System-level key performance indicators for building performance evaluation

Quantifying building energy performance through the development and use of key performance indicators (KPIs) is an essential step in achieving energy saving goals in both new and existing buildings. Current methods used to evaluate improvements, however, are not well represented at the system-level (e.g., lighting, plug-loads, HVAC, service water heating). Instead, they are typically only either measured at the whole building level (e.g., energy use intensity) or at the equipment level (e.g., chiller efficiency coefficient of performance (COP)) with limited insights for benchmarking and diagnosing deviations in performance of aggregated equipment that delivers a specific service to a building (e.g., space heating, lighting). The increasing installation of sensors and meters in buildings makes the evaluation of building performance at the system level more feasible through improved data collection. Leveraging this opportunity, this study introduces a set of system-level KPIs, which cover four major end-use systems in buildings: lighting, MELs (Miscellaneous Electric Loads, aka plug loads), HVAC (heating, ventilation, and air-conditioning), and SWH (service water heating), and their eleven subsystems. The system KPIs are formulated in a new context to represent various types of performance, including energy use, peak demand, load shape, occupant thermal comfort and visual comfort, ventilation, and water use. This paper also presents a database of system KPIs using the EnergyPlus simulation results of 16 USDOE prototype commercial building models across four vintages and five climate zones. These system KPIs, although originally developed for office buildings, can be applied to other building types with some adjustment or extension. Potential applications of system KPIs for system performance benchmarking and diagnostics, code compliance, and measurement and verification are discussed

Toward Sustainable Energy-Independent Buildings Using Internet of Things

Buildings are one of the primary consumers of energy. In addition to the electricity grids, renewable energies can be used to supply the energy demand of buildings. Intelligent systems such as the Internet of Things (IoT) and wireless sensor technologies can also be applied to manage the energy consumption in buildings. Fortunately, integrating renewable energies with these intelligent systems enables creating nearly zero-energy buildings. In this paper, we present the results of our experimentation to demonstrate forming such a building and showing the benefits for building users and the society. We create a system by integrating photovoltaic (PV) technology with an IoT-based control mechanism to supply and consume energy. We further illustrate “how the integration of IoT and PV technology can bring added value to the users?”. To this end, we evaluate the performance of our system against conventional ways of energy supply and consumption for a lighting use case in a dairy store. We also investigate the environmental and economic impacts of our system. In our implementation, for the IoT-based control system, we have used a set of sensors, a server, and a wireless network to control the energy consumption. We developed a web application for user interaction and software-based settings. To control the lighting system, we developed an algorithm that utilizes the ambient light, users’ movements inside the store and a historical dataset. The historical dataset was collected from the users’ behaviour as a training set for the algorithm for turning on and off the lights. We also designed an electricity management system that computes the energy generation by the PV panels, controls the energy supply, and imports and exports electricity to the grid. The results show that our system is an efficient approach for creating energy-independent buildings by integrating renewable energies with IoT-based control systems. The results also show that our system not only responds to the internal demand by using domestic supply, but it also (i) offers economic benefit by exporting extra renewable electricity to the grid, and (ii) prevents producing huge amounts of CO2. Our system is one of the first works to achieve a nearly zero-energy building in the developing countries with low electricity accessibility

Toward Sustainable Energy-Independent Buildings Using Internet of Things

Buildings are one of the primary consumers of energy. In addition to the electricity grids, renewable energies can be used to supply the energy demand of buildings. Intelligent systems such as the Internet of Things (IoT) and wireless sensor technologies can also be applied to manage the energy consumption in buildings. Fortunately, integrating renewable energies with these intelligent systems enables creating nearly zero-energy buildings. In this paper, we present the results of our experimentation to demonstrate forming such a building and showing the benefits for building users and the society. We create a system by integrating photovoltaic (PV) technology with an IoT-based control mechanism to supply and consume energy. We further illustrate “how the integration of IoT and PV technology can bring added value to the users?”. To this end, we evaluate the performance of our system against conventional ways of energy supply and consumption for a lighting use case in a dairy store. We also investigate the environmental and economic impacts of our system. In our implementation, for the IoT-based control system, we have used a set of sensors, a server, and a wireless network to control the energy consumption. We developed a web application for user interaction and software-based settings. To control the lighting system, we developed an algorithm that utilizes the ambient light, users’ movements inside the store and a historical dataset. The historical dataset was collected from the users’ behaviour as a training set for the algorithm for turning on and off the lights. We also designed an electricity management system that computes the energy generation by the PV panels, controls the energy supply, and imports and exports electricity to the grid. The results show that our system is an efficient approach for creating energy-independent buildings by integrating renewable energies with IoT-based control systems. The results also show that our system not only responds to the internal demand by using domestic supply, but it also (i) offers economic benefit by exporting extra renewable electricity to the grid, and (ii) prevents producing huge amounts of CO2. Our system is one of the first works to achieve a nearly zero-energy building in the developing countries with low electricity accessibility

