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

2009年，建筑行业的二氧化碳排放量占美国二氧化碳排放量的近一半。根据美国能源情报署的数据，建筑消耗的能源比其他任何行业都多，占总能源消耗的48.7%，预计建筑能源消耗的增长速度将超过工业和交通部门。为此，到2030年，预计只有18%的美国建筑将依赖当前的能源管理技术，其余的要么进行改造，要么使用智能和更清洁的能源技术进行从头设计。这些建筑能源管理系统（BEMSs）需要整合大量数据，包括(a)连续收集的供暖、通风和空调（HVAC）传感器和驱动数据，(b)其他感官数据，如占用率、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)提供上下文信息的本地是否和GIS数据，以及(e)能源价格。电力（如智能电网）和天然气公用事业的消耗和成本数据。理论上，这些数据可以利用数据驱动的建筑优化（包括对建筑位置、朝向和替代节能策略的评估）、总拥有成本（tco）模拟工具和日常运营决策，从建筑物的初始设计和/或改造中加以利用。然而，在实践中，由于数据的大小和复杂性，关键过程运行的不同时空尺度，(a)创建支持此类模拟的模型，(b)执行涉及跨越多个时空框架的数百个相互依赖参数的模拟，受到以不同分辨率运行的复杂动态过程的影响，以及(c)分析模拟结果的成本极高。能源模拟数据管理系统（e-SDMS）软件将以可扩展的方式解决由观测和模拟产生的大量多变量序列的建模、索引、搜索、可视化和分析需求所带来的挑战。因此，e-SDMS将填补数据驱动型建筑设计和清洁能源（国家优先考虑的领域）的重要空白，并将使应用和服务具有重大的经济和环境影响。推动这项研究的关键观察结果是，许多对能源模拟迫切感兴趣的数据集包括以下内容：(a)大量的，(b)异质的，(c)多变量的，(d)时间的，(e)相互关联的（意思是感兴趣的参数相互依赖并受到建筑物结构的限制），以及(f)多分辨率的（意思是模拟和观测覆盖了几天到几个月的数据，可以在不同的空间、时间和参数粒度上进行考虑）。此外，为决策生成适当的模拟集合通常需要多个模拟，每个模拟都有不同的参数设置，对应于略有不同但似乎合理的场景。因此，通过支持在新设置中模块化重用现有仿真结果的数据管理软件，例如建筑模型的重新上下文化和模块化重组（或“草图”），以及在新参数、新建筑平面图和新环境下对仿真轨迹进行if-then分析，可以大大节省建模和分析的费用。在开发能源模拟数据管理系统（e-SDMS）时，该研究解决了使数据驱动的能源模拟在今天变得困难的关键数据挑战。这需要(a)一种新的建筑模型、仿真轨迹和传感器/驱动轨迹（BSS）数据模型来适应能源仿真数据和模型，(b)对感官数据和仿真轨迹以及相应的建筑模型进行特征分析和索引，以及(c)用于分析和探索仿真轨迹以及为新建筑规划和上下文元数据重新情境化模型的算法。因此，这项研究将影响计算方面的挑战，这些挑战来自于建模、分析、索引、可视化、搜索和重组的需要，以一种可扩展的方式，大量由能量观测和模拟产生的多变量序列。E-SDMS包括：(a) eViz服务器，作为E-SDMS的前端；(b) eDMS中间件，用于特征提取、索引、仿真分析和草图绘制；(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分区和资源分配策略。多分辨率数据编码、划分和分析算法是可高效计算的，利用大量并行性，并产生高质量、紧凑的数据描述。

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.