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

End-use demand in commercial office buildings: case-study and modelling recommendations

While considerable progress has been made on developing high-resolution stochastic models of electricity demand for the domestic sector, non-domestic models remain relatively undeveloped. This paper provides general recommendations about how such models might be structured for commercial offices, based on detailed analysis of high-resolution end-use demand data for a single multi-tenanted office building. The results indicate that modelling of commercial office buildings could be viewed as analogous to modelling a group of dwellings with partial residency (to represent individual office units within the building), with communal heating and communal spaces, a limited number of work related appliances, and occupant activities restricted to those related to work

Probabilistic evaluation of UK domestic solar photovoltaic systems: An integrated geographical information system PV estimation tool

It is shown how key predictor parameters for the spatial estimation of PV yield, self -consumption and thereby economic and social indicators can be extracted from a GIS system and introduced into a Bayesian Network model. This model endogenises the uncertainties and incorporates spatial variability inherent in these parameters. Empirical monthly and annual yield measurements obtained from over 600 PV installations have been obtained and compared with estimated yields obtained by two key solar tools used for performance estimation in the UK – these are PVGIS and the UK Government’s Standard Assessment Procedure (SAP) for domestic buildings. Mean bias estimates and root mean square error estimations were obtained for each tool and the results used to construct an uncertainty distribution in PV yield prediction given key input parameters such as system rating, orientation and tilt. This uncertainty was used to furnish a probabilistic graphical model with a prior distribution for PV yield estimation. This was integrated into a Geographical Information (GIS) system furnished with roof and building stock parameters including roof attributes obtained from lidar data. Elements held in a vector layer of the GIS system can be selected and the resultant distributions of input parameters automatically fed to the model to yield a posterior distribution of the PV yield. The model is able to propagate the yield uncertainty to other probabilistic models, including ones which predict the internal rate of return and self -consumption. The latter is in turn predicted by empirical marginal distributions of domestic electricity consumption. Thus with a given posterior distributions of PV yield, new posterior distributions for the internal rate of return, self-consumption and carbon emission savings are automatically calculated. By integration with GIS this novel approach allows the spatial analysis of the uncertainty pertaining to representative risk factors for PV adoption in the UK, and facilitate the estimation by installers, investors, and local authorities in a manner which endogenises uncertainty