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Data completeness test and feature learning method for building load prediction

A technology of load forecasting and feature learning, applied in forecasting, data processing applications, instruments, etc., can solve problems such as limited use, achieve the effects of enhancing robustness, reducing computational complexity, and improving forecasting accuracy

Pending Publication Date: 2022-04-05
TIANJIN UNIV
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Problems solved by technology

Since the data-driven model has certain requirements on the quality of the data, this limits the use of this method in actual engineering to a certain extent.

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  • Data completeness test and feature learning method for building load prediction
  • Data completeness test and feature learning method for building load prediction
  • Data completeness test and feature learning method for building load prediction

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Embodiment Construction

[0051] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0052] Take certain case building A, B as embodiment. Building A is the source domain with complete data. The external disturbance data includes 4 types of 12-dimensional features, as shown in Table 1; the internal disturbance data includes 8 types of 63-dimensional features, as shown in Table 2.

[0053] Table 1 Summary of external disturbance parameters

[0054]

[0055] Table 2 Summary of internal disturbance parameters

[0056]

[0057] Building B is the target domain with incomplete data. The optimal feature set to transfer from building A, the complete feature set for best prediction accuracy, and the features contained in the new dataset in building B are shown in Table 3.

[0058] Table 3 The new data set...

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Abstract

The invention discloses a data completeness test and feature transfer learning method for building load prediction, and the method comprises the following steps: proposing a feature screening method based on diffusion kernel density estimation and maximum correlation minimum redundancy, and determining an optimal feature set under different load prediction models, establishing a data set feature completeness test method through the determination of the correlation between the features of the new feature set and the optimal feature set and the feature distribution similarity; and establishing a load prediction model of feature migration. According to the method, the problem that the building load prediction precision is reduced due to incomplete data features can be solved, the stability of the prediction result can still be kept when the input of the model generates tiny change, and meanwhile, the calculation complexity of the building load prediction model is reduced. The method has the advantages that building load prediction precision is greatly improved, robustness of the load prediction model is enhanced, and calculation time is shortened.

Description

technical field [0001] The invention belongs to the research field of building load forecasting and data mining, and in particular relates to a data integrity test and feature transfer learning method for building load forecasting. Background technique [0002] In the energy consumption of public buildings, the energy consumption of air-conditioning systems accounts for about 40% or even higher, and the air-conditioning systems of buildings have huge energy-saving potential. In order to improve the energy efficiency of building air-conditioning systems, various building energy-saving technologies and energy system optimization methods such as collaborative optimization of renewable energy and building energy storage, building demand-side response, and distributed energy system optimization are emerging in an endless stream. However, the implementation of various control systems and optimal designs mentioned in these studies is based on accurate load forecasting, so it is par...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/08
Inventor 丁研黄宸李沛霖
Owner TIANJIN UNIV
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