SI2-SSE: E-SDMS: Energy Simulation Data Management System Software

SI2-SSE：E-SDMS：能源模拟数据管理系统软件

• 批准号: 1339835
• 负责人: Kasim Candan
• 金额: $ 49.97万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-10-01 至 2018-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1339835&HistoricalAwards=false
• 关键词: SI2; SSE; SDMS; Energy; Simulation

The building sector was responsible for nearly half of CO2 emissions in US in 2009. According to the US Energy Information Administration, buildings consume more energy than any other sector, with 48.7% of the overall energy consumption, and building energy consumption is projected to grow faster than the consumptions of industry and transportation sectors. As a response to this, by 2030 only 18% of the US building stock is expected to be relying on the current energy management technologies, with the rest either having been retrofitted or designed from the ground up using smart and cleaner energy technologies. These building energy management systems (BEMSs) need to integrate large volumes of data, including (a) continuously collected heating, ventilation, and air conditioning (HVAC) sensor and actuation data, (b) other sensory data, such as occupancy, humidity, lighting levels, air speed and quality, (c) architectural, mechanical, and building automation system configuration data for these buildings, (d) local whether and GIS data that provide contextual information, as well as (e) energy price, consumption, and cost data from electricity (such as smart grid) and gas utilities. In theory, these data can be leveraged from the initial design and/or retrofitting of buildings with data driven building optimization (including the evaluation of the building location, orientation, and alternative energy-saving strategies) to total cost of ownership (TCOs) simulation tools and day-to-day operation decisions. In practice, however, because of the size and complexity of the data, the varying spatial and temporal scales at which the key processes operate, (a) creating models to support such simulations, (b) executing simulations that involve 100s of inter-dependent parameters spanning multiple spatio-temporal frames, affected by complex dynamic processes operating at different resolutions, and (c) analyzing simulation results are extremely costly. The energy simulation data management system (e-SDMS) software will address challenges that arise from the need to model, index, search, visualize, and analyze, in a scalable manner, large volumes of multi-variate series resulting from observations and simulations. e-SDMS will, therefore, fill an important hole in data-driven building design and clean-energy (an area of national priority) and will enable applications and services with significant economic and environmental impact.The key observations driving the research is that many data sets of urgent interest to energy simulations include the following: (a) voluminous, (b) heterogeneous, (c) multi-variate, (d) temporal, (e) inter-related (meaning that the parameters of interest are dependent on each other and constrained with the structure of the building), and (f) multi-resolution (meaning that simulations and observations cover days to months of data and may be considered at different granularities of space, time, and parameters). Moreover, generating an appropriate ensemble of simulations for decision making often requires multiple simulations, each with different parameters settings corresponding to slightly different, but plausible, scenarios. Therefore, significant savings in modeling and analysis can be obtained through data management software supporting modular re-use of existing simulation results in new settings, such as re-contextualization and modular recomposition (or "sketching") of building models and if-then analysis of simulation traces under new parameters, new building floorplans, and new contexts. In developing the energy simulation data management system (e-SDMS), the research addresses the key data challenges that render data-driven energy simulations, today, difficult. This requires (a) a novel building models, simulation traces, and sensor/actuation traces (BSS) data model to accommodate energy simulation data and models, (b) feature analysis and indexing of sensory data and simulation traces along with the corresponding building models, and (c) algorithms for analysis and exploration of simulation traces and re-contextualization of models for new building plans and contextual metadata. This research will therefore, impact computational challenges that arise from the need to model, analyze, index, visualize, search, and recompose, in a scalable manner, large volumes of multi-variate series resulting from energy observations and simulations. E-SDMS consists of an (a) eViz server, which works as a frontend to e-SDMS, an (b) eDMS middleware for feature extraction, indexing, simulation analysis, and sketching, and an (c) eStore backend for data storage. To avoid waste and achieve scalabilities needed for managing large data sets, e-SDMS employs novel multi-resolution data partitioning and resource allocation strategies. The multi-resolution data encoding, partitioning, and analysis algorithms are efficiently computable, leverage massive parallelism, and result in high quality, compact data descriptions.

