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This paper presents the application of multi-objective genetic algorithms for holistic building design that considers multiple criteria; building energy use, capital cost, daylight distribution and thermal indoor environment. The optimization focus is related to building envelope parameters. To obtain relevant feedback from multi-objective optimizations in early design stages, evaluation speed is a key concern. The paper presents a fast evaluation method fit for the early design stages. It uses a combination of two different quasi-steady-state methods for energy and indoor environment evaluations, a Radiance implementation for daylight simulations and a scripted algorithm for capital cost evaluations. The application of the method is developed around an integrated dynamic model which allows visual design feedback from all evaluations to be an integrated part of the design tool experience. It is concluded, that quasi-steady-state methods implemented as part of integrated dynamic models are fast and flexible enough to support building energy-, indoor environment- and cost-optimization the early design stages. [ABSTRACT FROM AUTHOR]

Lighting systems in buildings have substantial influence on both the occupants' productivity and energy efficiency. This paper addresses the problem of minimizing the energy necessary to provide predefined scheduled lighting levels upon a set of targets through an optimization technique. An experimental setup has been designed, built and used to gather data regarding the influences of five light sources upon three targets. The optimization problems corresponding to different lighting demands were solved with the recently introduced Competition Over Resources bio-inspired metaheuristic optimization algorithm. Modifications on this algorithm were proposed in order to enhance its performance in terms of convergence speed and computational effort. The modified algorithm was firstly tested with classical benchmark functions, and then with a real time lighting problem. Once algorithm performance improvements were verified, the energy consumption over a scheduled lighting demand was measured under different test conditions as to evaluate potential savings whilst minimizing the computational cost of running the algorithm. Using the proposed modifications, the best parameters for each lighting demand condition in the predefined schedule have been identified. By using these parameters along with a strategy of reusing the best solution while the lighting demand does not change, smaller population sizes in the algorithm could be used. Finally, the minimum algorithm population size at which energetic performance begins to be affected was identified. Promising results in terms of energy efficiency and reduced computational effort were achieved. [ABSTRACT FROM AUTHOR]

Real-time optimization (RTO) has been developed to improve the cost and/or energy efficiency of complex heating, ventilation and air-conditioning (HVAC) systems. In current literature, almost all of the developed real-time optimization methods belong to the type of time-driven optimization, in which the action of optimization is triggered by “time”. As optimization should be done when the system operating conditions experience a change that is large enough to cause current operational setting not optimal any more, optimization strategies should recognize ‘significant’ changes and perform optimization when necessary. Since the time-driven optimization is a periodic mechanism in nature while those ‘significant’ changes may not be periodic, the time-driven optimization cannot capture ‘significant’ changes and do the optimization promptly. Therefore, this paper proposes an event-driven optimization (EDO) for complex HVAC systems, the key idea of which is to use “event” rather than “time” to trigger the action of optimization. A systematic event-driven optimization method is illustrated, where its main tasks, including event definition and event identification, will be discussed. The proposed method will be compared with a conventional time-driven method using case studies, through which the main advantages of the proposed method will be identified. [ABSTRACT FROM AUTHOR]

Ground source heat pump systems have been proved to be one of the most efficient systems for heating and cooling in buildings. However, an optimal energy performance depends on a good control of the auxiliaries, which stand for an important part of the total energy consumption. The authors previously developed an experimental in situ optimization methodology for the water circulation pumps frequency of ground source heat pump systems when single stage and multi-stage ON/OFF regulation is employed. However, the user comfort was not completely met under extreme weather conditions during summer. This paper presents the upgrading of this energy optimization strategy combining circulation pumps frequency variation and building supply temperature compensation in order to ensure the user comfort while keeping high energy savings. Experimental results show that the user comfort is met by means of this new methodology and the energy savings (33%) are even higher than those obtained with the previous methodology. [ABSTRACT FROM AUTHOR]

This paper discusses the maintenance plan optimization problem for the energy efficiency purpose in the building energy efficiency retrofitting context. A subproblem namely the Building Retrofitted Facilities Corrective Maintenance Planning (BRFCMP) problem is proposed, where the corrective maintenance for malfunctioning retrofitted items are involved. The aggregate performances of the homogeneous retrofitted item groups, instead of the individual item performances, are the main consideration of the optimization issue. An aggregate population level optimization model is proposed to address the BRFCMP problem. When further taking into account the uncertainties, the optimization problem is cast into an optimal control problem to reduce the consequent adverse impact, given the dynamic nature of the aggregate performances of the item groups during operation. Both the optimization and control system approaches are applied to solve the BRFCMP problem without or considering uncertainties. An actual building retrofitting project is used as the case study to investigate the important role of maintenance to the building energy efficiency. The effectiveness of the proposed approaches is verified by simulation results. [ABSTRACT FROM AUTHOR]

The recast version of the Energy Performance of Buildings Directive (2010/31/EU) proposes a comparative methodology aimed at defining the energy performance of buildings “with a view to achieving cost-optimal levels”. Really, how can the cost-optimal technologies be detected? Moreover, how can the most proper packages of energy efficiency measures be chosen in order to obtain the cost-optimality? This paper would provide answers to these questions, by proposing a new methodology for the evaluation of the cost-optimality, by means of the multi-objective optimization of energy performance of buildings and indoor thermal comfort. The optimization procedure is developed by means of the coupling between MatLab and EnergyPlus, by implementing a genetic algorithm, and it allows the evaluation of profitable and feasible packages of energy efficiency measures applied to buildings. Then, following the adoption of these packages, the global cost over the lifecycle of the building is calculated, in order to identify the cost-optimal solution. After the presentation of the methodology, the developed method is proposed for the optimization of the energy retrofit of an existing building. Furthermore, the method can be applied also for new architectures, by considering reference buildings. [ABSTRACT FROM AUTHOR]

Retrofitting of existing buildings offers significant opportunities for improving occupants’ comfort and well-being, reducing global energy consumption and greenhouse gas emissions. This is being considered as one of the main approaches to achieve sustainability in the built environment at relatively low cost and high uptake rates. Although a wide range of retrofit technologies is readily available, methods to identify the most suitable set of retrofit actions for particular projects are still a major technical and methodological challenge. This paper presents a multi-objective optimization model using genetic algorithm (GA) and artificial neural network (ANN) to quantitatively assess technology choices in a building retrofit project. This model combines the rapidity of evaluation of ANNs with the optimization power of GAs. A school building is used as a case study to demonstrate the practicability of the proposed approach and highlight potential problems that may arise. The study starts with the individual optimization of objective functions focusing on building's characteristics and performance: energy consumption, retrofit cost, and thermal discomfort hours. Then a multi-objective optimization model is developed to study the interaction between these conflicting objectives and assess their trade-offs. [ABSTRACT FROM AUTHOR]