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

The key performance indicators of the BIM implementation process

Contemporarily some firms in the construction industry are attempting to adopt a BIM method of working. Each of these attempts reflects a varying BIM adoption philosophy and inevitably different BIM technologies, implementation strategies and roadmaps. On the other hand, all these attempts are often motivated to attain competitive advantages for product delivery in the market place. The question of what the best method of adopting BIM has not been answered yet. That is to say, it is required to identify a standard method that will benchmark the different BIM adoption cases by comparing the efficiency gains in these cases: a standard benchmarking method can help the stakeholders to decide on the most appropriate strategies for themselves. This paper explains the live experience of BIM adoption in a KTP (Knowledge Transfer Partnership) project, undertaken between the University of Salford and John McCall Architects practicing in the housing and regeneration fields, with a particular focus on a set of KPIs that have been developed and tested through the action research strategy in the project. Weighting of these KPI’s has been developed from an architectural business perspectiv

2008 Index of Higher Education Fundraising Performance: Summary of Annual Fund Key Performance Indicators

Analyses are based on fiscal year 2008 donor transactions from 33 public and 32 private universities and colleges

Використання Key performance indicators, Key risk indicators для визначення ефективностi управлiння інформацiйними активами підприємства

Стаття присвячена актуальнiй проблемi вимiрювання ефективностi дiяльностi пiдприємства та його готовностi до ризикiв в сферi iнформацiйної безпеки. Автор розглядає метод вимiрювання ефективностi управлiння iнформацiйними активами з використанням системи KPI та KRI з вiдповiдними метриками. Використання такої системи дозволяє пiдприємству виявити вектори, за якими необхiдно оптимiзувати процеси управлiння , виявити ризики для iнформацiйних систем пiдприємства на раннiх стадiях та розробити вiдповiднi превентивнi заходи

Driving continuous improvement

The quality of improvement depends on the quality of leading and lagging performance indicators. For this reason, several tools, such as process mapping, cause and effect analysis and FMEA, need to be used in an integrated way with performance measurement models, such as balanced scorecard, integrated performance measurement system, performance prism and so on. However, in our experience, this alone is not quite enough due to the amount of effort required to monitor performance indicators at operational levels. The authors find that IT support is key to the successful implementation of performance measurement-driven continuous improvement schemes

Numerical Key Performance Indicators for the Validation of PHM Health Indicators with Application to a Hydraulic Actuation System

In order to perform Prognostic and Health Management (PHM) of a given system, it is necessary to define some relevant variables sensitive to the different degradation modes of the system. Those variables are named Health Indicators (HI) and they are the keystone of PHM. However, they are subject to a lot of uncertainties when computed in real time and the stochastic nature of PHM makes it hard to evaluate the efficiency of a HI set before the extraction algorithm is implemented. This document introduces Numerical Key Performance Indicators (NKPI) for the validation of HI computed only from data provided by numerical models in the upstream stages of a PHM system development process. In order to match as good as possible the reality, the multiple sources of uncertainties are quantified and propagated into the model. After having introduced the issue of uncertain systems modeling, the different NKPI are defined and eventually an application is performed on a hydraulic actuation system of an aircraft engine

Key performance indicators for the National Bowel Cancer Screening Program: technical report

Provides a summary of the development process and the technical specification for the 11 agreed performance indicators that are part of the National Bowel Cancer Screening Program Performance Indicator Set. Summary Cancer contributes significantly to the burden of illness in the Australian community. Bowel cancer is one of the most significant cancer types in terms of incidence and mortality. In 2010, 14,860 people were diagnosed with bowel cancer and in 2011 there were 3,999 deaths from the disease. Screening for bowel cancer is available in Australia through the National Bowel Cancer Screening Program (NBCSP), which aims to reduce the incidence, illness and mortality related to bowel cancer through screening to detect cancers and pre-cancerous lesions in their early stages, when treatment is most successful. Reporting statistics about the NBCSP in a standardised way is vital to ensure that governments, researchers and health workers have access to relevant and reliable statistics about the performance of the program over time. This report describes the National Bowel Cancer Screening Program Performance Indicator Set (NBCSP PIs) and is a reference tool for anyone who wishes to understand, measure and report the progress of bowel cancer screening in Australia. The indicators were developed by the National Bowel Cancer Screening Program Report and Indicator Working Group (the working group) and have been endorsed by the Standing Committee on Screening, the Community Care and Population Health Principal Committee, the National Health Information Standards and Statistics Committee and the National Health Information and Performance Principal Committee. The indicators are consistent with the five Australian Population Based Screening Framework (PBSF) steps of recruitment, screening, assessment, diagnosis and outcomes

Automatic best wireless network selection based on key performance indicators

Introducing cognitive mechanisms at the application layer may lead to the possibility of an automatic selection of the wireless network that can guarantee best perceived experience by the final user. This chapter investigates this approach based on the concept of Quality of Experience (QoE), by introducing the use of application layer parameters, namely Key Performance Indicators (KPIs). KPIs are defined for different traffic types based on experimental data. A model for an ap- plication layer cognitive engine is presented, whose goal is to identify and select, based on KPIs, the best wireless network among available ones. An experimenta- tion for the VoIP case, that foresees the use of the One-way end-to-end delay (OED) and the Mean Opinion Score (MOS) as KPIs is presented. This first implementation of the cognitive engine selects the network that, in that specific instant, offers the best QoE based on real captured data. To our knowledge, this is the first example of a cognitive engine that achieves best QoE in a context of heterogeneous wireless networks