Public activities are mostly carried out in large public buildings, which are closely related to social management. At present, people's demand for public building facilities is increasing, its shape evolution is becoming more complex, and the scienti c and technological content of construction related technology is also increasing. The development trend of green public buildings is more and more strong. The traditional building design can not effectively deal with the energy consumption of public buildings and people's demand for their performance. This paper introduces BIM and machine learning technology to study their practical application in the design of green public buildings, and tests the perfect machine learning algorithm. According to the experimental test results, the building energy consumption decreased by 14.3%, the carbon emission decreased by 11.39%, and the absolute value of PMV thermal comfort decreased by 34.7%, which obviously achieved the optimization effect. BIM Technology parametric design can enable the design model formed by conceptual design research to automatically draw construction drawings, detailed drawings and other drawings according to the drawing requirements and standards, thus saving the designer's time and enabling him to transfer the drawing time to the program design. Finally, through experiments, the economy, rationality and operability of using BIM Technology to design green public buildings are con rmed. In this paper, machine learning and BIM Technology are introduced, so as to carry out design research for green public buildings design.
With the development of urbanization, the city has entered the stage of improving the quality of the stock, and the number of urban demolition has increased, resulting in the generation of construction waste and great pressure on the environment. The discharge of construction waste threatens the sustainable development of urban environment. And because large public buildings have high energy consumption and high emissions, it is urgent to establish an effective energy consumption measurement system. In the past, the calculation method for energy consumption was relatively single, which could not effectively manage energy consumption. This paper discusses how to use construction waste scientifically based on the Internet of things, and using advanced measurement technology to monitor and analyze building energy consumption is an effective way to solve this problem. Based on this, this paper constructs a building energy consumption measurement system through the Internet of things technology. By using BP linear neural network model to predict, the system effectively improves the prediction accuracy and makes it close to the actual value. The system is mainly based on the collection terminal, the centralized data terminal and the data management terminal, and then builds a communication bridge between the terminals through the Internet of things and the Internet. The response time of energy consumption monitoring and management is relatively short, and the response speed of power control management and energy consumption management analysis is relatively fast, which can effectively measure the energy consumption of buildings. In addition, this paper also makes a brief analysis of the recycling of construction waste, and puts forward the corresponding strategic analysis of resource utilization. This paper designs an effective energy consumption measurement system by introducing the Internet of things technology into the building field.
With the development of urbanization, the city has entered the stage of improving the quality of the stock, and the number of urban demolition has increased, resulting in the generation of construction waste and great pressure on the environment. The discharge of construction waste threatens the sustainable development of urban environment. And because large public buildings have high energy consumption and high emissions, it is urgent to establish an effective energy consumption measurement system. In the past, the calculation method for energy consumption was relatively single, which could not effectively manage energy consumption. This paper discusses how to use construction waste scientifically based on the Internet of things, and using advanced measurement technology to monitor and analyze building energy consumption is an effective way to solve this problem. Based on this, this paper constructs a building energy consumption measurement system through the Internet of things technology. By using BP linear neural network model to predict, the system effectively improves the prediction accuracy and makes it close to the actual value. The system is mainly based on the collection terminal, the centralized data terminal and the data management terminal, and then builds a communication bridge between the terminals through the Internet of things and the Internet. The response time of energy consumption monitoring and management is relatively short, and the response speed of power control management and energy consumption management analysis is relatively fast, which can effectively measure the energy consumption of buildings. In addition, this paper also makes a brief analysis of the recycling of construction waste, and puts forward the corresponding strategic analysis of resource utilization. This paper designs an effective energy consumption measurement system by introducing the Internet of things technology into the building field.
Public activities are mostly carried out in large public buildings, which are closely related to social management. At present, people's demand for public building facilities is increasing, its shape evolution is becoming more complex, and the scientific and technological content of construction related technology is also increasing. The development trend of green public buildings is more and more strong. The traditional building design can not effectively deal with the energy consumption of public buildings and people's demand for their performance. This paper introduces BIM and machine learning technology to study their practical application in the design of green public buildings, and tests the perfect machine learning algorithm. According to the experimental test results, the building energy consumption decreased by 14.3%, the carbon emission decreased by 11.39%, and the absolute value of PMV thermal comfort decreased by 34.7%, which obviously achieved the optimization effect. BIM Technology parametric design can enable the design model formed by conceptual design research to automatically draw construction drawings, detailed drawings and other drawings according to the drawing requirements and standards, thus saving the designer's time and enabling him to transfer the drawing time to the program design. Finally, through experiments, the economy, rationality and operability of using BIM Technology to design green public buildings are confirmed. In this paper, machine learning and BIM Technology are introduced, so as to carry out design research for green public buildings design.
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