CN110046751A - Multi-energy system dispatching method based on the prediction of radial base energy consumption and real-time energy efficiency - Google Patents
Multi-energy system dispatching method based on the prediction of radial base energy consumption and real-time energy efficiency Download PDFInfo
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Abstract
A kind of multi-energy system dispatching method based on the prediction of radial base energy consumption and real-time energy efficiency, is related to technical field of energy management, the solution is to reduce operating cost.This method, which is first itemized, acquires building energy consumption data, environment temperature, ambient humidity, building flow of the people, constructs an energy consumption data library using the data of acquisition, and fill up to the data lacked in database；Building energy consumption data are converted to by index without dimension assessment value using method for normalizing again, so that each index value in building energy consumption data is all in the same number of levels；Then energy consumption prediction model is constructed using neural network radial basis function again；The building prediction of energy consumption that target day is predicted using energy consumption prediction model implements regulation to the multi-energy system of building further according to prediction of energy consumption and the energy efficiency coefficient of each powering device.Method provided by the invention, the energy adjustment suitable for building multi-energy system.
Description
Technical field
The present invention relates to energy management technology, more particularly to a kind of more based on the prediction of radial base energy consumption and real-time energy efficiency
The technology of energy resource system dispatching method.
Background technique
Traditional garden energy is mainly made of the energy resource system of centralization energy supply, and the energy resource system of centralization energy supply is all adopted
With the equipment centralized production of large capacity, the conveying of the energy is then carried out by dedicated pipeline.With the appearance of emerging technology, in order to avoid
Take the multi-energy system of the compositions such as refrigeration, trilogy supply, ice storage, conventional electricity refrigeration more to have been favored by people.
With the popularization of tou power price policy, current most of gardens are only some simple controls to multi-energy system
System carries out cold-storage to ice storage when electricity price low ebb, electricity price peak value when is let cool, and lacks to whole system
Quantitative analysis and efficiently control.Therefore, existing multi-energy system can not be realized optimal while meeting total capacity requirement
Power, operating cost are relatively high.
Summary of the invention
For above-mentioned defect existing in the prior art, can be reduced technical problem to be solved by the invention is to provide a kind of
The multi-energy system dispatching method based on the prediction of radial base energy consumption and real-time energy efficiency of multi-energy system operating cost.
In order to solve the above-mentioned technical problem, provided by the present invention a kind of based on the prediction of radial base energy consumption and real-time energy efficiency
Multi-energy system dispatching method, the multi-energy system include cold, heat and electricity triple supply subsystem, electric refrigeration subsystem, ice storage
Subsystem, specific step is as follows for this method:
1) it itemizes building energy consumption data, the environment temperature, ambient humidity, building flow of the people of collection site, using adopting
The data of collection construct an energy consumption data library, and fill up to the building energy consumption data lacked in energy consumption data library, specifically
Fill up formula are as follows:
L (d, t)=a1*(d1,t)+a2*(d2,t)
Wherein, the building energy consumption data that L (d, t) is lacked by d days t moments of y, L (d1, t) and it is y d-1 days t moment
Building energy consumption data, L (d2, t) be y d-2 days t moment building energy consumption data, a1、a2For preset numerical value weight；
2) the building energy consumption data in energy consumption database are converted to by index without dimension assessment using method for normalizing
Value, so that each index value in building energy consumption data is all in the same number of levels, specific conversion formula are as follows:
WhereinFor the building energy consumption assessment value after normalization, xiFor building energy consumption measured value, m is input vector dimension
Number, ximaxFor the maximum value of sample data, ximinFor the minimum value of sample data；
3) using neural network radial basis function construct energy consumption prediction model, and in energy consumption data library environment temperature,
Data after ambient humidity, building flow of the people and step 2) normalized are input sample, are carried out to energy consumption prediction model
Training；
4) the building prediction of energy consumption of target day is predicted using energy consumption prediction model, it is pre- further according to the building of target day
The energy efficiency coefficient for surveying energy consumption and each powering device in multi-energy system implements regulation to the multi-energy system of building.
