A Comprehensive Energy Monitoring Environment for District Energy Grid Systems (original) (raw)
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Building energy information systems: user case studies
Energy Efficiency, 2010
Measured energy performance data are essential to national efforts to improve building efficiency, as evidenced in recent benchmarking mandates, and in a growing body of work that indicates the value of permanent monitoring and energy information feedback. This paper presents case studies of energy information systems (EIS) at four enterprises and university campuses, focusing on the attained energy savings, and successes and challenges in technology use and integration. EIS are broadly defined as performance monitoring software, data acquisition hardware, and communication systems to store, analyze and display building energy information. Case investigations showed that the most common energy savings and instances of waste concerned scheduling errors, measurement and verification, and inefficient operations. Data quality is critical to effective EIS use, and is most challenging at the subsystem or component level, and with non-electric energy sources. Sophisticated prediction algorithms may not be well understood but can be applied quite effectively, and sites with custom benchmark models or metrics are more likely to perform analyses external to the EIS. Finally, resources and staffing were identified as a universal challenge, indicating a need to identify additional models of EIS use that extend beyond exclusive in-house use, to analysis services.
Building Energy Monitoring and Analysis
2013
U.S. and China are the world's top two economics. Together they consumed one-third of the world's primary energy. It is an unprecedented opportunity and challenge for governments, researchers and industries in both countries to join together to address energy issues and global climate change. Such joint collaboration has huge potential in creating new jobs in energy technologies and services. Buildings in the US and China consumed about 40% and 25% of the primary energy in both countries in 2010 respectively. Worldwide, the building sector is the largest contributor to the greenhouse gas emission. Better understanding and improving the energy performance of buildings is a critical step towards sustainable development and mitigation of global climate change. This project aimed to develop a standard methodology for building energy data definition, collection, presentation, and analysis; apply the developed methods to a standardized energy monitoring platform, including hardware and software, to collect and analyze building energy use data; and compile offline statistical data and online real-time data in both countries for fully understanding the current status of building energy use. This helps decode the driving forces behind the discrepancy of building energy use between the two countries; identify gaps and deficiencies of current building energy monitoring, data collection, and analysis; and create knowledge and tools to collect and analyze good building energy data to provide valuable and actionable information for key stakeholders. Key research findings were summarized as follows: 1. Identified the need for a standard data model and platform to collect, process, analyze, and exchange building performance data due to different definitions of energy use and boundary, difficulty in exchanging data, and lack of current standards. 2. Compared energy monitoring systems to identify gaps, including iSagy, Pulse Energy, SkySpark, sMap, EPP, ION, and Metasys. 3. Contributed to develop a standard data model to represent energy use in buildings (ISO standard 12655 and a Chinese national standard) 4. Determined that buildings in the United States and China are very different in design, operation, maintenance, occupant behavior: U.S. buildings have more stringent comfort standards regarding temperature, ventilation, lighting, and hot-water use and therefore higher internal loads and operating hours, and China buildings having higher lighting energy use, seasonal HVAC operation, more operators, more use of natural ventilation, less outdoor ventilation air, and wider range of comfort temperature. 5. Completed data collection for six office buildings, one in UC Merced campus, one in Sacramento, one in Berkeley, one in George Tech campus, and two in Beijing. 6. Compiled a source book of 10 selected buildings in the United States and China with detailed descriptions of the buildings, data points, and monitoring systems, and containing energy analysis of each building and an energy benchmarking among all buildings. 7. Recognized limited availability of quality data, particularly with long periods of time-interval data, and general lack of value for good data and large datasets. 8. Compiled a building energy database, with detailed energy end use at 1-hour or 15-minute time interval, of six office buildings-four in the U.S. and two in China. The database is available to the public and is a valuable resource for building research. 9. Developed methods and used them in data analysis of building performance for the five buildings with adequate data, including energy benchmarking, profiling (daily, weekly, monthly), and diagnostics. 10. Recommended energy efficiency measures for building retrofit in both countries. U.S. buildings show more potential savings by reducing operation time, reducing plug-loads, expanding comfort temperature range, and turning off lights or equipment when not in use; while Chinese buildings can save energy by increasing lighting system efficiency, and improving envelope insulation and HVAC equipment efficiency. The research outputs from the project can help better understand energy performance of buildings, improve building operations to reduce energy waste and increase efficiency, identify retrofit opportunities for existing buildings, and provide guideline to improve the design of new buildings. The standardized energy monitoring and analysis platform as well as the collected real building data can also be used for other CERC projects that need building energy measurements, Research Team and Collaboration The joint research team (Table 1) includes the LBNL team, the ORNL team, the U.S. industry partners, the Tsinghua team and the China industry partners. Tianzhen Hong of LBNL led the U.S. side research and Jianjun Xia of Tsinghua University led the China side research. Richard Karney and Yi Jiang served as the senior technical advisors for the project. The research team had biweekly conference calls to discuss project progress and resolve issues. The team organized a series of workshops (Appendix B) to exchange research findings, seek inputs and comments from researchers, practitioners, industry partners, HVAC manufacturers, government agencies, and other stakeholders. The joint research work also made significant contribution to the IEA Annex 53. Exchanged students from Tsinghua University stayed at LBNL for a few months to work on joint technical tasks.
