Abstract
In order to solve the problem of high energy consumption of public buildings and optimize and improve energy conservation of public buildings, we built a building energy consumption prediction model based on NAR neural network prediction technology improved by BP neural network algorithm, and the energy consumption value is predicted. The large public buildings as the research object, the key factors to determine the effect of building energy consumption and collect the corresponding data processing, as the input parameters of neural network prediction public buildings energy consumption value, according to the actual situation will eventually NAR prediction of neural network and BP network prediction method and the comparative analysis the measured data. The results show that NAR neural network can predict the energy consumption of public buildings more accurately than BP neural network under different building parameters.
Highlights
Energy is an important basis for the progress and development of human society
The energy problem of large public buildings has been concerned by the state
Functional diversity, using varying conditions, brought many difficulties to energy consumption prediction, and neural network technology to build forecasting model provides a possible, in this paper, the main research method is through the field acquisition object building electricity consumption over a period of time, temperature and other data into neural network input layer, the network model for training simulation, prediction and the result was compared with actual value, verify the NAR neural network forecasting method in order to achieve the purpose of accuracy[1]
Summary
Energy is an important basis for the progress and development of human society. China is in the rapid development of urban construction period, the number of large public buildings is increasing. The energy problem of large public buildings has been concerned by the state. Many scholars at home and abroad have applied the relevant knowledge of data mining to the prediction and analysis of building energy consumption and obtained good research results. Functional diversity, using varying conditions, brought many difficulties to energy consumption prediction, and neural network technology to build forecasting model provides a possible, in this paper, the main research method is through the field acquisition object building electricity consumption over a period of time, temperature and other data into neural network input layer, the network model for training simulation, prediction and the result was compared with actual value, verify the NAR neural network forecasting method in order to achieve the purpose of accuracy[1]
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