Further, in step 1), the value that the value of a1 is 0.6, a2 is 0.4.
Multi-energy system dispatching method provided by the invention based on the prediction of radial base energy consumption and real-time energy efficiency, using nerve
Network radial basis function prediction model predicts the following energy consumption of building, so as to predict building energy consumption situation in advance, and
The fortune of multi-energy system can be reduced to building with scheduling and planning can be made in advance in conjunction with the energy efficiency coefficient of powering device
Row cost.
Specific embodiment
Technical solution of the present invention is described in further detail below in conjunction with specific embodiment, but the present embodiment and is not had to
It is all that protection scope of the present invention should all be included in using similar structure and its similar variation of the invention in the limitation present invention, this
Pause mark in invention indicates the relationship of sum, and the English alphabet in the present invention is case sensitive.
A kind of multi-energy system scheduling based on the prediction of radial base energy consumption and real-time energy efficiency provided by the embodiment of the present invention
Method, the multi-energy system include cold, heat and electricity triple supply subsystem, electric refrigeration subsystem, ice storage subsystem, this method
Specific step is as follows:
1) it itemizes building energy consumption data, the environment temperature, ambient humidity, building flow of the people of collection site, using adopting
The data of collection construct an energy consumption data library, and fill up to the building energy consumption data lacked in energy consumption data library, specifically
Fill up formula are as follows:
L (d, t)=a1*(d1,t)+a2*(d2,t)
Wherein, the building energy consumption data that L (d, t) is lacked by d days t moments of y, L (d1, t) and it is y d-1 days t moment
Building energy consumption data, L (d2, t) be y d-2 days t moment building energy consumption data, a1、a2For preset numerical value weight, a1
Representative value be 0.6, a2 representative value be 0.4, the value of a1, a2 can be adjusted according to actual items；
The presence of the case where will appear shortage of data or data exception in the collection process of data, these abnormal datas are made
At the loss of a large amount of useful information, the certainty ingredient contained in system is made to be more difficult to hold, or even can make to predict
Entanglement is generated in journey, needs to pre-process the data of acquisition, repairs the missing and abnormal data in history energy consumption sequence；
2) the building energy consumption data in energy consumption database are converted to by index without dimension assessment using method for normalizing
Value, so that each index value in building energy consumption data will be built all in the same number of levels using method for normalizing
The method that object energy consumption data is converted to index without dimension assessment value is the prior art, specific conversion formula are as follows:
WhereinFor the building energy consumption assessment value after normalization, xiFor building energy consumption measured value, m is input vector dimension
Number (i.e. the quantity of the factor of influence building energy consumption), ximaxFor the maximum value of sample data, ximinFor the minimum of sample data
Value；
3) energy consumption prediction model is constructed using neural network radial basis function (RBF), and with the environment in energy consumption data library
Data after temperature, ambient humidity, building flow of the people and step 2) normalized are input sample, predict mould to energy consumption
Type is trained；
Use the method for neural network radial basis function (RBF) building energy consumption prediction model for the prior art, for large size
For public building, influences the parameter of energy consumption mostly and there is nonlinear characteristic, therefore establish energy consumption model and be typically complex,
And a large amount of data are handled, and neural network has good description non-thread as a kind of Nonlinear Modeling and prediction technique
Property ability and high fitting precision, carrying out modeling using neural network radial basis function (RBF) has that convergence is fast, precision is high
Effect；
4) the building prediction of energy consumption of target day is predicted using energy consumption prediction model, it is pre- further according to the building of target day
The energy efficiency coefficient for surveying energy consumption and each powering device in multi-energy system implements regulation to the multi-energy system of building.
