Whole building energy simulation (BES) models play a significant role in the design and optimisation of buildings. Simulation models may be used to compare the cost-effectiveness of energy-conservation measures (ECMs) in the design stage as well as assessing various performance optimisation measures during the operational stage. However, due to the complexity of the built environment and prevalence of large numbers of independent interacting variables, it is difficult to achieve an accurate representation of real-world building operation. Therefore, by reconciling model outputs with measured data, we can achieve more accurate and reliable results. This reconciliation of model outputs with measured data is known as calibration. This paper presents a detailed review of current approaches to model development and calibration, highlighting the importance of uncertainty in the calibration process. This is accompanied by a detailed assessment of the various analytical and mathematical/statistical tools employed by practitioners to date, as well as a discussion on both the problems and the merits of the presented approaches.
a b s t r a c tWithin the operational phase buildings are now producing more data than ever before, from energy usage, utility information, occupancy patterns, weather data, etc. In order to manage a building holistically it is important to use knowledge from across these information sources. However, many barriers exist to their interoperability and there is little interaction between these islands of information.As part of moving building data to the cloud there is a critical need to reflect on the design of cloudbased data services and how they are designed from an interoperability perspective. If new cloud data services are designed in the same manner as traditional building management systems they will suffer from the data interoperability problems.Linked data technology leverages the existing open protocols and W3C standards of the Web architecture for sharing structured data on the web. In this paper we propose the use of linked data as an enabling technology for cloud-based building data services. The objective of linking building data in the cloud is to create an integrated well-connected graph of relevant information for managing a building. This paper describes the fundamentals of the approach and demonstrates the concept within a Small Medium sized Enterprise (SME) with an owner-occupied office building.
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