems that monitor and operate the household appliances, the opportunities for automated DR have emerged. For example, smart appliances with deferrable loads can be scheduled to shift their load without consumers' intervention, given that many consumers might not engage enough to perform the manual DR. However, it has been shown that unjustified load compensation from many HEM-enabled consumers in peak times could result in high off-peak demand. Therefore, it is essential for utilities to identify and target the consumers for participation based on certain criteria. To address this issue, in this paper, we proposed a method for the selection of consumers who are using smart appliances with the highest potential for a DR program. The proposed method measures the (1) frequency, (2) consistency, and (3) peak time usage of deferrable loads across several days. We evaluate our approach on a historical real-world electricity consumption dataset from residential households. The findings demonstrate the efficacy of the proposed method to sort consumers (with the smart appliance) based on their potential to participate in DR.","PeriodicalId":294697,"journal":{"name":"Proceedings of the 5th Conference on Systems for Built Environments","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133431057","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: 8

Web of things based IoT standard interworking test case: demo abstract 基于物联网的物联网标准互联测试用例:演示摘要

Proceedings of the 5th Conference on Systems for Built Environments Pub Date : 2018-11-07 DOI: 10.1145/3276774.3281012

Youngmin Ji, Kisu Ok, Woo Suk Choi

引用次数: 8

Helios 赫利俄斯

Proceedings of the 5th Conference on Systems for Built Environments Pub Date : 2018-11-07 DOI: 10.1163/2589-7802_dddo_dddo_helios

Noman Bashir, David E. Irwin, P. Shenoy

引用次数: 0

Enhancing household-level load forecasts using daily load profile clustering 使用每日负荷概况聚类增强家庭级负荷预测

Proceedings of the 5th Conference on Systems for Built Environments Pub Date : 2018-11-07 DOI: 10.1145/3276774.3276793

E. Barbour, Marta C. González

{"title":"Enhancing household-level load forecasts using daily load profile clustering","authors":"E. Barbour, Marta C. González","doi":"10.1145/3276774.3276793","DOIUrl":"https://doi.org/10.1145/3276774.3276793","url":null,"abstract":"Forecasting the electricity demand for individual households is important for both consumers and utilities due to the increasing decentralized nature of the electricity system. Particularly, utilities often have very little information about their consumers except for aggregate building level loads, without knowledge of interior details about the household appliance sets or occupants. In this paper, we explore the possibility of enhancing the day-ahead load forecasts for hundreds of individual households by clustering their daily load profile history to obtain each consumer's specific typical consumption patterns. The clustering method is based on load profile shape using the Earth Mover's Distance metric to calculate similarity between load profiles. The forecasting methods then predict the next day shape from the empirical probability of previous cluster transitions in the consumer's load history and estimate the magnitude either by using historical load relationships with temperature and forecast temperatures or previous day consumption levels. The generated forecasts are compared to a benchmark Multiple Linear Regression (MLR) day-ahead forecast and persistence forecasts for all individuals. While at the aggregate level the MLR method represents a significant improvement over persistence forecasts, on an individual level we find that the best forecasting model is specific to the individual. In particular, we find that the MLR model produces lower errors when consumers have a consistent daily temperature response and the cluster model with previous day magnitude produces lower errors for consumers whose consumption changes abruptly in magnitude for several days at a time. Our work adds to the state of knowledge surrounding individual household load forecasting and demonstrates the potential for cluster-based methodologies to enhance short term load forecasts.","PeriodicalId":294697,"journal":{"name":"Proceedings of the 5th Conference on Systems for Built Environments","volume":"86 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120874098","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

DeepLane DeepLane

Proceedings of the 5th Conference on Systems for Built Environments Pub Date : 2018-11-07 DOI: 10.1145/3276774.3276791

Ravi Bhandari, A. Nambi, Venkata N. Padmanabhan, B. Raman

{"title":"DeepLane","authors":"Ravi Bhandari, A. Nambi, Venkata N. Padmanabhan, B. Raman","doi":"10.1145/3276774.3276791","DOIUrl":"https://doi.org/10.1145/3276774.3276791","url":null,"abstract":"Current smartphone-based navigation applications fail to provide lane-level information due to poor GPS accuracy. Detecting and tracking a vehicle's lane position on the road assists in lane-level navigation. For instance, it would be important to know whether a vehicle is in the correct lane for safely making a turn, perhaps even alerting the driver in advance if it is not, or whether the vehicle's speed is compliant with a lane-specific speed limit. Recent efforts have used road network information and inertial sensors to estimate lane position. While inertial sensors can detect lane shifts over short windows, it would suffer from error accumulation over time. In this paper we present DeepLane, a system that leverages the back camera of a windshield-mounted smartphone to provide an accurate estimate of the vehicle's current lane. We employ a deep learning based technique to classify the vehicle's lane position. DeepLane does not depend on any infrastructure support such as lane markings and works even when there are no lane markings, a characteristic of many roads in developing regions. We perform extensive evaluation of DeepLane on real world datasets collected in developed and developing regions. DeepLane can detect vehicle's lane position with an accuracy of over 90% in both day and night conditions. We have implemented DeepLane as an Android-app that runs at 5 fps on CPU and upto 15 fps on smart-phone's GPU and can also assist existing navigation applications with lane-level information.","PeriodicalId":294697,"journal":{"name":"Proceedings of the 5th Conference on Systems for Built Environments","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124143211","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: 16

