Heat supply load prediction energy-saving control method and system based on BP neural network prediction model

Abstract

The invention relates to the technical field of central heating, in particular to a heat supply load prediction energy-saving control method and system based on a BP neural network prediction model. The heat supply load prediction energy-saving control method and system predict a heating load Q3 of a heating building in each heating period by utilizing the BP neural network prediction model to obtain a heating load prediction value; and the opening degree of an intelligent balance valve IBV of the heating building is adjusted according to the heating load prediction value, energy saving is facilitated, and the thermal comfort of heating users is improved. The invention further relates to a heat supply load prediction energy-saving control system based on the BP neural network prediction model, and the heat supply load prediction energy-saving control system applies the method.

Description

technical field [0001] The invention relates to the technical field of central heating, in particular to a BP neural network-based heat load prediction and energy-saving control method and system for central heating. Background technique [0002] At present, all parts of the world are facing huge challenges in terms of energy. In my country, energy issues have become the core issue of social development, and efficient use of energy technologies has gradually attracted the attention of researchers. Survey data show that China's building energy consumption accounts for 20% to 40% of the country's total energy consumption, and it is still growing at a rate of more than 10% every year. Among them, about 40% of the total energy consumption of the building comes from the central heating system, and the existing central heating system cannot meet the needs of users due to the low level of automation and the lack of advanced forecasting technology. Heat, resulting in reduced heatin...

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Problems solved by technology

Among them, about 40% of the total energy consumption of the building comes from the central heating system, and the existing central heating system cannot meet the needs of users due to the low level of automation and the lack of advanced forecasting technology. heat, leading to a decrease in heating user comfort, and also causing a large amount of heat loss

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