Research on Measurement Method of Electricity Carbon Emissions during Operation and Maintenance of Primary and Secondary School Buildings based on LSTM Recurrent Neural Network——Case Study of Ruian Binhai Experimental Primary School
This paper uses LSTM recurrent neural network to predict the electricity carbon emissions of a teaching building construction in Ruian Binhai Experimental Primary School.By collecting and analyzing energy consumption data,building characteristics and environmental factors,a carbon emission prediction model is established based on LSTM cycle neural network,and prediction results are compared with the SVR algorithm.The results show that the model has obvious advantages in prediction accuracy and stability,and provides a reliable scientific basis for carbon emission management and emission reduction decisions.This study provides a useful reference for promoting building carbon emission reduction and sustainable development.
carbon emission predictionneural networkprimary and secondary school buildings
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