Proceedings of the 1st ACM International Workshop on Urban Building Energy Sensing, Controls, Big Data Analysis, and Visualization最新文献

Visualizing Urban Microclimate and Quantifying its Impact on Building Energy Use in San Francisco 可视化城市微气候及其对旧金山建筑能源使用的量化影响

Proceedings of the 1st ACM International Workshop on Urban Building Energy Sensing, Controls, Big Data Analysis, and Visualization Pub Date : 2019-11-13 DOI: 10.1145/3363459.3363536

Tianzhen Hong, Yujie Xu, Kaiyu Sun, Wanni Zhang, Xuan Luo

引用次数: 0

Potential Demand Reduction from Buildings in a Simulated Utility 模拟公用事业中建筑物的潜在需求减少

Proceedings of the 1st ACM International Workshop on Urban Building Energy Sensing, Controls, Big Data Analysis, and Visualization Pub Date : 2019-11-13 DOI: 10.1145/3363459.3363526

B. Bass, J. New

{"title":"Potential Demand Reduction from Buildings in a Simulated Utility","authors":"B. Bass, J. New","doi":"10.1145/3363459.3363526","DOIUrl":"https://doi.org/10.1145/3363459.3363526","url":null,"abstract":"The availability of large-area sensing, scalable algorithms, and high-performance computing has enabled the possibility of urban-scale building energy modeling using new methods not limited to the scalability of manual building energy model creation or retrieval of county-by-county tax assessor's data. Automatic Building detection and Energy Model creation (AutoBEM) has created 178,368 building energy models for the Electric Power Board (EPB) of Chattanooga, TN, and compared simulation performance to 15-minute data from each building. These models leverage several data sources (e.g. imagery, GIS, utility), software tools to extract building properties (e.g. footprint, height, façade type, window-to-wall ratio, occupancy, building type), simulate at scale on two of the world's #1 fastest supercomputers, and provide web-based visual analytics. Grid-interactive efficient buildings offer the potential to reduce utility and rate-payer energy costs during each calendar month's hour of critical generation -- when the least efficient, most costly, and often dirtiest generation assets must be brought online. In this paper, EnergyPlus is used to simulate over 150,000 buildings to assess the technical potential of utility-controlled smart thermostats. This is analyzed under a couple scenarios leveraging buildings as thermal batteries via pre-conditioning to coast through peak hours. Results are provided in box and whisker plots assessing the distribution of peak demand reduction at the utility-scale along with breakouts of energy and demand savings by building type and vintage.","PeriodicalId":260957,"journal":{"name":"Proceedings of the 1st ACM International Workshop on Urban Building Energy Sensing, Controls, Big Data Analysis, and Visualization","volume":"21 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":"116700663","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

Estimating Whole Building Occupancy from Transportation Simulations 从交通模拟中估计整个建筑物的占用率

Proceedings of the 1st ACM International Workshop on Urban Building Energy Sensing, Controls, Big Data Analysis, and Visualization Pub Date : 2019-11-13 DOI: 10.1145/3363459.3363532

R. Muehleisen, Qi Li

引用次数: 0

Climate Change Impacts on Campus Buildings Energy Use: An AI-based Scenario Analysis 气候变化对校园建筑能源使用的影响:基于人工智能的情景分析

Proceedings of the 1st ACM International Workshop on Urban Building Energy Sensing, Controls, Big Data Analysis, and Visualization Pub Date : 2019-11-13 DOI: 10.1145/3363459.3363540

Soheil Fathi, R. Srinivasan

{"title":"Climate Change Impacts on Campus Buildings Energy Use: An AI-based Scenario Analysis","authors":"Soheil Fathi, R. Srinivasan","doi":"10.1145/3363459.3363540","DOIUrl":"https://doi.org/10.1145/3363459.3363540","url":null,"abstract":"Almost 50% of global energy consumption is associated with buildings, among which buildings operational energy account for a major share. Buildings operational energy needs are highly dependent on the envelope and space functionality characteristics, as well as climate properties, which is expected to change over time. While managing buildings energy performance, climate change can be a threat as it can cause significant changes in energy use patterns. This paper discusses the use of artificial intelligence techniques to assess the impact of climate change on campus buildings energy use under various future envelope and climate scenarios. We have relied on hourly energy use data of buildings situated in the University of Florida, Gainesville, FL, as well as their thermo-physical and space functionality properties. To study the impact of climate change, we have used climate properties of three future climate scenarios developed by North American Regional Climate Change Assessment Program (NARCCAP) for Gainesville, FL, represented based on their impact within a 40-year prospect: median (year 2063), hottest (2057), and coldest (2041). In order to conduct the scenario analysis, we refer to the Technology Roadmap, Energy Efficient Building Envelopes, that is used to assess and present the development of technology products in building sector. According to the roadmap, we have assessed campus energy use for five envelope scenarios for each of the future climate scenarios.","PeriodicalId":260957,"journal":{"name":"Proceedings of the 1st ACM International Workshop on Urban Building Energy Sensing, Controls, Big Data Analysis, and Visualization","volume":"81 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":"125217497","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: 7

