Energy saving of buildings for reducing carbon dioxide emissions using novel dendrite net integrated adaptive mean square gradient

Artificial neural networks have been widely applied in construction industries. Due to dendrites of biological neurons participating in the pre-calculation of neural networks, the structure of the traditional artificial neural network needs to be adjusted subjectively. Thus, a novel dendrite net based on the adaptive mean square gradient is proposed in this paper. The energy consumption of buildings is predicted and analyzed by the proposed method to cut the carbon dioxide emissions. The adaptive mean square gradient method is used to update the weight matrix of the dendrite net method, which can avoid errors caused by selecting hidden layer nodes to improve the prediction accuracy of the proposed method. Finally, the proposed method is used to energy saving and emission reducing of the construction industry. Compared with other methods, the experimental results show the availability of the proposed method. Through predicting the heating and cooling loads based on the proposed method, the construction plan is adjusted to decrease the energy consumption of buildings. Moreover, the appliances energy consumption is predicted and analyzed by the proposed method to improve energy efficiency and cut carbon dioxide emissions.

Automated summary

This paper proposes a novel dendrite net based on the adaptive mean square gradient for predicting and reducing building energy consumption and carbon dioxide emissions. The effectiveness of the proposed method is demonstrated through experiments and its application in the construction industry.