Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation最新文献_第3页

Control of Air Free-Cooled Data Centers in Tropics via Deep Reinforcement Learning 通过深度强化学习控制热带地区的空气无冷数据中心

Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation Pub Date : 2019-11-13 DOI: 10.1145/3360322.3360845

D. V. Le, Yingbo Liu, Rongrong Wang, Rui Tan, Y. Wong, Yonggang Wen

{"title":"Control of Air Free-Cooled Data Centers in Tropics via Deep Reinforcement Learning","authors":"D. V. Le, Yingbo Liu, Rongrong Wang, Rui Tan, Y. Wong, Yonggang Wen","doi":"10.1145/3360322.3360845","DOIUrl":"https://doi.org/10.1145/3360322.3360845","url":null,"abstract":"Air free-cooled data centers (DCs) have not existed in the tropical zone due to the unique challenges of year-round high ambient temperature and relative humidity (RH). The increasing availability of servers that can tolerate higher temperatures and RH due to the regulatory bodies' prompts to raise DC temperature setpoints sheds light upon the feasibility of air free-cooled DCs in tropics. This paper studies the problem of controlling the temperature and RH of the air supplied to the servers in a free-cooled tropical DC below certain thresholds to maintain servers' computing performance and reliability. To achieve the goal, a portion of the hot air generated by the servers is recirculated and mixed with the fresh outside air to adjust the RH of the supply air. To address the complex psychrometric dynamics, we apply deep reinforcement learning to learn the control policy that aims at minimizing the energy used for moving air and on-demand cooling. Extensive evaluation based on real data traces collected from an air free-cooled testbed and comparisons with hysteresis-based and model-predictive control approaches show the superior performance of our solution.","PeriodicalId":128826,"journal":{"name":"Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","volume":"149 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":"122038929","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: 23

Comparing Gray Box Methods to Derive Building Properties from Smart Thermostat Data 比较灰盒方法从智能温控器数据中获取建筑属性

Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation Pub Date : 2019-11-13 DOI: 10.1145/3360322.3360836

Gaby M. Baasch, A. Wicikowski, Gaëlle Faure, R. Evins

{"title":"Comparing Gray Box Methods to Derive Building Properties from Smart Thermostat Data","authors":"Gaby M. Baasch, A. Wicikowski, Gaëlle Faure, R. Evins","doi":"10.1145/3360322.3360836","DOIUrl":"https://doi.org/10.1145/3360322.3360836","url":null,"abstract":"The development of quantitative techniques for determining the amount of heat lost through the building envelope is essential for targeted retrofits. This type of evaluation is traditionally a resource intensive process that involves onsite appraisal and in-situ measurements. In order to build more efficient and scalable methods for retrofit analysis, new sources of data could be used. Smart thermostat data, for example, provide a valuable resource, however they often lack detailed information about the building characteristics and energy loads. This paper presents and compares three methods for assessing heating characteristics of households using a dataset that does not contain heating power. The three methods are based on: (1) balance point plots, (2) the extraction of indoor temperature decay curves, and (3) the classic differential equation for indoor temperature. These methods all take a gray box approach in which physics-based and machine learning models are combined. The dataset used for this study consists of over 4,000 houses in Ontario and New York. The three methods are applied to each building and the resulting data is analyzed to determine whether the results are statistically sound. It is found that there is a positive linear correlation between characteristics derived for each method, although there is uncertainty about absolute values. This result indicates that the methods can be used to ascertain relative values for the thermal characteristics of a building. The methods suggested in this paper may therefore be used to filter heating profiles to target potential retrofit measures or other stock-level decisions.","PeriodicalId":128826,"journal":{"name":"Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","volume":"24 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":"122213976","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

TripAware TripAware

Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation Pub Date : 2019-11-13 DOI: 10.1145/3360322.3360871

