Building air-conditioning energy consumption prediction method based on BP neural network model

Abstract

The invention discloses a building air-conditioning energy consumption prediction method based on a BP neural network model. The method comprises: analyzing influence factors of building air-conditioning energy consumption; according to influence parameters, collecting historical building air-conditioning energy consumption sample parameters, and preprocessing the parameters; using a BP neural network, according to dimensionality of the sample parameters, establishing a building air-conditioning energy consumption prediction model; using the preprocessed sample parameters as a training sample, training the building air-conditioning energy consumption prediction model; collecting near-term real-time building air-conditioning energy consumption sample parameters to evaluate the building air-conditioning energy consumption prediction model; if errors are in an allowed range, output of the model being a building air-conditioning energy consumption predicted value; if not, training the model again. The building air-conditioning energy consumption prediction method based on a BP neural network model is advantaged in that learning rules are simple, a computer can easily implement, and the method has excellent robustness, memory capability, nonlinear mapping capability, and powerful self-learning capability.

Description

technical field [0001] The invention relates to the technical field of air-conditioning energy consumption prediction, in particular to a method for predicting building air-conditioning energy consumption based on a BP neural network model. Background technique [0002] In current modern buildings, especially public buildings, the energy consumption of air-conditioning systems has always accounted for about 50-60% of building energy consumption, and the energy-saving potential is huge. How to effectively manage the energy consumption of air-conditioning systems has been one of the current research hotspots. Accurate prediction of energy consumption of building air-conditioning systems has important theoretical and practical significance for optimizing the operation mode of heating and air-conditioning systems and realizing the comprehensive energy-saving operation of building air-conditioning systems. Air-conditioning energy consumption prediction is of great significance f...

AI Technical Summary

Problems solved by technology

This method establishes a building air-conditioning load forecasting model based on support vector machine theory, and uses genetic algorithm, ant colony algorithm and particle swarm algorithm to optimize the parameters of support vector machine. This method has the advantages of fast learning speed, global optimality and It has the advantages of strong generalization ability, but at the same time, support vector machines also have the disadvantages of being difficult to implement for large-scale training samples and difficult to solve multi-class problems at the same time.

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