Physical-data fusion building analysis method based on extreme learning machine
An extreme learning machine and data fusion technology, applied in the field of power system, can solve the problems of slow demand side response, low building model accuracy, low load prediction accuracy, etc., to reduce complexity and improve accuracy.
Pending Publication Date: 2020-11-06
SHENYANG POLYTECHNIC UNIV +1
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Problems solved by technology
Its purpose is to solve the problems of low accuracy of building models, slow demand-side response, and low accuracy of load forecasting.
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[0022] The specific implementation method of the present invention will be described below in conjunction with the accompanying drawings, so that those skilled in the art can better understand the present invention.
[0023] The present invention aims at the traditional building modeling and analysis process, due to the large demand for samples in the modeling process, environmental data, user behavior and other dynamic factors are not considered, resulting in the complex structure of the built model, poor model accuracy, and load forecasting. To solve the problem of low precision, a physical-data fusion building modeling method based on extreme learning machine is proposed. The invention is suitable for building models, improves the accuracy of the model on the basis of reducing the complexity of the model, and at the same time greatly increases the prediction accuracy of the load.
[0024] Based on the existing technology, the present invention combines multivariate data suc...
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The invention relates to the field of power systems, in particular to a physical-data fusion building modeling analysis method based on an extreme learning machine. The method comprises the followingsteps: firstly, acquiring and preprocessing data: constructing a building physical model based on an overall measurement and recognition method through collected and preprocessed building data and electrical data; training the building physical model, the collected and preprocessed user data, environment data and actual power consumption data by using an extreme learning machine to obtain a physical-data fusion model; and inputting the static parameters of the to-be-analyzed power consumption behaviors and the dynamic parameters into the physical-data fusion model through the physical model toobtain an analysis result. The system comprises a data collection and preprocessing module and a building physical module. Physical-data fusion module. The invention provides a physical-data fusion building modeling analysis method based on an extreme learning machine in order to solve the problems that an existing building model is low in precision, slow in demand side response and low in load prediction precision.
Description
technical field [0001] The invention relates to the field of electric power systems, in particular to an analysis method for physical-data fusion building modeling based on extreme learning machines. Background technique [0002] With the vigorous advancement of the ubiquitous power Internet of Things construction, the arrangement of massive sensing terminals makes the analysis and control of power consumption of digital houses a hot spot in research and application. Existing house energy efficiency analysis is usually based on the distribution network platform area. or an aggregated model of a single building. The dynamic changes of the external environment, uneven heat distribution in the building, the changing state of the internal connection structure, and even the activities of people will cause the parameter deviation of the building power consumption model, which in turn will affect the accurate analysis of the power consumption of the building. [0003] Therefore, b...
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Login to View More IPC IPC(8): G06Q10/06G06Q50/06G06N3/04G06N3/08G06K9/62
CPCG06Q10/067G06Q10/0639G06Q50/06G06N3/08G06N3/045G06F18/211G06F18/25
Inventor 崔嘉胡罗乐杨俊友孙峰周小明陈得丰杨智斌佟昊松苑经纬李桐
Owner SHENYANG POLYTECHNIC UNIV