Jonathan A. Wright

Optimization of building thermal design and control by multi-criterion genetic algorithm

The design of buildings is a multi-criterion optimization problem, there always being a trade-off to be made between capital expenditure, operating cost, and occupant thermal comfort. This paper investigates the application of a multi-objective genetic algorithm (MOGA) search method in the identification of the optimum pay-off characteristic between the energy cost of a building and the the occupant thermal discomfort. Results are presented for the pay-off characteristics between energy cost and zone thermal comfort, for three design days and three building weights. Inspection of the solutions indicates that the MOGA is able to find the optimum pay-off characteristic between the daily energy cost and zone thermal comfort. It can be concluded that multi-criterion genetic algorithm search methods offer great potential for the identification of the pay-off between the elements of building thermal design, and as such can help inform the building design process.

Self-adaptive fitness formulation for constrained optimization

A self-adaptive fitness formulation is presented for solving constrained optimization problems. In this method, the dimensionality of the problem is reduced by representing the constraint violations by a single infeasibility measure. The infeasibility measure is used to form a two-stage penalty that is applied to the infeasible solutions. The performance of the method has been examined by its application to a set of eleven test cases from the specialized literature. The results have been compared with previously published results from the literature. It is shown that the method is able to find the optimum solutions. The proposed method requires no parameter tuning and can be used as a fitness evaluator with any evolutionary algorithm. The approach is also robust in its handling of both linear and nonlinear equality and inequality constraint functions. Furthermore, the method does not require an initial feasible solution.

A VENTILATED SLAB THERMAL STORAGE SYSTEM MODEL

Abstract A simplified dynamic thermal model of a hollow core concrete slab thermal storage system and associated room is described. The model is based on a thermal network that can address the heat exchange between the slab cores and the ventilation air, the thermal storage in the building fabric, and the effect of the heat disturbances on the room. The increase in convective heat transfer at the corners of the ventilation cores is also discussed. For normal cyclic operation, the simulated mass and zone temperatures are both in phase with measured performance data. The model root mean square error between the simulated and measured performance is no more than 0.5 °C for the average slab mass temperature and 1.0 °C for the zone air temperature.

Demonstration of Fault Detection and Diagnosis Methods for Air-Handling Units

Results are presented from controlled field tests of two methods for detecting and diagnosing faults in HVAC equipment. The tests were conducted in a unique research building that featured two air-handling units serving matched sets of unoccupied rooms with adjustable internal loads. Tests were also conducted in the same building on a third air handler serving areas used for instruction and by building staff. One of the two fault detection and diagnosis (FDD) methods used first-principles-based models of system components. The data used by this approach were obtained from sensors typically installed for control purposes. The second method was based on semiempirical correlations of submetered electrical power with flow rates or process control signals. Faults were introduced into the air-mixing, filter-coil, and fan sections of each of the three air-handling units. In the matched air-handling units, faults were implemented over three blind test periods (summer, winter, and spring operating conditions). In each test period, the precise timing of the implementation of the fault conditions was unknown to the researchers. The faults were, however, selected from an agreed set of conditions and magnitudes, established for each season. This was necessary to ensure that at least some magnitudes of the faults could be detected by the FDD methods during the limited test period. Six faults were used for a single summer test period involving the third air-handling unit. These fault conditions were completely unknown to the researchers and the test period was truly blind. The two FDD methods were evaluated on the basis of their sensitivity, robustness, the number of sensors required, and ease of implementation. Both methods detected nearly all of the faults in the two matched air-handling units but fewer of the unknown faults in the third air-handling unit. Fault diagnosis was more difficult than detection. The first-principles-based method misdiagnosed several faults. The electrical power correlation method demonstrated greater success in diagnosis, although the limited number of faults addressed in the tests contributed to this success. The first-principles-based models require a larger number of sensors than the electrical power correlation models, although the latter method requires power meters that are not typically installed. The first-principles-based models require training data for each subsystem model to tune the respective parameters so that the model predictions more precisely represent the target system. This is obtained by an open-loop test procedure. The electrical power correlation method uses polynomial models generated from data collected from “normal” system operation, under closed-loop control. Both methods were found to require further work in three principal areas: to reduce the number of parameters to be identified; to assess the impact of less expensive or fewer sensors; and to further automate their implementation. The first-principles-based models also require further work to improve the robustness of predictions.

Constrained, mixed-integer and multi-objective optimisation of building designs by NSGA-II with fitness approximation

Graphical abstractDisplay Omitted HighlightsA surrogate based on radial basis function networks is adapted for mixed-type variables, multiple objectives and constraints and integrated into NSGA-II.A deterministic method to include infeasible solutions in the population is proposed.Variants of NSGA-II including these changes are applied to a typical building optimisation problem, with improvements in solution quality and convergence speed.Analysis of the constraint handling and fitness landscape of the problem is also conducted. Reducing building energy demand is a crucial part of the global response to climate change, and evolutionary algorithms (EAs) coupled to building performance simulation (BPS) are an increasingly popular tool for this task. Further uptake of EAs in this industry is hindered by BPS being computationally intensive: optimisation runs taking days or longer are impractical in a time-competitive environment. Surrogate fitness models are a possible solution to this problem, but few approaches have been demonstrated for multi-objective, constrained or discrete problems, typical of the optimisation problems in building design. This paper presents a modified version of a surrogate based on radial basis function networks, combined with a deterministic scheme to deal with approximation error in the constraints by allowing some infeasible solutions in the population. Different combinations of these are integrated with Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and applied to three instances of a typical building optimisation problem. The comparisons show that the surrogate and constraint handling combined offer improved run-time and final solution quality. The paper concludes with detailed investigations of the constraint handling and fitness landscape to explain differences in performance.

