CN113280543A - River water source heat pump system optimization control method and system based on multi-source data and DBN - Google Patents
River water source heat pump system optimization control method and system based on multi-source data and DBN Download PDFInfo
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Abstract
The invention discloses an optimized control method and system of a river water source heat pump system based on multi-source data and DBN, which comprises the steps of collecting data, cleaning the collected data, and extracting key data influencing the river water source heat pump system; evaluating the energy supply capacity of the current river water source heat pump system, the energy utilization capacity of a user, the energy transmission efficiency and the energy transmission loss of the river water source heat pump system according to key data obtained after data cleaning; evaluating the relation among energy supply capacity, user energy consumption capacity and energy transmission loss in different control schemes; and (3) establishing an optimal control model by combining key data of historical contemporaneous energy consumption information, and obtaining an optimal control scheme of the river source heat pump system by adopting a deep belief network optimization solution. The invention tracks the state information of the current water source, the user and the heat pump system in real time, fuses the historical state information, adopts a big data depth fusion algorithm, realizes the optimal control of the river water source heat pump system, and improves the energy transmission efficiency.
Description
Technical Field
The invention belongs to the technical field of river water source heat pump systems, and relates to an optimized control method and system of a river water source heat pump system based on multi-source data and a DBN (database data network).
Background
The water source heat pump technology is a technology which utilizes low-temperature and low-grade heat energy resources formed by solar energy and geothermal energy absorbed in shallow water sources on the earth surface, such as underground water, rivers and lakes, and adopts a heat pump principle to realize the transfer of low-grade heat energy to high-grade heat energy through the input of a small amount of high-grade electric energy. The working principle of the water source heat pump system is that heat in a building is transferred to a water source in summer; in winter, energy is extracted from a water source with relatively constant temperature, and the temperature is raised by using air or water as secondary refrigerant by utilizing the heat pump principle and then the secondary refrigerant is sent to a building, so that the energy utilization technology is a renewable energy utilization technology.
The river water source heat pump is used for taking heat from river water, is a good heat pump heat source and an air conditioner cold source, enables the heat pump unit to run more reliably and stably, and ensures the high efficiency and the economy of the system. A centralized cooling and heating system of a river water source heat pump is an ecological and environment-friendly energy supply mode for cooling or heating by utilizing river water temperature difference. River water is pumped and collected into the heat pump of the energy station by electric power to carry out cold-heat conversion, so that an air conditioning system is formed. Compared with the conventional air conditioning system, the energy supply mode utilizing the river water temperature difference saves more energy. The device has no pollution in operation, can be built in a residential area, has no combustion, no smoke exhaust, no waste discharge and no place for stacking fuel wastes.
At present, the control flexibility of the river water source heat pump system is poor. The river water source heat pump system has multiple and complex influence factors, and very complex nonlinear mapping relations exist among related state parameters. And the big data analysis can change the traditional detection method of the fixed threshold value, can integrate and analyze real-time and historical massive state data, realizes multi-dimensional and differential evaluation of water source function, user energy consumption and equipment conveying energy by utilizing the change of the correlation between longitudinal time and transverse state data, and can optimally regulate and control the river water source heat pump system in time.
Disclosure of Invention
In order to solve the defects in the prior art, the invention aims to provide a river source heat pump system optimization control method and system based on multi-source data and DBNs, which are used for tracking the state information of a current water source, a user and a heat pump system in real time, fusing historical state information and realizing the river source heat pump system optimization control by adopting a big data depth fusion algorithm.
The invention adopts the following technical scheme that,
a river water source heat pump system optimization control method based on multi-source data and DBNs comprises the following steps:
step 1, collecting data including meteorological information, hydrological information, user information, equipment running state information and historical contemporaneous energy utilization information;
step 2, cleaning the acquired data, and extracting key data influencing the river water source heat pump system;
step 3, evaluating the energy supply capacity of the current river water source heat pump system, the energy utilization capacity of a user, the energy transmission efficiency and the energy transmission loss of the river water source heat pump system according to the key data obtained after data cleaning;
step 4, evaluating the relation among energy supply capacity, user energy consumption capacity and energy transmission loss in different control schemes; if the energy supply level is greater than the user energy consumption level + energy transmission loss, performing step 5;
and 5, constructing an optimal control model by combining key data of historical contemporaneous energy consumption information, and obtaining an optimal control scheme of the river source heat pump system by adopting a depth confidence network optimization solution.
Further, in the step 2, the collected data is cleaned by adopting a principal component analysis method.
Further, the step 3 specifically includes:
step 3.1, evaluating the energy supply capacity of the current river water source heat pump system according to key data of meteorological information and hydrological information obtained after data cleaning;
step 3.2, evaluating the energy utilization capacity of the current user according to the weather information obtained after data cleaning and key data of the user information;
and 3.3, evaluating the energy transmission efficiency and energy transmission loss of the river water source heat pump system according to the key data of the equipment running state information obtained after data cleaning.
Further, in said step 3.1,
the energy supply capacity of the river water source heat pump system is the maximum energy supply power of the river water source heat pump system in unit time, and the following calculation formula is satisfied:
PG＝|t1-ts|×Mt
wherein, t1Is the current temperature of river water source, tsFor the user to demand the temperature, MtIs the quality of the water supply per unit time.
