John Wamburu, Christopher Raff, David E. Irwin, P. Shenoy
{"title":"Greening Electric Bike Sharing Using Solar Charging Stations","authors":"John Wamburu, Christopher Raff, David E. Irwin, P. Shenoy","doi":"10.1145/3408308.3427621","DOIUrl":"https://doi.org/10.1145/3408308.3427621","url":null,"abstract":"Electric bikes have emerged as a popular form of transportation for short trips in dense urban areas and are being increasingly adopted by bike share programs for easy accessibility to riders. Motivated by the rising popularity of electric bikes, a form of an electric vehicle, we study the research question of how to design a zero-carbon electric bike share system. Specifically we study the challenges in designing solar charging stations for electric bike systems that enable either net-zero or a fully zero-carbon operation. We design a prototype two bike solar charging station to demonstrate the feasibility of our approach. Using insights and data from our prototype solar charging station, we then conduct a data driven analysis of the costs and benefits of converting an entire bike system into one powered using solar charging stations. Using empirical analysis, we determine the panel and battery capacity for each station, and perform a feasibility evaluation of the system using 8 months of ridership data. Our results show that equipping each bike station with a single grid-tied solar panel is adequate to meet the annual charging demand from electric bikes and achieve net-zero operation using net-metering. For an off-grid setup, our analysis shows that a bike station needs twice as many solar panels, on average, along with a 1.8kWh battery, with the busiest bike station needing 6× more solar capacity than in the net-metering case. Our analysis also reveals a tradeoff between the array size and the battery size needed to achieve true-zero carbon operation for the electric bike share system.","PeriodicalId":287030,"journal":{"name":"Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","volume":"144 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":"122431982","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":"Automatic Damage Detection on Rooftop Solar Photovoltaic Arrays","authors":"Qi Li, Keyang Yu, Dong Chen","doi":"10.1145/3408308.3431130","DOIUrl":"https://doi.org/10.1145/3408308.3431130","url":null,"abstract":"Homeowners may spend up to ~$375 to diagnose their damaged rooftop solar PV systems. Thus, recently, there is a rising interest to inspect potential damage on solar PV arrays automatically and passively. Unfortunately, current approaches may not reliably distinguish solar PV array damage from other degradation (e.g., shading, dust, snow). To address this issue, we design a new system---SolarDiagnostics that can automatically detect and profile damages on rooftop solar PV arrays using their rooftop images with a lower cost. We evaluate SolarDiagnostics by building a lower cost (~$35) prototype and using 60,000 damaged solar PV array images. We find that pre-trained SolarDiagnostics is able to detect damaged solar PV arrays with a Matthews Correlation Coefficient of 0.95.","PeriodicalId":287030,"journal":{"name":"Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","volume":"20 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":"125966705","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":"The Impacts of CSI Temporal Variations on CSI-based Occupancy Monitoring Systems: An Exploratory Study","authors":"Hoonyong Lee, C. Ahn, Nakjung Choi","doi":"10.1145/3408308.3427624","DOIUrl":"https://doi.org/10.1145/3408308.3427624","url":null,"abstract":"Channel State Information (CSI) has been used for an alternative sensing source for occupancy monitoring systems to classify activities of daily living (ADLs). Previous studies have proposed learning-based activity classification models, which require similar distributions of CSI for training and testing datasets. However, as CSI varies even in a static environment, the activity classification model trained with data collected in a particular day would be invalid for other time frames. In this context, this study examines the impacts of the CSI temporal variations on the learning-based occupant activity monitoring systems. An experiment was performed to collect the CSI data while an occupant performed daily activities for six days. Three learning-based activity classification models reconstructed from the previous studies were trained and tested with time-dependent cross validation. The performances of the benchmark models were greatly degraded (below 60%) with testing data collected at different days than the training data, while their performances with testing data collected at the same day with training data were over 90%. This study also explores the opportunity to address this issue with transfer learning techniques.","PeriodicalId":287030,"journal":{"name":"Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","volume":"4 1-2 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":"114352376","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":"Can Future Wireless Networks Detect Fires?","authors":"David Radke, Omid Abari, Tim Brecht, K. Larson","doi":"10.1145/3408308.3427978","DOIUrl":"https://doi.org/10.1145/3408308.3427978","url":null,"abstract":"Latencies, operating ranges, and false positive rates for existing indoor fire detection systems like smoke detectors and sprinkler systems are far from ideal. This paper explores the use of wireless radio frequency (RF) signals to detect indoor fires with low latency, through walls and other occlusions. We build on past research focused on wireless sensing, and introduce RFire, a system which uses millimeter wave technology and deep learning to extract instances of fire. We perform line-of-sight (LoS) and occluded non-LoS experiments with fire at different distances, and find that RFire achieves a best-result mean latency of 24 seconds when trained and tested in multiple environments. RFire yields at least a 4 times improvement in mean alarm latency over today's alarms.","PeriodicalId":287030,"journal":{"name":"Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","volume":"229 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":"116432286","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}
José R. Vázquez-Canteli, Sourav Dey, G. Henze, Z. Nagy
