Building load forecasting method and device based on improved IHCMAC neural network

Abstract

The invention discloses a building load forecasting method and device based on an improved IHCMAC (Hyperball Cerebellar Model Articulation Controller) neural network model. The method comprises the steps of: simulating the actual operation of a building to obtain building cold/heat load data and influencing factor data; determining input variables of the model according to the degree of correlation between the influencing factors and the building cold/heat load; clustering the input variables according to a particle swarm-K mean clustering algorithm to obtain values of L clustering centers, i.e., model node values, and defining a Gaussian kernel function for each node; and updating the weights of the nodes via a weight training algorithm to obtain a building load forecasting value of the model. The method has the advantages of fast convergence, high learning precision and strong generalization ability, and can provide a decision basis for energy-saving optimization control of a building system.

Description

technical field [0001] The invention belongs to the field of building energy consumption optimization, in particular to a building load forecasting method and device based on an improved IHCMAC neural network. Background technique [0002] With the rapid development of my country's economy, the demand for energy is increasing day by day, and the construction industry has become one of the three "big energy-consuming households". According to relevant statistics, the energy consumption of this industry accounts for about 20% of the world's energy consumption, and the domestic building energy consumption accounts for about 28% of the total energy consumption of the society. The work of building energy conservation such as equipment has been fully launched. However, due to the complexity of the building structure, the influence of human factors, and the characteristics of thermal delay, the actual energy consumption of the building has energy waste, insufficient energy supply ...

AI Technical Summary

Problems solved by technology

The time series method is mostly used in situations where the trend of change is not obvious, and the prediction effect is not ideal for real-time prediction or data fluctuations; the support vector machine method can achieve the best balance between the prediction accuracy and learning ability of the model, but the selection of the kernel is based on experience. The function will affect the prediction effect; the gray system is mostly used to find the law of the development of things in the huge incomplete statistical data, which is not suitable for the research of building load forecasting

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