Research On The Building Form Digital Energy-efficient Design For Office Building In Severe Cold Region

The office buildings in severe cold region are great in number and energy consumption, which shows considerable energy saving potentials. As the interface of office space and nature environment, building forms have significant effect on energy consumption and indoor comfort. Shifting from the energy-saving design efficiency, accuracy and multiple performances integration point of view, the existing building form energy-efficient design process has yet to be upgraded. This study aims to propose a digital energy-efficient design workflow, platform and strategy for office building form in severe cold regions, improve the precision and efficiency of office building form energy-efficient design in severe cold regions and strengthen multiple performances integration ability of energy-efficient design.The study followed the technical routes form thinking to workflow, form workflow to platform, from platform to strategy, used the method of document research,case study, sampled surveys, statistical analysis, building information modeling, programming, building performance simulation, artificial neural network modeling and genetic algorithm. The study analyzed the digital trends of building form energy-efficient design from the evolution of design thinking, design workflow digital reconstruction, design platform digital reform and design strategy digital transformation aspects. Besides, the study developed the digital energy-efficient design workflow for office building in severe cold regions, including building and environmental information integration, form and performance mapping relationship development and multi-objectives building form optimization sub-process. Also, the study developed building and environmental information model, neural network model and genetic algorithm model. Through writing the interface program, the study coupled these three models and developed the digital energy-efficient design platform. Based on the energy-saving, thermal comfort and daylighting demands, the study proposed the digital energy-efficient design strategies for office building form in severe cold region including objectives, decision variables and constraints through the simulation experiment. Finally, the study validated the digital energy-efficient design workflow, platform and strategy with case practice.The study explored the evolution process of energy-efficient design thinking, from form modeling and generative design thinking to performance-driven energy-efficient design thinking and analyzed the digital reform trends of building form energy-efficient design workflow, platform and strategy. For the first time, the significance of digital energy-efficient design is systematically explained. Besides, the study proposed the digital energy-efficient design workflow for office building form in severe cold regions. Compared with the existed workflow, the digital energy-efficient design workflow shows three technical advantages, which are performance-driven design decision making, multi-performances simulation integration and multi-discipline information parametric collaboration. The study developed building and environmental information model and realized multi-discipline information parametric collaboration. The validation results showed that this model could analyze environmental information, carry out building information modeling for both standard and non-standard and predict the energy demands, thermal and daylighting performance. The study developed energy consumption, thermal and daylighting performance prediction neural network model. And the validation results showed that the correlation coefficient s between neural networks predicted values and targets is over 0.980 without overfitting and hundreds of prediction hours have been saved. The study developed GANN-BIM digital energy-efficient design platform through coupling genetic algorithm model, building and environmental information model and neural networks. The practice validation showed that the GANN-BIM model could improve the energy-efficiency, thermal and daylighting performance for office building in severe cold region s, generate the non-dominated solutions balancing multiple performance demands. Based on the regional characteristics and performance demands of office building in severe cold regions, the study proposed digital energy-efficient design strategies including design objectives, decision variables and constraints.This study could improve efficiency and decision accuracy of energy-efficient design, strengthen the coupled consideration of composite performance demands and improve building form energy-efficient design effect for office building in severe cold regions. Besides, the digital energy-efficient design platform could analyze environmental information, predict composite performances and carry out multi-objectives building form optimization, which will enhance the information level of building energy-efficient design. Moreover, through integrating building and environmental information of office building in severe cold regions, this study developed the thermal and daylighting performance prediction neural network model according with the building material and construction properties in severe cold regions, which will enhance the regional consideration in building energy-efficient design process.