Multi-Criteria optimisation using past, real time and predictive performance benchmarks

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Abstract

Performance based design, construction, commissioning and operation of buildings requires virtual testing and validation of project alternatives. In the case of environmental and energy management of buildings, whole Building Energy Simulation (BES) models can be used to determine indoor environmental conditions, building energy consumption, system performance, and associated CO2 emissions, etc. BES is currently used at the design and commissioning phases of the Building Life Cycle (BLC) but not during the operational phase. This paper defines a methodology that incorporates predictive BES into building operation while acknowledging present technological limitations with respect to model accuracy and required resources. This predictive model also requires detailed definition and characterisation of inputs including: Historical data from buildings; Real time data such as measurements from meters and wired and wireless sensors underpinned by a Building Management System (BMS) and Future data such as short term weather forecast values and expected occupancy schedules. The paper concludes with a demonstration of the predictive BES model methodology using an existing building at University College Cork, Ireland.

Introduction

Globally, buildings account for approximately 40% of global energy consumption but building performance during operation does not normally reflect the original design expectations [1]. Research projects have developed IT methodologies and tools to address the complex problem of building performance assessment. The common goal of these projects is to support the building manager in reducing building energy consumption without compromising environmental performance.

European member states are currently implementing formal certification and assessment of Buildings. The Energy Performance of Buildings Directive [2] prescribes energy certificates for new and existing buildings. Certification is a one-time event and subsequent to this evaluation building performance is not continuously monitored, thus making it difficult to maintain optimum operation.

This research work focuses on building energy performance during the operation phase of the Building Life Cycle (BLC) and is an integral part of the ITOBO project (Information and Communication Technology for Sustainable and Optimised Building Operation) [3]. Optimised Building Operation integrates radio frequency identification (RFID), wireless sensors, mobile technologies and building information models (BIM) with repair and maintenance activities.

This paper describes a review of currently available and emerging methodologies aimed at monitoring and reducing building energy consumption. This paper proposes a new performance based methodology that will be adopted as part of the ITOBO project. A brief case study is discussed to demonstrate the application of the methodology.

Section snippets

Available methodologies

Each and every building is unique and its use evolves over time. Operation objectives reflected by building control strategies should be coupled with present building usage. In practice, intended operation may match actual building use at the design and construction stages but during operation building function evolves without a corresponding change to the Building Management System. In the absence of regular assessments, intended and actual building operation will continue to diverge.

Proposed methodology

The methodology presented in this paper introduces automation of Fault Detection process to support performance assessment and optimisation, and focuses on the building specific benchmarks required for this. The methodology accounts for the benefits of automated Continuous Commissioning tools, which reduce both the costs and time associated with commissioning. The methodology also reduces the level of technical knowledge required, thus making it accessible to building managers.

This systematic

Case study

The Environmental Research Institute (ERI) building, located at University College Cork (UCC), was designed as a green flagship building and a low energy research facility [13]. It is a combination of both lab and office space requirements. Such facilities are often dismissed as too complex and specialized for employing a sustainable design approach. Operation of these facilities while managing low levels of energy consumption is notoriously difficult to achieve [14]. However, recent studies

Conclusions

This paper analyses the available methodologies for the building operation assessment and proposes a methodology that defines automated Fault Detection to support the application of CC. The core part of the research work within this methodology lies on the identification and classification of the inputs used in the Fault Detection process, as well as on the supply of all the information required by the Building Manager for the decision making process.

This automated process is based on the

References (17)

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