Heat Supply Optimization Based On Machine Learning And Knowledge Graph

Abstract

At present, heating companies mostly use a set-point temperature control curve. The operator adjusts the temperature control curve according to watching the outdoor weather conditions, which usually results in economic conditions. Given the above condition, we analyze the heating system’s mechanism first, then compile and analyze much historical data. According to the analysis results, we establish the heat exchange station-user room temperature joint model based on the machine learning method. Using the above model, the system is deeply explored, and the optimized secondary heating curve is designed and simulated. The results show that the user’s room temperature stability has an obvious control effect, which is of great significance to meet the room temperature compliance rate. At the same time, the knowledge graph of the heating field and some applications based on the knowledge graph are also built to provide more convenient and friendly operating advice for operators.