Jesse Zhang, Jack Sullivan, Vasudev Venkatesh P. B., Kyle Tse, Andy Yan, J. Leyden, Kalyanaraman Shankari, R. Katz

{"title":"TripAware","authors":"Jesse Zhang, Jack Sullivan, Vasudev Venkatesh P. B., Kyle Tse, Andy Yan, J. Leyden, Kalyanaraman Shankari, R. Katz","doi":"10.1145/3360322.3360871","DOIUrl":"https://doi.org/10.1145/3360322.3360871","url":null,"abstract":"To combat climate change, we need to change user transportation behavior to be less carbon intensive. Prior work on motivating this behavior change has been predominantly qualitative and lacks comparison. This makes it challenging to determine which interventions should be deployed at scale. The behavior change community needs a process to compare interventions against each other in pilot studies before committing deployment resources. We perform the first quantitative comparison, to our knowledge, of behavior change strategies in the transportation behavior domain. Since this is a pilot with a limited recruitment budget, we design a Randomized Controlled Trial (RCT) using an open source platform. We assign 41 users to three mobile applications: Emotion, Information, Control. The RCT allows us to draw statistically valid inferences that can suggest future avenues for larger-scale studies. We found that Emotion resulted in greater engagement with the application (p=0.006, 0.035, 0.031, 0.040) while Information improved the sustainability of travel behavior (p = 0.043). These exploratory statistical results can motivate the design of future studies to further explore combinations of these approaches for sustainable transportation behavior.","PeriodicalId":128826,"journal":{"name":"Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","volume":"16 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":"122325454","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

Capturing Regularity of ADL Routines Using Hierarchical Clustering Models 利用层次聚类模型捕获ADL例程的规律性

Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation Pub Date : 2019-11-13 DOI: 10.1145/3360322.3361007

P. Mohan, Bogyeong Lee, Theodora Chaspari, C. Ahn

引用次数: 3

A toolkit for low-cost thermal comfort sensing 一个低成本热舒适传感工具包

Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation Pub Date : 2019-11-13 DOI: 10.1145/3360322.3360994

Adam Tyler, Oliver Bates, A. Friday, M. Hazas

引用次数: 0

Exploiting Multiple Receivers for CSI-Based Activity Classification Using A Hybrid CNN-LSTM Model 利用CNN-LSTM混合模型开发基于csi的多接收器活动分类

Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation Pub Date : 2019-11-13 DOI: 10.1145/3360322.3361015

Hoonyong Lee, C. Ahn, Nakjung Choi

{"title":"Exploiting Multiple Receivers for CSI-Based Activity Classification Using A Hybrid CNN-LSTM Model","authors":"Hoonyong Lee, C. Ahn, Nakjung Choi","doi":"10.1145/3360322.3361015","DOIUrl":"https://doi.org/10.1145/3360322.3361015","url":null,"abstract":"Channel State Information (CSI) has been used as an alternative sensing source for monitoring occupant's activities indoors. While various approaches have been proposed to extract features from the CSI and classify activities, those features fail to yield the spatial-temporal aspects of activities. In this context, this study presents new approach to extract appropriate features from multiple receivers. Time-series CSI data collected from a Wi-Fi receiver is converted into an image data by Short-Time Fourier Transform (STFT), and then such the image data from multiple receivers are combined into a large image data, so that it contains information about the spatial-temporal aspects and distinct patterns of the activities. Convolutional Neural Network (CNN) extracted miscellaneous features from the converted image data. The extracted features are then fed into Long Short-Term Memory (LSTM) to classify the activities. The proposed hybrid CNN-LSTM model offers over 95% accuracy for classifying the key activities in daily living (ADLs) (e.g., walking, eating, toileting, bathing, etc.). This approach also shows consistent performance in two different housing environments.","PeriodicalId":128826,"journal":{"name":"Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","volume":"24 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":"128474493","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

Path and Speed Planning Online Platform for Energy-Efficient Timely Truck Transportation 高效节能卡车实时运输路径与速度规划在线平台

Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation Pub Date : 2019-11-13 DOI: 10.1145/3360322.3361012

Yaowei Long, Zidong Wu, Hongyi Liu, Titing Cui, Wenjie Xu, Minghua Chen

引用次数: 1

DUET: Towards a Portable Thermal Comfort Model DUET:走向便携式热舒适模型

Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation Pub Date : 2019-11-13 DOI: 10.1145/3360322.3360842

Zimu Zheng, Yimin Dai, Dan Wang

{"title":"DUET: Towards a Portable Thermal Comfort Model","authors":"Zimu Zheng, Yimin Dai, Dan Wang","doi":"10.1145/3360322.3360842","DOIUrl":"https://doi.org/10.1145/3360322.3360842","url":null,"abstract":"Thermal comfort, achieved by estimating the thermal sensation of occupants, has long been an important research topic. Numerous models and systems have been developed to improve the estimates of the accuracy of thermal comfort. Many either require extra devices to be installed; or require occupants to provide frequent feedback hindering the large scale deployability of the system. Data-driven models separate the process of collecting data used to establish the thermal comfort model from the process of deploying the model, making these models portable in deployment. Recent studies on data-driven thermal comfort models often make use of a single model. A single model can introduce large errors in practice, as thermal comfort is highly dependent on a variety of contextual factors, such as building type, location, and so on. In this paper, we for the first time study the contextual adaptation involved in predicting the thermal comfort of individuals by training multi-task models. We develop a Dynamic MUlti-task PrEdiction on Thermal Comfort (DUET) model. A key idea of our model is to use metadata to automatically define multi-task. Fortunately, there are ongoing efforts in metadata development in buildings, e.g., Brick. We extract metadata from Brick and evaluate our model using the public ASHRAE dataset. We demonstrate that in terms of error rate, DUET outperforms PMV model by 39% and STL by 31%.","PeriodicalId":128826,"journal":{"name":"Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","volume":"67 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":"117012326","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: 6

Energy Exchange Model in Routed Energy Distribution Network 路由配电网中的能量交换模型

Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation Pub Date : 2019-11-13 DOI: 10.1145/3360322.3361017

Tatiana Endrjukaite, Alexander Dudko, Leon R. Roose

引用次数: 3

Evaluation of Clustering and Time Series Features for Point Type Inference in Smart Building Retrofit 智能建筑改造中点类型推断的聚类和时间序列特征评价

Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation Pub Date : 2019-11-13 DOI: 10.1145/3360322.3360839

Zixiao Shi, G. Newsham, Long Chen, H. Gunay

{"title":"Evaluation of Clustering and Time Series Features for Point Type Inference in Smart Building Retrofit","authors":"Zixiao Shi, G. Newsham, Long Chen, H. Gunay","doi":"10.1145/3360322.3360839","DOIUrl":"https://doi.org/10.1145/3360322.3360839","url":null,"abstract":"Metadata inference for building automation system (BAS) is an increasingly important topic to promote wider adoption of smart building technologies. Metadata inference is used to automatically discover semantics within the BAS, such as labelling sensors, discover control variable relationships, etc. Clustering analysis has been applied in many previous research studies to achieve faster smart building retrofits through automated or semi-automated BAS point labelling. However, previous research using clustering only used small data sets of two to five buildings. This research examines the effectiveness of this approach on a broader scale with 40 commercial and institutional buildings and more than 65,000 labelled BAS points. Different clustering strategies with varying feature space and clustering algorithms are examined. Furthermore, this study compares which time series features and generation approach may enhance labelling efficiency. Positive results from this study support the effectiveness of applying clustering for point type inference. Results show the complimentary nature of additional time series features when the existing raw metadata from the BAS is less descriptive.","PeriodicalId":128826,"journal":{"name":"Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","volume":"29 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":"121659938","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: 12