Research on energy consumption prediction and management strategy of intelligent buildings based on artificial intelligence

In recent years, with the rapid development of society, the energy consumption of buildings in our country is also growing. Excessive energy consumption will lead to environmental problems such as the increase of greenhouse gas emissions, so it is necessary to promote building energy conservation and emission reduction to reduce the problem of excessive energy consumption. The key to building energy conservation and emission reduction is to predict the future trend of building energy consumption and achieve accurate prediction of building energy consumption, which can assist in guiding building energy conservation planning and energy use strategies. However, the existing building energy consumption forecasting methods are difficult to model the highly complex and nonlinear building energy consumption data, and it is difficult to capture the time-dependent relationship of energy consumption data. For the non-stationary and mutational multi-variable building energy consumption series, how to consider the relationship between each variable on energy consumption and capture the abrupt trend of energy consumption data is still a major challenge in building energy consumption prediction. Therefore, this paper will use neural network to study the above problems. In view of the characteristics that building energy consumption is affected by various factors and building energy consumption data is highly complex, this paper proposes a building energy consumption prediction model based on neural network, and uses the antagonistic mechanism of neural network to learn the hidden layer relationship between building energy consumption and its influencing factors. By designing suitable objective function and network structure for the neural network, the neural network is applied to the prediction of building energy consumption, and the experimental results show that the prediction performance is good.