Building Energy Use Prediction Owing to Climate Change: A Case Study of a University Campus 基于气候变化的建筑能耗预测——以某大学校园为例

Proceedings of the 1st ACM International Workshop on Urban Building Energy Sensing, Controls, Big Data Analysis, and Visualization Pub Date : 2019-11-13 DOI: 10.1145/3363459.3363531

Haekyung Im, R. Srinivasan, Soheil Fathi

{"title":"Building Energy Use Prediction Owing to Climate Change: A Case Study of a University Campus","authors":"Haekyung Im, R. Srinivasan, Soheil Fathi","doi":"10.1145/3363459.3363531","DOIUrl":"https://doi.org/10.1145/3363459.3363531","url":null,"abstract":"Global warming is expected to increase of 1.5°C between 2030 and 2052. This may lead to an increase in building energy consumption. With the changing climate, cities, communities, and university campuses need a more dynamic forecasting model to predict their future energy demands to mitigate risks. Although many building energy prediction models have been developed, only a few have focused on climate change and its impacts. This paper discusses the development of a regression-based forecasting model for a university campus energy use under changing climate. The forecasting model development follows a four-step process: (1) data frame setting, (2) descriptive statistics, (3) statistical regression modeling, and (4) validation and prediction. The independent numeric variables used as inputs are building characteristics (gross square feet, lighting power density, equipment power density; U factor of roof, wall, and windows; building age; years after building renovation; and window-to-wall ratio) and weather data (temperature and humidity). The outputs are electricity and chilled water for 2054. Prior to modeling, matrix plots and histograms are used to identify correlations between variables. This step is followed by normalization of independent variables to check their impact. Finally, multiple linear regression model for electricity and Lasso regression models for chilled water estimations are developed. For the purpose of predicting energy consumption owing to climate change, we used weather data that represents 2054. The equipment power density was the most important factor for electricity consumption and temperature was the most important one for chilled water consumption. The prediction models give an insight of which factors remain essential and applicable to campus building policy to prevent wasting energy in buildings, as a result of climate change.","PeriodicalId":260957,"journal":{"name":"Proceedings of the 1st ACM International Workshop on Urban Building Energy Sensing, Controls, Big Data Analysis, and Visualization","volume":"81 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":"123849611","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: 5

Acquisition of Typical Occupancy Schedules for Commercial Buildings from Social Networks 从社交网络获取商业建筑的典型占用时间表

Proceedings of the 1st ACM International Workshop on Urban Building Energy Sensing, Controls, Big Data Analysis, and Visualization Pub Date : 2019-11-13 DOI: 10.1145/3363459.3363537

Xing Lu, Fan Feng, Zheng O’Neill

{"title":"Acquisition of Typical Occupancy Schedules for Commercial Buildings from Social Networks","authors":"Xing Lu, Fan Feng, Zheng O’Neill","doi":"10.1145/3363459.3363537","DOIUrl":"https://doi.org/10.1145/3363459.3363537","url":null,"abstract":"Occupancy behavior in buildings has been a hotspot of research as building systems become more sophisticated. Traditional sensing technologies suffer from high costs, inevitable sensor errors, and scalability issues and thus are not widely implemented in buildings. In this notes paper, two different approaches for extracting the typical occupancy schedules for the input to the building energy simulation are explored based on the data from social networks. The first approach is to use text classification algorithms to identify whether people are present in space where they are making posts on social networks. To achieve this, word embedding and machine learning algorithms for the classification are used. On top of that, we could extract the typical occupancy schedules by assuming certain people counting rules. The second approach is to utilize the processed GPS location tracking data provided by social network giants such as Facebook and Google Map. Web scraping techniques are used to obtain the data and extract the building typical occupancy schedules. Two preliminary case studies demonstrate these two approaches as a proof of concept using a museum building, the Art Institute of Chicago. The results show that the extracted building occupancy schedules from different social network data sources (Twitter, Facebook, and Google Map) share a similar trend but slightly distinct with each other, which requires more explorations and further validations and corrections. However, the methodology and promising results from this preliminary study will lay the foundation of the occupancy sensing through the social network data mining, which aims to provide another data source for occupancy sensing in buildings at the building level. This will provide another alternative to estimate the occupancy at the community- and urban-scale.","PeriodicalId":260957,"journal":{"name":"Proceedings of the 1st ACM International Workshop on Urban Building Energy Sensing, Controls, Big Data Analysis, and Visualization","volume":"6 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":"126469043","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

