Hourly Heating load Prediction of Radiant Floor Heating System Based on the BP Neural Network

摘要

An improved error transfer BP neural network model is use to predict the dynamic heating load in a house or a dwelling unit with the character of hour heating load. Compared with the conventionally physical model, the computation consumption is reduced greatly for the less number of the parameters by improving the error transfer ways. The numerical simulation and experimental measure in a low energy consumption building of Dalian city are performed and the BP neural network model was based entirely on the field survey data. The results show that the simulated results are well agreed with the experimental data and the averaged relative error is less than 5%. Furthermore, this improved model can predict accurately hour heating load during the course of next 24 hours and it is favorable for predicting the short time heating load problems.