Optimisation of office building facades by means of genetic algorithms

The importance of considering the environmental performance of buildings during conceptual stages of the design process is growing as a consequence of the restrictive requirements of building regulations and energy certification. The fa\ue7ade plays a key role in the design of buildings that need to meet strict requirements of energy efficiency and at the same time provide internal comfort conditions. For the work of this thesis, a simulation-optimisation tool was developed in Matlab environment to automate the coupling of the free energy simulation program EnergyPlus to the optimisation capabilities of the genetic algorithms included in Matlab\u201fs Optimisation Toolbox

Impact of adjustment strategies on building design process in different climates oriented by multiple performance

Adjustment strategies including window ventilation and shading have important improvements in energy consumption, thermal and light environments, furthermore, the upper limit for improvement is affected by design parameters. However, studies incorporating adjustment strategies in the building design process are very limited. To address this research gap, we explore the effects of window ventilation and shading on building design performance from uncertainty analysis, sensitivity analysis, and multi-objective optimization. Furthermore, China's typical climate zones are compared given climate effects. Results indicate that (1) the uncertainty of total energy demand in the severe cold climate is most affected with the uncertainty increase rate being 32.0%, the uncertainty of thermal comfort ratio in the hot summer and cold winter climate and the hot summer and warm winter climate is most affected with the uncertainty increase rate being 16.3% and 14.0%, respectively. (2) the sensitivity analysis of the thermal comfort ratio is more sensitive to adjustment strategies than to total energy demand. The severe cold climate is more vulnerable than in other climates. (3) when multi-objective optimization is performed with maximum thermal comfort and minimum total energy demand when considering adjustment strategies, the severe cold climate has the greatest energy-saving potential (38.1%) and the hot summer and cold winter climate has the largest potential to improve thermal comfort (17.6%). More importantly, the light environment is within the comfort range from the daylight glare index, the illuminance, and illuminance uniformity ratios

Optimisation of piping network design for district cooling system

A district cooling system (DeS) is a.scheme for centralised cooling energy distribution which takes advantage of economies of scale and load diversity. . A cooling medium (chilled water) is generated at a central refrigeration plant and then supplied to a district area, comprising multiple buildings, through a closed-loop piping circuit. Because of the substantial capital investment involved, an optimal design of the distribution piping . configuration is one of the crucial factors for successful implementation of a district 1'. cooling scheme. Since there. exists an enormous number of different combinations of the piping configuration, it is not feasible to evaluate each individual case using an exhaustive approach. This thesis exammes the problem of determining an optimal distribution piping configuration using a genetic algorithm (GA). In order to estimate the spatial and temporal distribution of cooling loads; the climatic conditions of Hong Kong were investigated and a weather database in the form of a typical meteorological year (TMY) was developed. Detailed thermal modelling of a number of prototypical buildings was carried out to determine benchmark cooling loads. A novel Local Search/Looped Local Search algorithm was developed for finding optimal/near-optimal distribution piping configurations. By means of computational . experiments, it was demonstrated that there is a promising improvement to GA performance by including the Local Search/Looped Local Search algorithm, in terms of both solution quality and computational efficiency. The effects on the search performance of a number of parameters were systematically investigated to establish the most effective settings. In order to illustrate the effectiveness of the Local Search/Looped Local Search algorithm, a benchmark problem - the optimal communication,spanning tree (OCST) was used for comparison. The results showed that the Looped Local Search method developed in this work was an effective tool for optimal network design of the distribution piping system in DCS, as well as for optimising the OCST problem.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Optimizing window configuration counterbalancing energy saving and indoor visual comfort for Sydney dwellings

Building penetrations are the most-potent elements providing daylight and moderating the lighting energy consumption and affecting indoor comfort and consequent energy usage. In a semi-tropical climate with a green environment such as Sydney, there is a radical demand to extend windows providing views. This research aims to optimize sunlight admission and maintain indoor comfort while minimizing energy consumption. The method for investigation is to simulate a multiobjective optimization using NSGA-II considering visual and thermal comfort along with energy usage and view of the outside. A combination of human and machine assessments responding to manual and microcontroller-operated indoor validating simulation improves the generalizability. The solutions were assessed for local codes compliance and double-checked against statistical sky conditions. Regarding north, a window-to-wall ratio of 10.7–20% delivers an optimum daylight metric, yielding a 12.16% decrease in energy use intensity. For an east-facing window, altering 26.4% of WWR decreases 2% in lighting energy and a provides a drastic change in visual comfort. Regarding west, changing WWR by about 51% brings about a 50% saving in lighting but no change in other energy loads. Regarding south, when window length is limited to 39% envelope width, it delivers the optimum energy consumption. This study covers visual and thermal comfort together with energy usage and view of the outside, which has not been investigated for southern hemisphere dwellings. A combined simulation and field measurement of human and machine assessment justifies the solutions

Effectiveness of standalone simulation-based optimization software in optimizing the life cycle cost of residential buildings