Engineering vs. Ambient type visualizations: Quantifying effects of different data visualizations on energy consumption 工程与环境类型可视化:量化不同数据可视化对能源消耗的影响

Proceedings of the 1st ACM International Workshop on Urban Building Energy Sensing, Controls, Big Data Analysis, and Visualization Pub Date : 2019-11-13 DOI: 10.1145/3363459.3363527

Lucas Spangher, A. Tawade, Alex Devonport, C. Spanos

{"title":"Engineering vs. Ambient type visualizations: Quantifying effects of different data visualizations on energy consumption","authors":"Lucas Spangher, A. Tawade, Alex Devonport, C. Spanos","doi":"10.1145/3363459.3363527","DOIUrl":"https://doi.org/10.1145/3363459.3363527","url":null,"abstract":"Engaging consumers with energy use is a challenging endeavor; however, different forms of data visualization may help by creating relationships between a user and their data. Here we propose and conduct an experiment to test two different visualizations: an \"engineering-type\" visualization of a bar chart, and an \"ambient-type\" visualization of a cartoon tree. Participants in a competitive energy-saving game within an office building in Singapore received weekly emails visualizing their energy consumption and encouraging them to interact with their online game profile, and energy use was measured in addition to qualitative feedback through pre-experiment and post-experiment surveys. We find that the engineering-type visual did not result in statistically significant reductions whereas the ambient-type did, resulting in an average decrease of 82 Wh per day for the three days following treatment. Additionally, we find that greater engagement uniformly correlates positively with energy savings. A finite state machine simulation indicates that were this experiment scaled to 1000 people over 100 days, the total energy savings might be expected to be 1.1MWh.","PeriodicalId":260957,"journal":{"name":"Proceedings of the 1st ACM International Workshop on Urban Building Energy Sensing, Controls, Big Data Analysis, and Visualization","volume":"36 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":"134049748","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: 9

Quantification of Energy Cost Savings through Optimization and Control of Appliances within Smart Neighborhood Homes 通过智能社区家庭中电器的优化和控制来节约能源成本的量化

Proceedings of the 1st ACM International Workshop on Urban Building Energy Sensing, Controls, Big Data Analysis, and Visualization Pub Date : 2019-11-13 DOI: 10.1145/3363459.3363535

S. Chinthavali, Varisara Tansakul, S. Lee, Anika Tabassum, J. Munk, J. Jakowski, M. Starke, T. Kuruganti, H. Buckberry, J. Leverette

{"title":"Quantification of Energy Cost Savings through Optimization and Control of Appliances within Smart Neighborhood Homes","authors":"S. Chinthavali, Varisara Tansakul, S. Lee, Anika Tabassum, J. Munk, J. Jakowski, M. Starke, T. Kuruganti, H. Buckberry, J. Leverette","doi":"10.1145/3363459.3363535","DOIUrl":"https://doi.org/10.1145/3363459.3363535","url":null,"abstract":"Electric utilities are driving towards enabling automatic scheduling and control of the consumption pattern of appliances such as heating, ventilation, and air conditioning (HVAC) and water heater (WH) systems (e.g., through preheating and pre-cooling, etc.) within smart neighborhoods to minimize energy cost and peak load demand. Quantifying economic savings through direct comparison of the optimized energy usage profile on a specific day with the typical non-optimized usage profile on another day is not a fair comparison because energy usage highly depends on weather conditions and human behaviour especially appliance like HVAC on those days. In this paper, we propose a novel approach of identifying similar weather day pairs which can then be used to compare the energy use profiles within homes between the identified pairs. We then demonstrate how the proposed approach can be used to compute cost savings due to optimization and control of smart appliances at home and neighborhood-level within a future-focused smart neighborhood of 62 residential homes. We also demonstrate a simulation based approach to quantify cost savings and showcase our findings through customized and interactive visualizations.","PeriodicalId":260957,"journal":{"name":"Proceedings of the 1st ACM International Workshop on Urban Building Energy Sensing, Controls, Big Data Analysis, and Visualization","volume":"28 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120913898","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: 1

