Academic Journal of Computing & Information Science, 2026, 9(7); doi: 10.25236/AJCIS.2026.090703.
Xiangyi Chen1, Yu Chen2, Yusheng Li1, Shunchao Xu1, Tianhua Yang1
1School of Information Engineering, Hangzhou Medical College, Hangzhou, China, 311399
2School of Medical Imaging, Hangzhou Medical College, Hangzhou, China, 311399
The wide application of air source technology has imposed considerable pressure on the power grid system. By accurately predicting indoor temperatures and reducing unnecessary energy consumption, this will help alleviate the burden on the power system and bring about certain social and economic benefits. This article first provides the relevant background information and collects relevant data from two locations. Different identification parameters are set and combined with the thermodynamic differential equation model to solve the parameters for different locations. Secondly, GOOSE-BP and BP neural networks are used to predict different locations respectively, and their effects are also evaluated. Finally, based on the above two results, a constant temperature control strategy is established, and a dynamic programming model is established based on the electricity prices during peak and off-peak periods and constraints to calculate the optimized electricity costs for different locations. This article proposes different strategies for the two different locations to reduce the electricity burden of enterprises. Meanwhile, a feasible neural network model is proposed to optimize power distribution and reduce the burden on the power grid.
Neural Network, Heating Analysis, Thermodynamic Differential Equation Model, Constant Temperature Control Strategy, Dynamic Programming Model
Xiangyi Chen, Yu Chen, Yusheng Li, Shunchao Xu, Tianhua Yang. Heating Analysis and Prediction Based on GOOSE-BP Model Mathematical Modeling and Neural Networks. Academic Journal of Computing & Information Science (2026), Vol. 9, Issue 7: 20-27. https://doi.org/10.25236/AJCIS.2026.090703.
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