Energy requirements and energy efficiency of equipment coefficient (COP) are the regulation basis of multi-energy system, energy efficiency of equipment coefficient and equipment
Performance, the method for operation, runing time it is related, in identical heating (cold), select the equipment fortune that energy efficiency of equipment coefficient is high
Row, can achieve the purpose that energy-saving operating cost.
Claims (2)
1. a kind of multi-energy system dispatching method based on the prediction of radial base energy consumption and real-time energy efficiency, the multi-energy system include
There are cold, heat and electricity triple supply subsystem, electric refrigeration subsystem, ice storage subsystem, specific step is as follows for this method:
1) building energy consumption data, the environment temperature, ambient humidity, building flow of the people of subitem collection site, utilizes acquisition
Data construct an energy consumption data library, and fill up to the building energy consumption data lacked in energy consumption data library, specifically fill up
Formula are as follows:
L (d, t)=a1*(d1,t)+a2*(d2,t)
Wherein, the building energy consumption data that L (d, t) is lacked by d days t moments of y, L (d1, t) be y d-1 days t moment building
Energy consumption data, L (d2, t) be y d-2 days t moment building energy consumption data, a1、a2For preset numerical value weight；
2) the building energy consumption data in energy consumption database are converted to by index without dimension assessment value using method for normalizing, made
Each index value in building energy consumption data all in the same number of levels, specific conversion formula are as follows:
WhereinFor the building energy consumption assessment value after normalization, xiFor building energy consumption measured value, m is input vector dimension,
ximaxFor the maximum value of sample data, ximinFor the minimum value of sample data；
3) energy consumption prediction model is constructed using neural network radial basis function, and with the environment temperature in energy consumption data library, environment
Data after humidity, building flow of the people and step 2) normalized are input sample, are instructed to energy consumption prediction model
Practice；
4) the building prediction of energy consumption that target day is predicted using energy consumption prediction model predicts energy further according to the building of target day
The energy efficiency coefficient of consumption and each powering device in multi-energy system implements regulation to the multi-energy system of building.
2. the multi-energy system dispatching method according to claim 1 based on the prediction of radial base energy consumption and real-time energy efficiency,
Be characterized in that: in step 1), the value that the value of a1 is 0.6, a2 is 0.4.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114611823A (en) * | 2022-03-23 | 2022-06-10 | 特斯联科技集团有限公司 | Optimized dispatching method and system for electricity-cold-heat-gas multi-energy-demand typical park |
| CN115994785A (en) * | 2023-01-09 | 2023-04-21 | 淮阴工学院 | Intelligent prediction method and system for catering traffic stock |
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| CN102831551A (en) * | 2012-08-17 | 2012-12-19 | 北京合众科林自动化工程技术有限公司 | Building energy management system |
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2019
- 2019-03-27 CN CN201910236056.XA patent/CN110046751A/en active Pending
Patent Citations (2)
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| CN102831551A (en) * | 2012-08-17 | 2012-12-19 | 北京合众科林自动化工程技术有限公司 | Building energy management system |
| CN105631539A (en) * | 2015-12-25 | 2016-06-01 | 上海建坤信息技术有限责任公司 | Intelligent building energy consumption prediction method based on support vector machine |
Non-Patent Citations (1)
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114611823A (en) * | 2022-03-23 | 2022-06-10 | 特斯联科技集团有限公司 | Optimized dispatching method and system for electricity-cold-heat-gas multi-energy-demand typical park |
| CN114611823B (en) * | 2022-03-23 | 2022-11-08 | 特斯联科技集团有限公司 | Optimized dispatching method and system for electricity-cold-heat-gas multi-energy-demand typical park |
| CN115994785A (en) * | 2023-01-09 | 2023-04-21 | 淮阴工学院 | Intelligent prediction method and system for catering traffic stock |
| CN115994785B (en) * | 2023-01-09 | 2023-09-29 | 淮阴工学院 | Intelligent prediction method and system for catering traffic stock |
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