Evaluating the Feasibility of Reusing Pre-trained Thermal Models in the Residential Sector 评估在住宅领域重复使用预训练热模型的可行性

Proceedings of the 1st ACM International Workshop on Urban Building Energy Sensing, Controls, Big Data Analysis, and Visualization Pub Date : 2019-11-13 DOI: 10.1145/3363459.3363529

M. Hossain, Tianyu Zhang, Omid Ardakanian

{"title":"Evaluating the Feasibility of Reusing Pre-trained Thermal Models in the Residential Sector","authors":"M. Hossain, Tianyu Zhang, Omid Ardakanian","doi":"10.1145/3363459.3363529","DOIUrl":"https://doi.org/10.1145/3363459.3363529","url":null,"abstract":"Modelling temperature dynamics of a building is necessary to develop control mechanisms for reducing energy consumption of heating and cooling equipment. While Resistance-Capacitance (RC) models can accurately explain how the indoor temperature changes over time, building such models requires the knowledge of the building insulation and thermal mass, which is not readily available for most residential buildings in operation today. In the absence of this information, model parameters can be estimated from coarsegrained data collected by smart thermostats. In this paper we train a Bayesian neural network to establish the RC model for a home equipped with a smart thermostat, and investigate how to reuse this model to predict the temperature inside another home which may not be equipped with a smart thermostat. Leveraging data from ecobee smart thermostats installed in over 4,000 homes in Canada, we validate that a small number of pre-trained neural network models is enough to develop a sufficiently accurate RC model for any home across the country and that this model outperforms a seasonal time-series model that is built using the same amount of data.","PeriodicalId":260957,"journal":{"name":"Proceedings of the 1st ACM International Workshop on Urban Building Energy Sensing, Controls, Big Data Analysis, and Visualization","volume":"2 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":"125182322","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: 9

Big Data: A Decade of Energy Characteristics of Single-Family Homes in Florida 大数据:佛罗里达独户住宅十年的能源特征

Proceedings of the 1st ACM International Workshop on Urban Building Energy Sensing, Controls, Big Data Analysis, and Visualization Pub Date : 2019-11-13 DOI: 10.1145/3363459.3363533

Rita Elias, R. Issa

{"title":"Big Data: A Decade of Energy Characteristics of Single-Family Homes in Florida","authors":"Rita Elias, R. Issa","doi":"10.1145/3363459.3363533","DOIUrl":"https://doi.org/10.1145/3363459.3363533","url":null,"abstract":"Housing in Florida is mainly driven by population growth of between 300,000 and 400,000 people per year, which makes Florida the third largest homebuilding state in the U.S. This high rate of population growth sheds light on the importance of building healthy residential houses that are energy-efficient throughout all of Florida, in all its climate zones. The purpose of this study is to investigate the direction Florida's single-family homes constructed between the years 2009 and 2018 are taking with respect to energy efficiency by analyzing their energy characteristics through big data analysis. Therefore, this study (a) developed a comprehensive literature review of the existing energy-efficient design strategies adopted in Florida. In addition, (b) explained the process of collecting information about materials and design strategies used in single-family houses constructed between the years 2009 and 2018, from energy forms prepared in the framework of the permit application process at every local building permit department in different counties all over Florida. Finally, (c) analyzed the evolution of information collected throughout the years, including but not limited to wall types, ceiling types, ducts' location, windows' features, heating and cooling systems, in the different climate zones of Florida influencing the building performance. The results of this big data analysis indicated that, on the average, single-family homes in Florida tended to get more energy-efficient and sustainable throughout the last decade (2009-2018). This conclusion cannot be totally confirmed unless all economic, environmental, and social aspects of sustainability are also taken into consideration.","PeriodicalId":260957,"journal":{"name":"Proceedings of the 1st ACM International Workshop on Urban Building Energy Sensing, Controls, Big Data Analysis, and Visualization","volume":"9 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":"133491133","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: 1