{"title":"The CityLearn Challenge 2020","authors":"José R. Vázquez-Canteli, Sourav Dey, G. Henze, Z. Nagy","doi":"10.1145/3408308.3431122","DOIUrl":"https://doi.org/10.1145/3408308.3431122","url":null,"abstract":"This poster shows the results of The CityLearn Challenge 2020. 4 teams competed over 6 months to design the best reinforcement learning agent for the energy management of a microgrid.","PeriodicalId":287030,"journal":{"name":"Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","volume":"13 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":"114680607","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":"Assessing ADL Routine Variability from High-dimensional Sensing Data using Hierarchical Clustering","authors":"Bogyeong Lee, C. Ahn, P. Mohan, Theodora Chaspari","doi":"10.1145/3408308.3427626","DOIUrl":"https://doi.org/10.1145/3408308.3427626","url":null,"abstract":"Irregular patterns of Activities of Daily Living (ADLs) are associated with mild cognitive impairment (MCI) of older adults. Measuring the variability of ADL routines using various non-intrusive sensors in smart home environments presents a great opportunity for early diagnosis of MCI. However, existing studies mostly rely on supervised learning approaches to recognize ADLs and measure their variabilities, which requires large efforts in human observation and manual annotation for constructing training datasets for each home environment. In this context, this study proposes an unsupervised hierarchical clustering method to capture ADL clusters and measure their variabilities. In particular, this study focuses on addressing the challenge in employing data from multiple heterogenous sensors. The results show that the proposed method can capture the variability of ADL routines using high-dimensional non-intrusive sensing data.","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":"132826404","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":"Detecting Building Occupancy with Synthetic Environmental Data","authors":"Manuel Weber, Christoph Doblander, P. Mandl","doi":"10.1145/3408308.3431124","DOIUrl":"https://doi.org/10.1145/3408308.3431124","url":null,"abstract":"Information about room-level occupancy is crucial to many building-related tasks, such as building automation or energy performance simulation. Carbon dioxide levels and other indoor environmental factors can be used as a proxy to detect occupancy. In this regard, machine learning solutions have been proposed, with solid performance in detecting presence, as well as counting the number of present occupants, if enough training data is available. The challenge is, to collect sufficient room-specific ground truth data for model training. With this poster, we address the use of knowledge transfer from synthetic data to reduce the amount of required real world data. We outline two approaches for the combination of transfer learning with physical simulations, and motivate the generation of additional synthetic data. Our results show that the required real world training data can be reduced by 50%.","PeriodicalId":287030,"journal":{"name":"Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","volume":"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":"126560695","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}
Ashish Gupta, Hari Prabhat Gupta, Tanima Dutta, Sajal K. Das
{"title":"Towards Identifying Alien Appliances using Semantic Information","authors":"Ashish Gupta, Hari Prabhat Gupta, Tanima Dutta, Sajal K. Das","doi":"10.1145/3408308.3431128","DOIUrl":"https://doi.org/10.1145/3408308.3431128","url":null,"abstract":"Currently, the applicability of recognition approaches is limited to only native (known) appliances for which training data is available. It means the appliance with no training instances appears as an alien to the approaches. An alien (new) appliance may introduce as household any time by the electricity consumer. The central focus on this paper is on building an appliance recognition approach that can accurately identify both native and alien appliances by leveraging semantic information. This work also collects electricity usage data by deploying smart meters in an apartment complex, for experimental evaluation. The initial accuracy results are satisfactory and validating the effectiveness of our approach for alien appliances.","PeriodicalId":287030,"journal":{"name":"Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","volume":"7 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":"126891620","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":"C-HVAC: A practical tool for assessing ventilation capacity for HVAC systems during the COVID-19 Pandemic","authors":"F. Kharvari, W. O’brien","doi":"10.1145/3408308.3431133","DOIUrl":"https://doi.org/10.1145/3408308.3431133","url":null,"abstract":"Although many health organizations are advising commercial and public sectors to maximize the outdoor air circulation and ventilation rates, such actions may not be completely feasible and impose a significant energy load on the HVAC systems. While the social distancing measures are being implemented, the aim of this paper is to present a feasible and practical solution for various stakeholders to calculate the maximum number of people that can occupy buildings during the COVID-19 pandemic when the HVAC systems of buildings work based on outside air or a combination of outside and recirculated air. As a result, C-HVAC, as an easy-to-use tool, was developed using thermodynamic principles that can calculate the maximum number of people that can occupy buildings during the COVID-19 pandemic based on ASHRAE's Building Readiness recommendations. The current paper is part of ongoing research on preventing the spread of highly infectious airborne diseases such as COVID-19 in indoor environments. The beta version of the tool is made publicly available through this article.","PeriodicalId":287030,"journal":{"name":"Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","volume":"79 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":"126179757","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":"Viability of a Dense, Low-Cost Particulate Matter Sensor Network","authors":"Hagen Fritz, Calvin Lin, Z. Nagy","doi":"10.1145/3408308.3431120","DOIUrl":"https://doi.org/10.1145/3408308.3431120","url":null,"abstract":"A technological revolution in recent years has allowed for the vast development of low-cost, commercially available air quality sensors. The affordability of such devices allows researchers, institutions, and private entities alike to create sensor networks in their local regions to monitor various pollutants. Many of these networks are on the city-scale, but only a few, if any, have focused on a more concentrated network. This research focuses on the utility of such a network on a university campus scale by analyzing measurements made from 15 sensors over a period of 9 months. Initial results indicate that while the network provides a wealth of data, variation amongst the sensor readings is low especially when considering daily averages in a cleaner environment like Austin, TX. Our work suggests that low-cost sensor networks in cleaner environments are better suited in networks that span larger geographical areas and data collected from them used to understand hourly or diurnal trends.","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":"131995900","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}