Adaptive diurnal prediction of ambient dry-bulb temperature and solar radiation

This paper presents a new adaptive weather-prediction model that can be used for on-line control of HVAC and thermal storage systems. The model can predict external dry-bulb temperature and solar radiation over the next 24 h. Because a building with a fabric thermal storage system has a slow response to thermal loads, a predictive controller is essential to operate the building and associated plant installation to respond effectively to external climatic conditions ahead of time. Three prediction methods are investigated in the paper: a pure stochastic method, a combined deterministic-stochastic method, and an expanded method for short-term temperature forecast. It has been found that the combined deterministic-stochastic method is simpler and gives the smallest prediction errors. For the prediction of solar radiation, a deterministic model is proposed. The proposed prediction algorithms for temperature and radiation are simple and efficient to conduct on a supervisory PC to predict hourly temperature and radiation profiles over the next 24 h. Updating temperature forecasts using observations available with time is also investigated in this paper.

HVAC optimisation studies: Sizing by genetic algorithm

Previous research into the optimum sizing of hvac systems has focused on the use of direct search optimisation methods. Although these methods can find a solution, it is difficult for them to move discrete variables along nonlinear constraint boundaries and they often fail as a result. This paper describes the performance of a simple genetic algorithm search method when applied to such a problem. The formulation of the problem is described together with the operation of the algorithm. It is concluded that the algorithm exhibits rapid initial progress but that final convergence is slow due to the highly constrained nature of the optimisation problem. It is suggested that a more effective use of the constraint functions could improve the convergence and robustness of the algorithm. The performance of the algorithm is also sensitive to the problem formulation.

Multi-objective optimization of cellular fenestration by an evolutionary algorithm

This paper describes the multi-objective optimized design of fenestration that is based on the façade of the building being divided into a number of small regularly spaced cells. The minimization of energy use and capital cost by a multi-objective genetic algorithm was investigated for: two alternative problem encodings (bit-string and integer); the application of constraint functions to control the aspect ratio of the windows; and the seeding of the search with feasible design solutions. It is concluded that the optimization approach is able to find near locally Pareto optimal solutions that have innovative architectural forms. Confidence in the optimality of the solutions was gained through repeated trail optimizations and a local search and sensitivity analysis. It was also concluded that seeding the optimization with feasible solutions was important in obtaining the optimum solutions when the window aspect ratio was constrained.

Evolutionary Synthesis of HVAC System Configurations: Algorithm Development (RP-1049)

This paper describes the development of a model-based optimization procedure for the synthesis of novel heating, ventilating, and air-conditioning system configurations. The optimization problem can be considered as having three suboptimization problems: the choice of a component set; the design of the topological connections between the components; and the design of a system operating strategy. In an attempt to limit the computational effort required to obtain a design solution, the approach adopted in this research is to solve all three subproblems simultaneously. The computational effort has been further limited by implementing simplified component models and including the system performance evaluation as part of the optimization problem (there being no need, in this respect, to simulate the system performance). The optimization problem has been solved using a Genetic Algorithm (GA) that has data structures and search operators specifically developed for the solution of HVAC system optimization problems. The performance of the algorithm and various search operators has been examined for a two-zone optimization problem, the objective of the optimization being to find a system design that minimizes system energy use. In particular, the performance of the algorithm in finding feasible system designs has been examined. It was concluded that the search was unreliable when the component set was optimized, but if the component set was fixed as a boundary condition on the search, then the algorithm had an 81% probability of finding a feasible system design. The optimality of the solutions is not examined in this paper but is described in an associated publication (Wright and Zhang 2008). It was concluded that, given a candidate set of system components, the algorithm described here provides an effective tool for exploring the design of novel HVAC systems.

Automatic design synthesis and optimization of component-based systems by evolutionary algorithms

A novel approach for automatic design synthesis and optimization using evolutionary algorithms (EA) is introduced in the paper. The approach applies to component-based systems in general and is demonstrated with a heating, ventilating and air-conditioning (HVAC) systems problem. The whole process of the system design, including the initial stages that usually entail significant human involvement, is treated as a constraint satisfaction problem. The formulation of the optimization process realizes the complex nature of the design problem using different types of variables (real and integer) that represent both the physical and the topological properties of the system; the objective is to design a feasible and efficient system. New evolutionary operators tailored to the component-based, spatially distributed system design problem have been developed. The process of design has been fully automated. Interactive supervision of the optimization process by a human-designer is possible using a specialized GUI. An example of automatic design of HVAC system for two-zone buildings is presented.