Advanced Analysis of Building Energy Performance using Computational Intelligence Approaches
Lead Research Organisation:
Loughborough University
Department Name: Civil and Building Engineering
Abstract
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People |
ORCID iD |
| Jonathan Wright (Principal Investigator) |
| Description | The project investigated the use of calibrated models to identify the separate base, heating, and cooling energy use from metered total energy use. Both steady-state and simplified lumped capacitance models where evaluated, with both models being calibrated to the metered data using an Evolutionary Algorithm (EA). The calibration task is challenging in terms of the number of calibration variables, but the EA proved to be effective in calibrating the models. The (dynamic) lumped-capacitance model showed some improvement in model accuracy over the steady-state model. As well as identifying the separate load elements, the model results could also be interpreted to identify the different time-dependent operating modes of the energy systems. The research constituted a successful proof of concept, with further challenges being associated with the formulation of models that have a tangible physical meaning. Formulation of such models would allow the approach to be used directly in a comparison of performance enhancements resulting from the refurbishment of the building energy systems (by examining the changes in the model parameters from pre and post refurbishment calibration of the model). |
| Exploitation Route | The approach investigated in the research has a potential application in the monitoring of building energy use, and in particular, the extent to which energy use is divided between a base-load, heating load, and cooling loads; the approach can also be used to identify the time-dependency of the energy system operation. With further development of the models, the approach may be used in evaluating the impact of building refurbishment through the examination of the model parameters calibrated with data from pre and post refurbishment periods. The approach might be exploited through its development by building energy control system providers and providers of energy facilities management software. |
| Sectors | Construction,Digital/Communication/Information Technologies (including Software),Energy |
| Description | The findings have been used in related research by the lead partner, Birmingham University (see EP/F062567/1) |
| First Year Of Impact | 2009 |
| Sector | Digital/Communication/Information Technologies (including Software) |
| Description | AEC Environmental Ltd |
| Organisation | Airborne Environmental Consultants (AEC) |
| Country | United Kingdom |
| Sector | Private |
| Start Year | 2008 |
| Description | Birmingham |
| Organisation | University of Birmingham |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | The research grant EP/F062222/1 held by Loughborough University was subsidiary to the main grant held by Birmingham University EP/F062567/1. Birmingham University were project instigators and managers, and provided all data and computer science input to the project. |
| Collaborator Contribution | Loughborough University provided knowledge on building energy and building energy systems, this being the focus of the modelling methods to be developed and evaluated by Birmingham University. The funding was to cover overheads for the Loughborough investigators time and for travel. |
| Impact | The project resulted in an understanding of the applicability of computational intelligence methods in building energy monitoring and analysis, but no further tangible outcomes have resulted from the collaboration. |
| Start Year | 2008 |
| Description | Optima Energy Management |
| Organisation | Optima Energy Management |
| Country | United Kingdom |
| Sector | Private |
| Start Year | 2008 |