Predictive analysis of indoor temperature and humidity based on BP neural network single-step prediction method

Abstract

Among the various environmental indicators in the home interior, the temperature and humidity values have the most obvious impact on people's quality of life. In order to increase the degree of intelligence in home life, this paper designs an indoor temperature and humidity prediction model based on BP neural network. Compared with the multi-step prediction method and the rolling prediction method, the single-step prediction method has a higher prediction accuracy rate in the short-term prediction range, so the temperature and humidity prediction model uses single-step prediction to realize the prediction of the indoor temperature and humidity value at a certain time in the future. In order to verify the prediction effect of the model, the temperature and humidity values of a household for the first 365 days of 2016 are used as training data to determine the prediction model, and the temperature and humidity values of the last day are used as the verification values of the predicted values, simulation analysis through MATLAB shows that the indoor temperature and humidity prediction model has a good prediction effect.