Building operation energy consumption prediction method

Abstract

The invention discloses a building operation energy consumption prediction method, which comprises the steps of preprocessing operation energy consumption data of an office building, performing feature analysis, and obtaining a time sequence of the operation energy consumption of the office building in a form of kilogram standard coal in a unified dimension; carrying out phase-space reconstructionon the time sequence of office building operation energy consumption by using a C-C correlation estimation method; judging whether the reconstructed office building operation energy consumption timesequence has chaotic characteristics or not by utilizing the maximum lyapunov index; performing short-term energy consumption prediction on the office building operation energy consumption time sequence with the chaotic characteristics by utilizing a Chaos-SVR neural network; dividing an error interval of a prediction result by using a mean-variance method, and constructing a Markov probability transfer matrix; carrying out Markov chain error correction on the Markov probability transfer matrix, and predicting the operation energy consumption of the office building according to the predictionvalue. According to the method, the office building operation energy consumption prediction precision is remarkably improved, and a decision basis is provided for optimized operation and energy-savingmanagement of the office building.

Description

technical field [0001] The invention belongs to the technical field of building operation energy consumption prediction, and in particular relates to a building operation energy consumption prediction method. Background technique [0002] In the whole life cycle of office buildings, problems such as high energy consumption and low energy efficiency are common, resulting in serious energy waste. Its energy-saving potential is huge, and the consumption reduction rate can reach 30% to 50%. Among them, office buildings account for the largest proportion of energy consumption. Therefore, it is of great significance to study the operating energy consumption of office buildings, and real-time and accurate prediction can provide data decision-making for optimizing operating efficiency, so as to achieve energy-saving goals. [0003] The prediction methods of building energy consumption are mainly divided into two categories: 1. Forward modeling; 2. Data-driven models. Machine lear...

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Problems solved by technology

The above studies are all based on multivariate input. Compared with the univariate time series, the factors to be considered are complex. Some use the cooling load of office buildings at historical moments to map to the same time dimension according to Bayesian theory as the univariate of the machine learning prediction model. Input, use Chaos-SVR and WD-SVR (Wavelet Decomposition-Support Vector Regression) to predict the time series of building cooling load respectively. Good prediction effect, but due to the problem of error accumulation in the combined model in the iterative process, it cannot well meet the needs of actual optimized operation

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