Disaggregating solar generation behind individual meters in real time 将太阳能发电实时分解到各个仪表后面

Proceedings of the 5th Conference on Systems for Built Environments Pub Date : 2018-11-07 DOI: 10.1145/3276774.3276776

Michaelangelo D. Tabone, S. Kiliccote, E. Kara

{"title":"Disaggregating solar generation behind individual meters in real time","authors":"Michaelangelo D. Tabone, S. Kiliccote, E. Kara","doi":"10.1145/3276774.3276776","DOIUrl":"https://doi.org/10.1145/3276774.3276776","url":null,"abstract":"Real-time photovoltaic (PV) generation information is crucial for distribution system operations such as switching, state-estimation, and voltage management. However, most behind-the-meter solar installations are not monitored. Typically, the only information available to the distribution system operator is the installed capacity of solar behind each meter; though in some cases even the presence of solar may be unknown. We present a method for disaggreagating behind-the-meter solar generation using only information that is already available in most distribution systems: advanced metering infrastructure, substation monitoring, and generation monitoring at a few PV systems nearby the circuit. The proposed method accurately predicts which homes have solar in over 90% of cases, and recovers the 15-min resolution PV generation signals with root mean square errors between 20% and 50% of average daily PV generation both historically and real-time. A sensitivity analysis shows the method to be robust to the number of buildings and time span of data used to fit. However including more than 3 solar proxies can cause false positive of PV systems behind meters. We find that the proposed method performs better at homes that export electricity to the grid more often.","PeriodicalId":294697,"journal":{"name":"Proceedings of the 5th Conference on Systems for Built Environments","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126347900","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: 33

A smart surveillance system using visual sensors: poster abstract 一种使用视觉传感器的智能监控系统:海报摘要

Proceedings of the 5th Conference on Systems for Built Environments Pub Date : 2018-11-07 DOI: 10.1145/3276774.3281016

Khaleda Akther Papry, Sharowar Md. Shahriar Khan, Mahmuda Naznin

引用次数: 0

A data-driven system for city-scale personal energy footprint estimations: poster abstract 用于城市规模个人能源足迹估算的数据驱动系统:海报摘要

Proceedings of the 5th Conference on Systems for Built Environments Pub Date : 2018-11-07 DOI: 10.1145/3276774.3281018

Peter Wei, Xiaofan Jiang

引用次数: 2

Scrabble 拼字游戏

Proceedings of the 5th Conference on Systems for Built Environments Pub Date : 2018-11-07 DOI: 10.1145/3276774.3276795

Jason Koh, Bharathan Balaji, Dhiman Sengupta, Julian McAuley, Rajesh E. Gupta, Yuvraj Agarwal

{"title":"Scrabble","authors":"Jason Koh, Bharathan Balaji, Dhiman Sengupta, Julian McAuley, Rajesh E. Gupta, Yuvraj Agarwal","doi":"10.1145/3276774.3276795","DOIUrl":"https://doi.org/10.1145/3276774.3276795","url":null,"abstract":"Interoperability in the Internet of Things relies on a common data model that captures the necessary semantics for vendor independent application development and data exchange. However, traditional systems such as those in building management are vertically integrated and do not use a standard schema. A typical building can consist of thousands of data points. Third party vendors who seek to deploy applications like fault diagnosis need to manually map the building information into a common schema. This mapping process requires deep domain expertise and a detailed understanding of intricacies of each building's system. Our framework - Scrabble - reduces the mapping effort significantly by using a multi-stage active learning mechanism that exploits the structure present in a standard schema and learns from buildings that have already been mapped to the schema. Scrabble uses conditional random fields with transfer learning to represent unstructured building information in a reusable intermediate representation. This reusable representation is mapped to the schema using a multilayer perceptron. Our novel semantic model based active learning mechanism requires only minimal input from domain experts to interpret esoteric, idiosyncratic data points. We have evaluated Scrabble on five buildings with thousands of different entities and our method outperforms prior work by 59%/162% higher Accuracy/Macro-averaged-F1 in a building when 10 examples are provided by an expert in both cases. Scrabble achieves 99% Accuracy with 100--160 examples for buildings with thousands of points while the other baselines cannot.","PeriodicalId":294697,"journal":{"name":"Proceedings of the 5th Conference on Systems for Built Environments","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114845350","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: 30