更好的建筑能源管理GeoBMS

K. Krishnamurthy, P. Singh, N. Sriraman
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摘要

建筑能源使用的优化提出了一个有影响力的和容易解决的行业机会。在过去的十年中,商业建筑运营商已经投资于内部部署的建筑管理系统(bms),以集中监控和操作建筑传感器和控制器。由于建筑物占用模式的变化以及持续的传感器和控制器升级,BMS配置会随着时间的推移而退化。最近的研究表明,如果维持最佳的BMS配置,将有额外10%的节能机会。建筑运营商在保持BMS配置优化方面面临着重大挑战。原因有很多。首先,大多数bms提供专有接口,需要定制的、一次性的远程访问集成。其次,不一致的BMS数据表示使得聚合和分析性能数据以最大效率运行系统变得困难。第三,bms通常被设计为单用户应用程序,这为支持共同决定最佳使用的多个涉众创造了复杂性。我们提出了一种混合云/内部部署模型,该模型解决了当前内部部署BMS实现的局限性,并结合了新云技术的优势。我们的混合模型采用基于云的基础设施“中间层”(我们称之为GeoBMS),它将构建性能应用程序的“顶层”与现有棕地BMS实现的“底层”连接起来。GeoBMS通过虚拟化解决了BMS的不可访问性;通过通用云数据模型表示不一致的数据;以及缺乏通过全球认证的多利益相关者访问。通过发布的api, GeoBMS能够创建创新的建筑性能应用程序。应用程序使用GeoBMS api访问以前不可用的本地BMS功能和配置数据。我们使用一个概念验证应用程序(我们称之为EnergyOptimize)进行演示,该应用程序为一个博物馆案例示例优化能源消耗。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
GeoBMS for Better Building Energy Management
Optimization of building energy usage presents an impactful and readily addressable industry opportunity. Commercial building operators have, over the past decade, invested in on-premise Building Management Systems (BMSs) to centrally monitor and operate building sensors and controllers. BMS configurations degrade over time due to changes in building occupancy patterns as well as from ongoing sensor and controller upgrades. Recent studies reveal that an additional 10% energy savings opportunity would be available if optimal BMS configurations were sustained. Building operators face significant challenges in keeping BMS configurations optimized. The reasons are many. First, most BMSs offer proprietary interfaces that require custom, one-off integrations for remote access. Second, inconsistent BMS data representation makes it hard to aggregate and analyze performance data in order to operate systems with maximum efficiency. Third, BMSs are often designed as single user applications, creating complications to support multiple stakeholders that collectively dictate optimal usage. We propose a hybrid cloud/on-premise model that addresses the limitations of current, on-premise BMS implementations and incorporates the benefits of new cloud technologies. Our hybrid model employs a cloud-based infrastructure “middle layer” (which we call GeoBMS) that connects the “top layer” of building performance applications with the “bottom layer” of existing brownfield BMS implementations. GeoBMS addresses BMS inaccessibility through virtualization; inconsistent data representation through common cloud data models; and lack of multi-stakeholder access through global authentication. Through published APIs, GeoBMS enables the creation of innovative building performance applications. Applications use GeoBMS APIs to access previously unavailable on-premise BMS functionality and configuration data. We illustrate using a proof-of-concept application (which we call EnergyOptimize) that optimizes energy consumption for a museum case-example.
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