Rural dwellings in cold regions features for high energy consumption and low comfort.The relevant researches are mainly concentrated in northeast China and eastern Inner Mongolia,while the researches on grassland dwellings in western Inner Mongolia are relatively few.In view of this situation,this paper proposes an optimization and retrofit framework for the climate characteristics and residential problems in western Inner Mongolia.The framework takes Energy Use Intensity(EUI),Predicted Percentage of Dissatisfied(PPD),and Life Cycle Cost(LCC)after retrofitting as its performance objectives.Deep Neural Network(DNN)and Non-dominated Sorting Genetic Algorithm(NSGA-Ⅱ)were used to optimize the residential building envelope.This paper selects a grassland residence in Ordos for case study,uses Grasshopper platform for performance simulation and data set acquisition,uses Python language to train the deep neural network model,and finally obtains a prediction model with relatively ideal R2 evaluation index.Then,genetic algorithm is used to optimize the model to obtain multiple Pareto frontier solutions.Kmeans clustering algorithm is used to analyze the Pareto frontier solution and select the ideal solution.The results show that LCC,EUI and PPD are reduced by 35%,33%and 17.75%respectively.
reconstruction frameperformance optimizationfolk house
NETL
NSTL
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