2009年，建筑业占美国二氧化碳排放量的近一半。根据美国能源信息署的数据，建筑物消耗的能源比任何其他部门都多，占总能源消耗的48.7%，预计建筑物能源消耗的增长速度将快于工业和运输部门的消耗。 到2030年，预计只有18%的美国建筑存量将依赖于当前的能源管理技术，其余的要么进行了改造，要么使用智能和清洁能源技术进行了从头开始的设计。这些建筑物能量管理系统（BEMS）需要集成大量数据，包括（a）连续收集的加热、通风和空调（HVAC）传感器和致动数据，（B）其他传感数据，诸如占用、湿度、照明水平、空气速度和质量，（c）这些建筑物的建筑、机械和建筑自动化系统配置数据，（d）提供背景信息的当地天气和地理信息系统数据，以及（e）来自电力（例如智能电网）和天然气公用事业的能源价格、消耗和成本数据。 从理论上讲，这些数据可以从最初的设计和/或改造建筑物与数据驱动的建筑优化（包括建筑物的位置，方向和替代节能策略的评估），以总拥有成本（TCO）模拟工具和日常运营决策。然而，在实践中，由于数据的大小和复杂性，关键过程操作的变化的空间和时间尺度，（a）创建模型以支持这样的模拟，（B）执行涉及跨越多个时空帧的100个相互依赖的参数的模拟，这些参数受到以不同分辨率操作的复杂动态过程的影响，以及（c）分析模拟结果是极其昂贵的。 能源模拟数据管理系统（e-SDMS）软件将解决由于需要以可扩展的方式对观测和模拟产生的大量多变量系列进行建模、索引、搜索、可视化和分析而产生的挑战。因此，e-SDMS将填补数据驱动建筑设计和清洁能源领域的一个重要空白（国家优先领域），并将使应用和服务具有重大的经济和环境影响。推动研究的关键观察结果是，许多对能源模拟具有迫切兴趣的数据集包括以下内容：（a）大量，（B）异质，（c）多变量，（d）时间，（e）相互关联（意味着感兴趣的参数彼此相关并且受建筑物结构的约束），以及（f）多分辨率（意味着模拟和观测涵盖数天至数月的数据，并可在不同的空间、时间和参数粒度上加以考虑）。此外，生成用于决策的适当的模拟集合通常需要多个模拟，每个模拟具有对应于略微不同但似乎合理的场景的不同参数设置。因此，通过数据管理软件支持在新的设置中模块化地重新使用现有的仿真结果，例如建筑模型的重新上下文化和模块化重组（或“草图”）以及在新的参数、新的建筑平面图和新的上下文下的仿真轨迹的if-then分析，可以获得建模和分析中的显著节省。 在开发能源模拟数据管理系统（e-SDMS）的过程中，该研究解决了当今难以进行数据驱动的能源模拟的关键数据挑战。这需要（a）新颖的建筑物模型、仿真迹线和传感器/致动迹线（BSS）数据模型以适应能量仿真数据和模型，（B）传感数据和仿真迹线沿着相应的建筑物模型的特征分析和索引，以及（c）用于仿真迹线的分析和探索以及用于新的建筑物规划和上下文元数据的模型的重新上下文化的算法。因此，这项研究将影响计算挑战，这些挑战来自于以可扩展的方式对能源观测和模拟产生的大量多变量系列进行建模、分析、索引、可视化、搜索和重组的需求。 E-SDMS包括：（a）eViz服务器，用作e-SDMS的前端;（B）eDMS中间件，用于特征提取、索引、模拟分析和草图绘制;以及（c）eStore后端，用于数据存储。 为了避免浪费和实现管理大型数据集所需的可扩展性，e-SDMS采用了新颖的多分辨率数据划分和资源分配策略。多分辨率数据编码、划分和分析算法是高效可计算的，利用大规模并行性，并产生高质量、紧凑的数据描述。