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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]

Abstract: Most air handling units (AHUs) in commercial buildings have an air economizer cycle for free cooling under certain outside air conditions. During the economizer cycle, the outside air and return air dampers are modulated to seek supply air temperature at its setpoint. The supply air temperature is typically set at 55F (13°C) to control humidity in the space. However, dehumidification is not necessary when the outside air is dry. Meanwhile, the space may have less cooling load due to envelope heat loss and/or occupant schedule changes. These facts provide an opportunity to use higher supply air temperature to reduce or eliminate mechanical cooling and terminal box reheat. On the contrary, a higher supply air temperature requires increased air flow as well as fan power. Therefore, an optimization question is formed, through which an optimal supply air temperature is identified to minimize total energy consumption. In this paper a steady-state energy consumption model is established for AHU systems under the economizer, and then an analytical optimization method is used to seek an optimal supply air temperature setpoint to minimize the energy cost. This paper presents AHU system energy modeling, supply air temperature optimization, simulated energy savings, and control sequence development. [Copyright &y& Elsevier]

Abstract: This paper presents an investigation of how Model Predictive Control (MPC) and weather predictions can increase the energy efficiency in Integrated Room Automation (IRA) while respecting occupant comfort. IRA deals with the simultaneous control of heating, ventilation and air conditioning (HVAC) as well as blind positioning and electric lighting of a building zone such that the room temperature as well as CO2 and luminance levels stay within given comfort ranges. MPC is an advanced control technique which, when applied to buildings, employs a model of the building dynamics and solves an optimization problem to determine the optimal control inputs. In this paper it is reported on the development and analysis of a Stochastic Model Predictive Control (SMPC) strategy for building climate control that takes into account the uncertainty due to the use of weather predictions. As first step the potential of MPC was assessed by means of a large-scale factorial simulation study that considered different types of buildings and HVAC systems at four representative European sites. Then for selected representative cases the control performance of SMPC, the impact of the accuracy of weather predictions, as well as the tunability of SMPC were investigated. The findings suggest that SMPC outperforms current control practice. [Copyright &y& Elsevier]

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]

This paper develops a new process for identifying the lowest cost package of energy efficiency measures (EEM) and hybrid energy system configuration for off-grid homes. Hybrid energy systems, which combine two or more types of energy technologies, often require significant capital expenditure, however, the cost can be reduced by applying EEM to the house to decrease energy demand. The method proposed here, termed Combined Optimization Process (COP), was tested on an off-grid hypothetical case and incorporates an iterative assessment of a building energy and efficiency optimization tool (BEopt) and a hybrid system optimization tool (HOMER). The COP results were compared with the base case where no efficiency measures were applied, and also with a standard process, which involved a selection of best-practice efficiency measures. The COP method yielded net present cost savings of 10% less than the base case, and 5% less than the standard process. The COP method developed in this paper is applicable for existing houses converting to off-grid status as well as for the design stage of off-grid houses. [ABSTRACT FROM AUTHOR]

Temperature and humidity independent control (THIC) systems demonstrate promising energy-saving potential compared with the conventional air conditioning systems. One type of THIC systems is proposed to use dual cooling sources with different temperatures since they could be produced easily by traditional vapor compression refrigeration chillers. This paper investigates the applicable condition of the THIC system based on dual cooling sources (DCSTHIC system). It is found that the DCSTHIC system has limited conditions (such as scopes of the heat moisture ratio and the fresh air rate) when applied, otherwise the indoor air temperature and humidity can’t be fulfilled, and further available scope of the heat moisture ratio realized by the DCSTHIC system is disclosed for air conditioned spaces. The minimum realized heat moisture ratio depends on the temperature of low-temperature cooling sources ( t LCS ). A parametric study is also conducted to analyze effects on the energy efficiency of DCSTHIC systems. These parameters include the heat moisture ratio ( ε ) and temperatures of dual cooling sources. Results show that DCSTHIC systems show more significant energy-saving potential in the air conditioned spaces with higher ε . The system COP improvement ranges from 3.1% to 17.5% compared with conventional air conditioning systems when the heat moisture ratio changes from 8000 kJ/kg to 15,000 kJ/kg. This study helps to guide the design and optimization of DCSTHIC systems. [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]

