Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation最新文献

{"title":"Ecologically Valid Multimodal Data Collection","authors":"Hagen Fritz, Z. Nagy","doi":"10.1145/3408308.3431126","DOIUrl":"https://doi.org/10.1145/3408308.3431126","url":null,"abstract":"This research effort focuses on combining affordable sensing technologies to understand individual behavioral patterns, health outcomes, and personal environments. We deployed our own environmental quality monitor along with established mobile sensing and experience sampling techniques in a study of more than 50 student participants for 3 months in central Texas. We report on our remote deployment, descriptive statistics of the collected data, and results from an initial exploratory analysis. Our novel dataset allows us to study individuals as well as groups and draw ecologically valid conclusions since participants were not isolated from their environments or routines.","PeriodicalId":287030,"journal":{"name":"Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","volume":"20 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116412092","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":"Sense, Send, Store, See, Share: the journey of SEIL-R, an electricity consumption dataset","authors":"Kevin Joshi, Rohit Gupta, Shinjan Mitra, K. Ramamritham","doi":"10.1145/3408308.3427615","DOIUrl":"https://doi.org/10.1145/3408308.3427615","url":null,"abstract":"Data-driven approaches to computationally manage electricity consumption is envisioned as a standard practice under the smart grid paradigm. The roll out of Advanced Metering Infrastructure provides the necessary push for installation of smart meters for electricity consumers. Since smart meters are capable of measuring at fine temporal resolutions they act as a source of data for research challenges ranging from smart energy management to consumer participation and adoption of renewable energy sources. However, geographical location, built environment, climate and lifestyle preferences affect the electricity consumption patterns of a consumer. Therefore it is imperative to derive solutions using data that can represent specific conditions of demography and geographical region. To this end, we present SEIL-R- a public dataset of electricity consumption by residences of a multi-storey building located in Mumbai. This work presents the entire process of mining, preliminary analysis and visualization of data collected from smart meters using off-the-shelf hardware and open-source software. The raw data of residences is anonymized and released with open-access standard for the research community.","PeriodicalId":287030,"journal":{"name":"Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122381932","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":"Bidding Strategy for Two-Sided Electricity Markets: A Reinforcement Learning based Framework","authors":"Bala Suraj Pedasingu, E. Subramanian, Y. Bichpuriya, V. Sarangan, Nidhisha Mahilong","doi":"10.1145/3408308.3427976","DOIUrl":"https://doi.org/10.1145/3408308.3427976","url":null,"abstract":"We aim to increase the revenue or reduce the purchase cost of a given market participant in a double-sided, day-ahead, wholesale electricity market serving a smart city. Using an operations research based market clearing mechanism and attention based time series forecaster as sub-modules, we build a holistic interactive system. Through this system, we discover better bidding strategies for a market participant using reinforcement learning (RL). We relax several assumptions made in existing literature in order to make the problem setting more relevant to real life. Our Markov Decision Process (MDP) formulation enables us to tackle action space explosion and also compute optimal actions across time-steps in parallel. Our RL framework is generic enough to be used by either a generator or a consumer participating in the electricity market. We study the efficacy of the proposed RL based bidding framework from the perspective of a generator as well as a buyer on real world day-ahead electricity market data obtained from the European Power Exchange (EPEX). We compare the performance of our RL based bidding framework against three baselines: (a) an ideal but un-realizable bidding strategy; (b) a realizable approximate version of the ideal strategy; and (c) historical performance as found from the logs. Under both perspectives, we find that our RL based framework is more closer to the ideal strategy than other baselines. Further, the RL based framework improves the average daily revenue of the generator by nearly €7,200 (€2.64 M per year) and €9,000 (€3.28 M per year) over the realizable ideal and historical strategies respectively. When used on behalf of a buyer, it reduces average daily procurement cost by nearly €2,700 (€0.97 M per year) and €57,200 (€52.63 M per year) over the realizable ideal and historical strategies respectively. We also observe that our RL based framework automatically adapts its actions to changes in the market power of the participant.","PeriodicalId":287030,"journal":{"name":"Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121545971","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":"Experiences and Learned Lessons from an Air Free-Cooled Tropical Data Center Testbed","authors":"D. V. Le, Yingbo Liu, Rongrong Wang, Rui Tan, L. Ngoh","doi":"10.1145/3408308.3427628","DOIUrl":"https://doi.org/10.1145/3408308.3427628","url":null,"abstract":"The