Designers aim to build nearly zero energy buildings and positive energy buildings to comply with regulations. However, due to many variables affecting the energy performance of buildings, energy-efficient building design is a challenging task. Among the proposed methods, simulation-based systems are promising. The proposed simulation-based systems are not suitable for the construction sector because of the long optimization periods. The primary goal of this study is to emphasize the necessity of standalone software packages in solving usability problems and to provide a tool for designers and architects to incorporate into their daily works. To demonstrate the advantages of standalone software a test study was conducted to find a cost-optimal configuration for a typical residential building. In addition, the obtained cost-optimal design was compared to the energy-optimal design obtained in previous studies and it was seen that the outcomes are in parallel with the results of previous studies. It was observed that the optimum insulation thickness obtained from the case study is significantly higher than the limiting values in the national regulation. The results of the parametric analysis demonstrated that wall type, window area, and window type have the highest influence on thermal performance. The results of the study have confirmed that stand-alone software performs optimizations faster overcomes the shortcomings of simulation-based optimization systems comprising integrated multiple software packages.Publisher's Versio

Multi-Objective Optimization for Cooling and Interior Natural Lighting in Buildings for Sustainable Renovation

In order to achieve the ‘nearly zero-energy’ target and a comfortable indoor environment, an important aspect is related to the correct design of the transparent elements of the building envelope. For improving indoor daylight penetration, architectural solutions such as light shelves are nowadays commercially available. These are defined as horizontal or inclined surfaces, fixed or mobile, placed on the inner and/or the outer side of windows, with surface features such to reflect the sunlight to the interior. Given the fact that these elements can influence different domains (i.e., energy need, daylighting, thermal comfort, etc.), the aim of this paper is to apply a multi-objective optimization method within the design of this kind of technology. The case study is a student house in the University of Athens Campus, subject to a deep energy renovation towards nZEB, under the frame of H2020 European project Pro-GET-onE (G.A No 723747). Starting from the numerical model of the building, developed in EnergyPlus, the multi-objective optimization based on a genetic algorithm is implemented. The variables used are various light shelves configurations by differing materials and geometry, as well as different window types and interior context scenarios. Finally, illuminance studies of the pre- and post-retrofit building are also provided through Revit illuminance rendering

Life-cycle optimization of building performance: a collection of case studies

The building sector is one of the most impacting on the energy demand and on the environment in developed countries, together with industry and transports. The European Union introduced the topic of nearly zero-energy building (nZEB) and promoted a deep renovations in the existing building stock with the aim of reducing the energy consumption and environmental impacts of the building sector. The design of a nZEB, and in general of a low-energy building, involves different aspects like the economic cost, the comfort indoor, the energy consumption, the life cycle environmental impacts, the different points of view of policy makers, investors and inhabitants. Thus, the adoption of a multicriteria approach is often required in the design process to manage some potential conflicting domains. In detail, one of the most suitable approaches is to integrate the preliminary building design (or renovation) phase in a multi-objective optimization problem, allowing to rapidly compare many alternatives and to identify the most adapt interventions

Machine learning modelling for predicting non-domestic buildings energy performance: a model to support deep energy retrofit decision-making

Non-domestic buildings contribute 20% of the UK's annual carbon emissions. A contribution exacerbated by its ageing stock of which only 7% is considered new-build. Consequently, the government has set regulations to decrease the amount of energy take-up by buildings which currently favour deep energy retrofitting analysis for decision-making and demonstrating compliance. Due to the size and complexity of non-domestic buildings, identifying optimal retrofit packages can be very challenging. The need for effective decision-making has led to the wide adoption of artificial intelligence in the retrofit strategy design process. However, the vast retrofit solution space and high time-complexity of energy simulations inhibit artificial intelligence's application. This paper presents an energy performance prediction model for non-domestic buildings supported by machine learning. The aim of the model is to provide a rapid energy performance estimation engine for assisting multi-objective optimisation of non-domestic buildings energy retrofit planning. The study lays out the process of model development from the investigation of requirements and feature extraction to the application on a case study. It employs sensitivity analysis methods to evaluate the effectiveness of the feature set in covering retrofit technologies. The machine learning model which is optimised using advanced evolutionary algorithms provide a robust and reliable tool for building analysts enabling them to meaningfully explore the expanding solution space. The model is evaluated by assessing three thousand retrofit variations of a case study building, achieving a root mean square error of 1.02 kgCO 2∕m 2×year equal to 1.7% of error

Multi-Objective Optimisation Framework for Designing Office Windows::Quality of View, Daylight and Energy Efficiency

This paper presents a new, multi-objective method of analysing and optimising the energy processes associated with window system design in office buildings. The simultaneous consideration of multiple and conflicting design objectives can make the architectural design process more complicated. This study is based on the fundamental recognition that optimising parameters on the building energy loads via window system design can reduce the quality of the view to outside and the received daylight – both qualities highly valued by building occupants. This paper proposes an approach for quantifying Quality of View in office buildings in balance with energy performance and daylighting, thus enabling an optimisation framework for office window design. The study builds on previous research by developing a multi-objective method of assessment of a reference room which is parametrically modelled using actual climate data. A method of Pareto Frontier and a weighting sum is applied for multi-objective optimisation to determine best outcomes that balance design requirements. The Results reveal the maximum possible window to wall ratio for the reference room. The optimisation model indicates that the room geometry should be altered to achieve the lighting and view requirements set out in building performance standards. The research results emphasise the need for window system configuration to be considered in the early design stages. This exploratory approach to a methodology and framework considers both building parameters and the local climate condition. It has the potential to be adopted and further refined by other researchers and designers to support complex, multi-factorial design decision-making