Parallel cold load prediction method based on building space unit

A technology of building space and forecasting method, which is applied in forecasting, neural learning methods, data processing applications, etc., can solve the problem that the cooling load forecasting model cannot extract the characteristics of building cooling load well, and achieve the goal of improving accuracy and forecasting accuracy Effect

Active Publication Date: 2020-07-10
XI'AN UNIVERSITY OF ARCHITECTURE AND TECHNOLOGY
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Problems solved by technology

[0004] The technical problem to be solved by the present invention is to provide a parallel cooling load prediction method based on building space units in order to solve the problem of...

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  • Parallel cold load prediction method based on building space unit
  • Parallel cold load prediction method based on building space unit
  • Parallel cold load prediction method based on building space unit

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Embodiment Construction

[0055] The present invention provides a parallel cooling load prediction method based on building space units. According to the target building plan and building space layout, the building space unit division is completed, and the DCN topology structure of the building space unit is designed according to the division basis, and the DCN installation is completed. An improved adaptive learning rate deep belief network-partial least squares (ADBN-PLSR) cooling load forecasting model is established. Each DCN downloads the prediction model, and the user can initiate a prediction command through any DCN. The DCN executes the command task and transmits the command to the entire DCN network based on the spanning tree and topology structure. Each DCN independently completes the prediction of the controlled area in parallel. And command, get the cooling load forecast result of the whole building from the initiating node. The independent prediction method of the spatial unit highly extra...

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Abstract

The invention discloses a parallel cold load prediction method based on a building space unit, and the method comprises the steps: completing the division of the building space unit according to a target building plane graph and a building space layout; designing a DCN topological structure of a distributed controller of the building space unit according to the division basis, and completing the installation of a DCN; establishing an improved adaptive learning rate deep belief network-partial least square method ADBN-PLSR cold load prediction model; enabling each DCN to download the predictionmodel; enabling a user to initiate a prediction instruction through any DCN, and enabling the DCN to execute an instruction task and transmit the instruction to the whole DCN network based on a spanning tree and a topological structure; enabling the DCNs to independently complete prediction of a controlled area in parallel, and finally acquiring a cold load prediction result of the whole buildingat an initiating node through a prediction result summation instruction. According to the space unit independent prediction method, the fluctuation characteristics of the building cold load are highly extracted, and the problems of low prediction precision and the like caused by the characteristics of high dimension, nonlinear dynamics and the like of cold load data are solved.

Description

technical field [0001] The invention belongs to the technical field of building cooling load forecasting, and in particular relates to a parallel cooling load forecasting method based on building space units. Background technique [0002] Energy problems are becoming more and more serious, and energy conservation and emission reduction work has become the focus of various countries. Air conditioners are one of the most common energy-consuming equipment in buildings, accounting for about 60% of total power and energy consumption. Aiming at the problem of energy saving and consumption reduction of air conditioners, ice storage energy storage technology has been popularized and used. The ice-storage air-conditioning system cools and stores ice at night when the power grid is low. The next day, according to the time-of-use electricity price and cooling load demand, the cooling capacity distribution of the chiller and ice tank is reasonably planned. [0003] Therefore, accurate ...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/08G06N3/08
CPCG06Q10/04G06Q50/08G06N3/08
Inventor 赵安军任延欢于军琪冉彤张万虎周昕玮席江涛董芳楠
Owner XI'AN UNIVERSITY OF ARCHITECTURE AND TECHNOLOGY
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