air free-cooling has been long thought infeasible in tropics due to the unique challenges of year-round high ambient temperature and relative humidity. In recent years, the increasing availability of servers that can tolerate higher temperatures and relative humidity levels sheds light upon the feasibility of the air free-cooling to enhance the data center energy efficiency. However, building an air free-cooled data center in the tropics requires extensive experiments to understand the details of how the tropical environment conditions will affect data center power consumption, computing throughput, and server hardware reliability. Thus, together with multiple partners in data center industry and research, we conducted a project that designs, builds, and experiments with an air free-cooled data center testbed consisting of three server rooms hosting 12 server racks with 60 kW total power rating. This paper presents the key observations, experiences and learned lessons obtained from our project. The experiments show that (1) the air free-cooling design that uses fans only can reduce the power usage effectiveness (PUE) by 38%, compared to the global average PUE, (2) the tropics' year-round high temperatures up to 37°C do not impede the air free-cooling, and (3) the implementation of the air free-cooled data centers in tropics requires special cares to deal with airborne contaminants to avoid fast corrosion rate and dust-induced server faults.","PeriodicalId":287030,"journal":{"name":"Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130143198","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":"Feature Mapping based Deep Neural Networks for Non-intrusive Load Monitoring of Similar Appliances in Buildings","authors":"R. Gopinath, Mukesh Kumar, K. Srinivas","doi":"10.1145/3408308.3427622","DOIUrl":"https://doi.org/10.1145/3408308.3427622","url":null,"abstract":"Energy management plays an important role in the smart sustainable cities development programme to utilise energy resources in a responsible manner for conserving the environment and improving well-being of the society. Building sector is one of the major sectors that consumes more energy in commercial and residential buildings. Recently, non-intrusive load monitoring technique (NILM) has become popular among the researchers for its capability in disaggregation of energy at appliance/load level from the measured aggregated energy. Appliance signatures are learned using machine learning and deep learning approaches for effective detection of appliance events and energy consumption. However, appliance detection becomes challenging when appliances in the electrical network are similar or same type. Therefore, effective feature learning methodologies need to be developed for distinguishing the events of similar loads more accurately. In this paper, we used the open source dataset1 that consists of fundamental electrical features extracted from the four fluorescent lamps having same technical specifications. From the preliminary experiments, it is observed that the baseline system performance with the support vector machine (SVM) and deep neural networks (DNN) is not much encouraging due to the overlapping and nonlinear characteristics of similar loads. To overcome this problem, we expresses the original feature vectors in terms of appliance independent basis vectors in a higher dimensional space using a feature mapping technique, locality constrained linear coding (LLC) and then used machine learning classifiers for similar load identification. From the experiments and results, it is observed that feature mapping based deep neural networks (LLC-DNN) outperforms the baseline, LLC-SVM and other reported approaches from the literature significantly for similar appliances detection in NILM system.","PeriodicalId":287030,"journal":{"name":"Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131586804","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":"See the Light: Modeling Solar Performance using Multispectral Satellite Data","authors":"A. S. Bansal, David E. Irwin","doi":"10.1145/3408308.3427610","DOIUrl":"https://doi.org/10.1145/3408308.3427610","url":null,"abstract":"Developing accurate solar performance models, which infer solar output from widely available external data sources, is increasingly important as the grid's solar capacity rises. These models are important for a wide range of solar analytics, including solar forecasting, resource estimation, and fault detection. The most significant error in existing models is inaccurate estimates of clouds' effect on solar output, as cloud formations and their impact on solar radiation are highly complex. In 2018 and 2019, respectively, the National Oceanic and Atmospheric Administration (NOAA) in the U.S. began releasing multispectral data comprising 16 different light wavelengths (or channels) from the GOES-16 and GOES-17 satellites every 5 minutes. Enough channel data is now available to learn solar performance models using machine learning (ML). In this paper, we show how to develop both local and global solar performance models using ML on multispectral data, and compare their accuracy to existing physical models based on ground-level weather readings and on NOAA's estimates of downward shortwave radiation (DSR), which also derive from multispectral data but using a physical model. We show that ML-based solar performance models