2016
Rich, well-organized building performance and energy consumption data enable a host of analytic capabilities for building owners and operators, from basic energy benchmarking to detailed fault detection and system optimization. Unfortunately, data integration for building control systems is challenging and costly in any setting. Large portfolios of buildings--campuses, cities, and corporate portfolios--experience these integration challenges most acutely. These large portfolios often have a wide array of control systems, including multiple vendors and nonstandard communication protocols. They typically have complex information technology (IT) networks and cybersecurity requirements and may integrate distributed energy resources into their infrastructure. Although the challenges are significant, the integration of control system data has the potential to provide proportionally greater value for these organizations through portfolio-scale analytics, comprehensive demand management, an...
Preliminary findings from an analysis of building Energy Information System technologies
2009
Energy information systems comprise software, data acquisition hardware, and communication systems that are intended to provide energy information to building energy and facilities managers, financial managers, and utilities. This technology has been commercially available for over a decade, however recent advances in Internet and other information technology, and analytical features have expanded the number of product options that are available. For example, features such as green house gas tracking, configurable energy analyses and enhanced interoperability are becoming increasingly common.
Building energy management and data analytics
2015 International Symposium on Smart Electric Distribution Systems and Technologies (EDST), 2015
Energy efficiency in buildings depends on the way the building is operated. Therefore energy management is the key component for efficient operation. Data analysis of operation data helps to better understand the systems and detect faults and inefficiencies. The facility manager benefits from smart analysis that makes use of machine learning algorithms and innovative visualizations. This analysis is part of a bigger review of the current structure of building automation as it is used in today's buildings. The operation targets in energy efficiency are complex, ambiguous and contradictory: indoor comfort, energy efficiency, high availability and low costs cannot be met at the same time. In order to improve building operation, a novel model of automation is discussed. The foundation of this model is in cognitive automation, since each building is unique in its selection of energy sources, architecture, usage and location, which implies that the building's control system has to be adapted individually. This paper connects the data-driven analysis of operation data with a cognitive concept to be used for operating the energy systems in a building and regarding goals on how to optimally operate while considering constraints about the limits of operation, using the complex, dynamic data from building automation.
A Data-driven Situational Awareness Approach to Monitoring Campus-wide Power Consumption
AIAA Propulsion and Energy Forum, 2018
The emerging of big data and data analytics has been an enabler to address key technical challenges in the energy sector. Situational awareness capabilities such as real-time sensor monitoring, anomaly detection, and timely anomalies warning are imperative to the facility operator of large scale energy distributing network. As a part of Georgia Tech’s Smart Campus Initiative, substantial volume and varieties of data obtained from multiple layers of sensor networks throughout the Georgia Tech campus are used to support situational awareness through real-time operations monitoring and energy usage tracking and assessment. More specifically, in this paper, a proposed data-driven approach is presented to model, simulate, and forecast the short-term electricity demand by factoring in available time information and weather data. The predictive results from the model are compared with the online readings collected from electricity meters of interest deployed onsite. As such, once an anomaly is detected and diagnosed with a sufficient confidence level, a corresponding warning can be issued to appropriate facilities personnel in a prompt manner, increasing the possibility to prevent potential disruption to the day-to-day campus operation.
Visual Analytics for Energy Monitoring in the Context of Building Management
2018 International Symposium on Big Data Visual and Immersive Analytics (BDVA), 2018
Building management systems (BMS) provide monitoring and control of most large-building assets (heating, ventilation, air conditioning, lighting, security systems, and so on). With the recent advancement of the Internet of Things and data management systems, BMS must gather and manage increasingly detailed data coming from a greater number and diversity of sources. The availability of such data should help building managers optimise the energy consumption of buildings. However, current BMS don't allow efficient visualisation of such data, which means that even if the data is available, it is not used to its full potential. In this paper, we describe a prototype BMS interface providing interactive visualisations of traditional building data (temperature, energy consumption), as well as more novel data (comfort feedback from occupants and live occupancy). We evaluate this prototype by first showing how it could be used to plan a long-term energy saving strategy, and then in a feedback session involving facility managers at a university.
Energy Management Information Systems for Energy Efficiency
IEEE Transactions on Industry Applications, 2019
An Energy Management Information System (EMIS) combines software, hardware, and data to support people in their efforts to manage energy at the process, system, facility, and enterprise level, year after year. A distributed measurement and monitoring system DMS is a fundamental part of an EMIS; it is constituted by the meters distributed in the switchboards of the power system. The paper suggests a method of assessment of a DMS based on an indicator called Level of Coverage (LOC). This indicator can evaluate the level of coverage of a single meter or of a whole system considering the classification in significant energy uses according to the standard ISO 50001.