Commercial building energy consumption prediction optimization method and system

Abstract

The invention discloses a commercial building energy consumption prediction optimization method and system, wherein the method comprises the steps: carrying out pre-screening of the influence factors of the commercial building energy consumption according to the importance degree, and obtaining the main influence indexes of the commercial building energy consumption; constructing an energy consumption sample set and dividing the energy consumption sample set into a training set and a test set; constructing a commercial building energy consumption random forest model, and determining parameters influencing the prediction precision of the random forest model; adopting a parallel ant colony algorithm, optimizing the parameters influencing the random forest model by using the data of the training set, obtaining the optimized parameters influencing the prediction precision of the random forest model, and thus obtaining the optimized commercial building random forest model; and substituting the data of the test set into the optimized commercial building random forest model, and carrying out energy consumption prediction to obtain a commercial building energy consumption prediction result. According to the method and system, the ant colony algorithm is improved by introducing the idea of parallel sorting, the problem of local convergence in the random forest multi-parameter optimization process is solved, and the convergence speed and the quality of the optimal solution are improved.

Description

technical field [0001] The invention belongs to the technical field of building energy consumption prediction, in particular to a method and system for predicting and optimizing the energy consumption of commercial buildings. Background technique [0002] In recent years, with the rapid development of urbanization, the energy consumption of commercial buildings has increased significantly, and the energy-saving research of commercial buildings has become an important direction of energy-saving research; the prediction and optimization of energy consumption of commercial buildings is an important part of its energy-saving research. Optimizing the operating efficiency of the power distribution system provides effective data for decision-making; therefore, it is of great practical significance to study the mechanism and law of building energy consumption and establish an accurate and effective prediction model. [0003] At present, most researchers use data-driven methods to es...

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Problems solved by technology

[0007] Aiming at the technical problems existing in the prior art, the present invention provides a commercial building energy consumption prediction optimization method and system to solve the problem that the existing random forest algorithm is sensitive to parameter selection, and the optimization process tends to have slow convergence speed and localized Optimum, leading to slow building energy consumption prediction and low prediction accuracy technical problems

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