based on multispectral data are much more accurate than weather- or DSR-based models, improving the average MAPE across 29 solar sites by over 50% for local models and 25% for global models.","PeriodicalId":287030,"journal":{"name":"Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131339475","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":"Geo-Distributed Driving Maneuver Anomaly Detection","authors":"Miaomiao Liu, Wan Du","doi":"10.1145/3408308.3431117","DOIUrl":"https://doi.org/10.1145/3408308.3431117","url":null,"abstract":"Auto-Encoder has been widely applied to anomaly detection areas. In this paper, we present a geo-distributed driving maneuver anomaly detection system based on auto-encoder. The auto-encoder is trained by using the normal driving data, so it memorizes the feature of normal driving pattern. The well trained auto-encoder is able to work as a classifier during the detection phase, it will tell whether the input data is normal or abnormal. To further improve the detection accuracy, we divide a city into a set of sub-regions by maximizing the spatial contrast within the same sub-region and minimizing the spatial contrast among different sub-regions. To examine performance of the proposed system, we evaluate it using a large dataset of GPS trajectories. The experiment results show our system achieves high detection accuracy.","PeriodicalId":287030,"journal":{"name":"Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123095150","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":"Incentivizing Privacy-Preserving Crowdsensing for Smart Transportation","authors":"Nan Wang, S. Chau","doi":"10.1145/3408308.3431131","DOIUrl":"https://doi.org/10.1145/3408308.3431131","url":null,"abstract":"We present a novel mechanism to enhance participatory crowdsensing for smart transportation by integrating privacy-preserving data filtering, aggregation and incentive-driven dissemination. Our mechanism filters out inaccurate data before computing the aggregate statistics (e.g., mean, variance) that will be disseminated as incentives to only the users, who contribute useful data to their computations in a privacy-preserving manner. Based on efficient homomorphic cryptosystems, our privacy-preserving mechanism achieves satisfactory data accuracy and privacy simultaneously.","PeriodicalId":287030,"journal":{"name":"Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130357328","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":"Vista: Spatial Data Representation for Smart Buildings","authors":"Matilda Kathryn Ferguson, Sudershan Boovaraghavan, Yuvraj Agarwal","doi":"10.1145/3408308.3431112","DOIUrl":"https://doi.org/10.1145/3408308.3431112","url":null,"abstract":"With the increasing prevalence and power of building IoT sensors and devices, there is a growing need for intuitive, accessible, and meaningful data visualization tools that are specific and tailored for building IoT. Existing tools that incorporate spatial contextualization are insulated to specific applications with predefined visualization goals. We present Vista, a front end tool, that presents dynamic data visualizations of building IoT data on an interface that allows for context and deeper evaluation of information. This framework proposes a method to transform any static building floor plan into a dynamic one with which one can create unique and intuitive data representations tailored to their own objectives. The modular design of this tool enables an authoring system flexible to heterogeneous data by separating the visual elements of the floor plan and the relevant data, enabling diverse visual presentations of various types of IoT information and inputs.","PeriodicalId":287030,"journal":{"name":"Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125340630","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":"Exploring Time Series Imaging for Load Disaggregation","authors":"Hafsa Bousbiat, Christoph Klemenjak, W. Elmenreich","doi":"10.1145/3408308.3427975","DOIUrl":"https://doi.org/10.1145/3408308.3427975","url":null,"abstract":"In this paper, we investigate the benefits of time-series imaging in load disaggregation, as we augment the wide-spread sequence-to-sequence approach by a key element: an imaging block. The approach presented in this paper converts an input sequence to an image, which in turn serves as input to a modified version of a common Denoising Autoencoder architecture used in load disaggregation. Based on these input images, the Autoencoder estimates the power consumption of a particular appliance. The main contribution presented in this paper is a comparison study between three common imaging techniques: Gramian Angular Fields, Markov Transition Fields, and Recurrence Plots. Further, we assess the performance of our augmented networks by a comparison with two benchmarking implementations, one based on Markov Models and the other one being a common Denoising Autoencoder. The outcome of our study reveals that in 19 of 24 cases, the considered augmentation techniques provide improved performance over the baseline implementation. Further, the findings presented in this paper indicate that the Gramian Angular Field could be better suited, though the Recurrence Plot was observed to be a viable alternative in some cases.","PeriodicalId":287030,"journal":{"name":"Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116439288","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}