A multi-objective fire safety and sustainability screening tool for specifying insulation materials has been developed. This paper discusses a methodology for balancing competing requirements by evaluating the thermal resistance, fire performance, sustainability, cost, acoustic damping, and durability objectives of various insulating materials through implementation of a weighted mean. Each variable is normalized and then weighted according to the emphasis placed on each objective, using experimental data for the relevant material property. Two control scenarios and four weighting scenarios are presented. The first control scenario excludes both the fire performance and sustainability objectives in the material evaluations. The second control scenario introduces sustainability as an objective, but still excludes fire performance. The four weighting scenarios each emphasize a different area of consideration: cost, thermal resistance, sustainability, or fire performance. Materials considered are cellulose, fiberglass, rockwool, polyurethane, and polystyrene. Results of this analysis rank the materials in order of desirability and provide a method to reorder this ranking based on the priority assigned to each objective. For the four weighting scenarios presented, rockwool was consistently ranked as the best performer, while extruded polystyrene was typically the weakest. However, in the first control scenario, closed-cell polyurethane performed best and cellulose performed worst. [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]

In order to optimize the global energy performance of a ground source heat pump system, special attention needs to be paid to the auxiliaries as they stand for a considerable part of the total energy consumption. A new in situ experimental methodology based on the frequency variation of the water circulation pumps in order to optimize the energy performance of the system was previously published by the authors for a ground source heat pump system using a single stage heat pump with ON/OFF regulation. The original single stage heat pump was recently replaced with a multistage unit consisting of two compressors of the same capacity working in tandem. A new experimental campaign was carried out and a new study was performed in order to adapt the in situ optimization methodology to the performance of the tandem compressors unit, and, by extension, to the multistage case. This paper presents the in situ optimization methodology for the water circulation pumps frequency adapted for multistage ground source heat pump systems. Results show that energy savings up to 32% can be obtained by applying this optimization methodology. [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]

Designing a low-energy building requires the evaluation of a large number of parameter combinations. Currently, there are several software programs available that are able to calculate the energy performance and life-cycle cost. However, these programs provide little information regarding what actions will improve the energy performance and how much will they cost. An iterative software program that optimises the parameters would be a solution: however, the larger the database, the slower the data processing and optimisation. The SEDICAE project applies a new methodology based on a tabu search and a simplified method to calculate the demand. A tabu search is a good method to avoid local minima and to permit an evaluation of different solutions. The methodology is designed to estimate the annual energy demand, life-cycle cost and the energy rating. In this paper, 48 different scenarios were analysed with SEDICAE software. [ABSTRACT FROM AUTHOR]

Abstract: Improving the heating performance of district heating networks (DHNs), one of the most important components in district heating systems, has significant influence on the promotion of energy efficiency. Based on the entransy theory, this paper develops an entransy dissipation-based method for global optimization of DHNs. We analyze the irreversibility of three types of heat transfer processes in DHNs: (1) the heat transfer process between the waters in primary heating networks and DHNs in substation heat exchangers; (2) the heat transfer process between supply water and indoor airs in radiators; and (3) the return water mixing process, then derive the expressions of entransy dissipation for each process, and finally deduce two different formulas of the total entransy dissipation in DHNs by the users’ demands and the structural and operating parameters of DHNs, respectively, from which we establish the mathematical relation between the users’ demands and the structural and operating parameters of DHNs. Based on this relation, two typical optimization problems of DHNs are converted into conditional extremum problems to deduce two optimization equation groups. Solving the equation groups gives the optimal structural and operating parameters for DHNs with highest energy efficiency. Finally, the method is proved valid and advantageous through the optimization for both 1-column-2-user and 2-column-4-user DHNs. [Copyright &y& Elsevier]

Abstract: Electric lighting consumes 19% of total electricity production worldwide. Open-plan offices constitute the majority of office stock, in which most office workers perform their daily tasks, and hence the lighting in those offices has significant impact on, not only energy usage, but also occupants’ productivity. This paper presents a unique lighting optimization approach for open-plan offices that is capable of tuning task lighting to each occupant''s preference in the most energy-efficient fashion. Exploiting individual controllability of the advanced networked lighting systems, each luminaire can be dynamically actuated at a different level to realize various lighting configurations without any physical rewiring. This research formulates lighting actuation into a linear programming problem and finds the optimal light outputs for each of the luminaires that collaboratively deliver specified lighting to each workstation while using the least amount of energy. Simulations using our lighting optimization approach have shown its versatility in providing proper workstation-specific task lighting under different requirements compared to business-as-usual practices. Energy savings are generated from tuning the light levels to occupants’ needs as well as keeping unoccupied areas unlit or minimally lit with this approach. [Copyright &y& Elsevier]