Research on rural house heating load prediction technology based on GA-BP artificial neural network

The prediction of the actual heating load of the rural houses has an important significance for the optimization of the rural house heating system and the application of new heating systems.Based on the long-term monitoring and correlation analysis of 13 indoor and outdoor parameters of rural houses in the North China,this paper studies the feasibility and reliability of the BP artificial neural network model optimized by the genetic algorithm (GA-BPANN) applied to the heating load prediction of rural houses.The results show that high-precision prediction results can be obtained when the GA-BPANN input variables are the top 6 parameters ranked according to the heating load related intensity(indoor temperature,outdoor temperature,indoor TVOC concentration,indoor and outdoor relative humidity and light intensity),which provides a reference for the reasonable determination of the prediction scheme and the rural house heating load.