Doubling Energy Efficiency at the University of Michigan by 2030

Approximately 84 million Americans spend their days in colleges, universities, and public or private primary and secondary schools.ii The commercial building sector, which includes educational institutions, accounts for 18.44 percent of overall energy consumption in the United States.iii Education buildings are ranked third highest of all commercial buildings, consuming over 600 trillion Btus of energy each year.iv Given these consumption levels, educational institutions have an opportunity to make a significant impact to increase energy efficiency in this country. The University of Michigan (herein, also “the University” or “UM”) has been working diligently to be leaders in this charge. In 2012, the Alliance to Save Energy proposed a goal of doubling energy productivity in the United States by 2030, thereby getting twice as much economic output for every unit of energy input.v This goal inspired Johnson Controls, Inc. (herein, “Johnson Controls” or “JCI”) to approach the University with a Master’s Project, enabling a group of students to learn from the expertise of Johnson Controls, and to be active participants in sustainability efforts at the University of Michigan. Additionally, the findings and recommendations developed to increase energy productivity on campus should likely contribute towards the University’s existing sustainability goal of reducing greenhouse gas (GHG) emissions. This project seeks to harness the knowledge, technology and best practices honed by Johnson Controls from decades of experience in energy conservation projects, as well as the expertise from the University of Michigan, including various professionals and organizations that actively work towards energy efficiency measures on campus. Leveraging these and other resources, our six graduate student member team (Appendix A) analyzed the University of Michigan’s current energy demand and management. We learned about the extensive work the energy management team has already been doing for several decades in some areas on campus, and about what opportunities there are for improvement. Our master's project team identified several recommendations for furthering the collective energy efficiency performance of the University, as well as recommendations on measures that can be taken in the Samuel T. Dana building (herein, the “Dana building”), which serves as a case study for the project. The key findings and recommendations, both campus-wide and for the Dana building, are detailed here.Master of ScienceNatural Resources and EnvironmentUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/117588/3/Doubling Energy Efficiency at the University of Michigan by 2030.pd

An Evaluation of the AH-64 Night Vision Systems for use in 21st Century Urban Combat

Currently one of the most arduous and dangerous aviation missions for the military attack helicopter pilot is the night combat mission. The mission entails flight at close proximity to the ground and obstacles such as wires, trees, and buildings in an effort to avoid detection by enemy air defense and insurgent small arms fire. Night flight requires the use of augmented vision systems and enhanced aircraft stability and control systems to allow pilots to effectively see and negotiate those hazards that would otherwise be visible during daylight. The U.S. Army currently fields two variants of augmented visionics, the Aviator Night Vision Imaging System (ANVIS) and the Pilot Night Vision System/Target Acquisition and Designation System (PNVS/TADS). ANVIS is a portable Image Intensification (I2) system usable by all Army airframes whereas PNVS and TADS are both forward looking infrared (FLIR) subcomponents attached to the nose of the AH-64 attack helicopter. Since aviators began using augmented vision systems complaints have been registered regarding loss of static and dynamic cues, presence of visual illusions and other visual symptoms. Currently the mission has grown to encompass urban and suburban reconnaissance and security operations using systems designed in the late 1970’s for transitioning to a battle position and near stationary engagement of heavy armor forces. This study evaluated both systems in use by AH-64D aviators serving in and around Baghdad, Iraq from November 2005 thru October 2006. Whereas previous studies concentrated solely on visual symptoms and complaints associated with IHADSS use, this was the first study of both the FLIR and I2 used in combination by AH-64 cockpit crews. In the constant-moving environment of aerial reconnaissance and security, I2 is preferable to the IHADSS by a majority of AH-64D pilots. Additionally, results showed a predominant favoring of the ANVIS over the PNVS/TADS for wire and aircraft avoidance due in large part to the enhanced visual acuity (20/25) of the ANVIS as compared to the 20/60 visual acuity of the IHADSS. The visual acuity disparity led to consistent reporting of insufficient visual cues by IHADSS users. The primary benefit, as seen by pilots, of the PNVS/TADS system was the flight symbology cues provided through the helmet mounted display. Through training and education the data is received as stimuli and converted into usable 3-D cues for improved situational awareness. As with all previous studies, visual symptoms associated with IHADSS use were present

Evaluating building energy performance: a lifecycle risk management methodology

There is widespread acceptance of the need to reduce energy consumption within the built environment. Despite this, there are often large discrepancies between the energy performance aspiration and operational reality of modern buildings. The application of existing mitigation measures appears to be piecemeal and lacks a whole-system approach to the problem. This Engineering Doctorate aims to identify common reasons for performance discrepancies and develop a methodology for risk mitigation. Existing literature was reviewed in detail to identify individual factors contributing to the risk of a building failing to meet performance aspirations. Risk factors thus identified were assembled into a taxonomy that forms the basis of a methodology for identifying and evaluating performance risk. A detailed case study was used to investigate performance at whole-building and sub-system levels. A probabilistic approach to estimating system energy consumption was also developed to provide a simple and workable improvement to industry best practice. Analysis of monitoring data revealed that, even after accounting for the absence of unregulated loads in the design estimates, annual operational energy consumption was over twice the design figure. A significant part of this discrepancy was due to the space heating sub-system, which used more than four times its estimated energy consumption, and the domestic hot water sub-system, which used more than twice. These discrepancies were the result of whole-system lifecycle risk factors ranging from design decisions and construction project management to occupant behaviour and staff training. Application of the probabilistic technique to the estimate of domestic hot water consumption revealed that the discrepancies observed could be predicted given the uncertainties in the design assumptions. The risk taxonomy was used to identify factors present in the results of the qualitative case study evaluation. This work has built on practical building evaluation techniques to develop a new way of evaluating both the uncertainty in energy performance estimates and the presence of lifecycle performance risks. These techniques form a risk management methodology that can be applied usefully throughout the project lifecycle