Further, in said step 3.2,
the energy utilization capacity of the user is the maximum energy utilization power required by the user in unit time, and the following calculation formula is satisfied:
PY＝|t2-ts|×Nt
wherein, t2Is the current temperature, t, of the usersFor the user to demand temperature, NtThe energy required for the temperature change of 1 degree is provided for the user.
Further, in said step 3.3,
the energy transmission efficiency is the maximum energy transmission efficiency of the river water source heat pump system in unit time, and the following calculation formula is satisfied:
maximum energy transmission efficiency is obtained by the user end/output power of the heat pump end
The energy transmission loss is the energy transmission loss of the river water source heat pump system in unit time, and the following calculation formula is satisfied:
energy transmission loss is the output power of the heat pump end, and the user end obtains the power.
Further, in the step 4,
and establishing an incidence relation model by adopting a Bayesian network, evaluating the relation among energy supply capacity, user energy consumption capacity and energy transmission loss in different control schemes.
Further, in the step 5,
and constructing an optimization control model, wherein the optimization target is energy transmission efficiency, and the constraint conditions are that the user energy level and the river water energy supply level are met.
A river water source heat pump system optimization control system based on multi-source data and DBNs comprises a data integration module, a data cleaning module, a river water energy supply level evaluation module, a user energy consumption level evaluation module, an energy transmission loss evaluation module, an association rule mining module and a river water source heat pump system optimization control module.
The data integration module is used for acquiring data information of the river water source heat pump system, wherein the data information comprises meteorological information, hydrological information, user information, equipment running state information and historical contemporaneous energy utilization information;
the data cleaning module is used for cleaning the acquired data by adopting a principal component analysis method and extracting key data influencing the river water source heat pump system;
the river water energy supply level evaluation module is used for evaluating the energy supply capacity of the current river water source heat pump system according to main key data of meteorological information and hydrological information obtained after data cleaning;
the user energy level evaluation module is used for evaluating the energy utilization capacity of the current user according to the weather information obtained after data cleaning and the main key data of the user information;
the energy transmission loss evaluation module is used for evaluating the energy transmission efficiency and energy transmission loss of the river water source heat pump system according to main key data of equipment running state information obtained after data cleaning;
the association rule mining module is used for establishing an association relation among results evaluated by the river water energy supply level evaluation module, the user energy consumption level evaluation module and the energy transmission loss evaluation module by adopting a Bayesian network;
and the optimized control module of the river water source heat pump system constructs an optimized control model by combining key data of historical synchronous energy consumption information after data cleaning, and obtains an optimized control scheme of the river water source heat pump system by adopting a depth confidence network optimization solution.
The invention has the advantages that compared with the prior art,
according to the river water source heat pump system optimization control system and method fusing multi-source state data and a deep belief network, state information of a current water source, a user and a heat pump system is tracked in real time, historical state information is fused, and a large-data deep fusion algorithm is adopted to realize optimization control of the river water source heat pump system, so that the aims of high energy transmission efficiency, accurate control precision and small system loss are achieved.
Drawings
FIG. 1 is a schematic structural diagram of a river water source heat pump system optimization control system fusing multi-source state data and a deep belief network, which is disclosed by the invention;
FIG. 2 is a flow chart of the optimized control method of the river water source heat pump system fusing multi-source state data and a deep belief network.
Detailed Description
The present application is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present application is not limited thereby.
As shown in fig. 1, the optimized control system of a river water source heat pump system based on multi-source state data and a Deep Belief Network (DBN) according to the present invention includes a data integration module, a data cleaning module, a river water energy supply level evaluation module, a user energy consumption level evaluation module, an energy transmission loss evaluation module, an association rule mining module, and a river water source heat pump system optimized control module.
And the data integration module is used for acquiring data information of the river water source heat pump system, wherein the data information comprises meteorological information, hydrological information, user information, equipment running state information and historical contemporaneous energy utilization information.
And the data cleaning module is used for cleaning the data acquired by the data integration module by adopting a principal component analysis method and extracting main key data influencing the river water source heat pump system.
And the river water energy supply level evaluation module is used for evaluating the energy supply capacity of the current river water source heat pump system according to main key data of meteorological information and hydrological information obtained after data cleaning.
And the user energy level evaluation module evaluates the energy utilization capacity of the current user according to the weather information obtained after the data cleaning and the main key data of the user information.
And the energy transmission loss evaluation module evaluates the energy transmission efficiency and energy transmission loss of the river water source heat pump system according to the main key data of the equipment running state information obtained after data cleaning.
And the association rule mining module is used for establishing an association relation among results evaluated by the river water energy supply level evaluation module, the user energy consumption level evaluation module and the energy transmission loss evaluation module by adopting a Bayesian network.
The optimized control module of the river water source heat pump system adopts a depth confidence network, utilizes the energy supply capacity, the energy consumption capacity, the energy transmission efficiency and the energy transmission loss obtained by the current river water energy supply level evaluation module, the user energy consumption level evaluation module and the energy transmission loss evaluation module, and combines the main key data of the historical contemporaneous energy consumption information after data cleaning and collected by the data integration module to obtain the control scheme of the optimized river water source heat pump system.
As shown in fig. 2, the method for optimizing and controlling a river-water source heat pump system based on multi-source state data and a Deep Belief Network (DBN) according to the present invention includes the following steps:
step 1, a data integration module collects data information, and the data information comprises the following steps: weather information, hydrological information, user information, equipment running state information and historical contemporaneous energy utilization information;
the meteorological information comprises seasons, average wind speed in the first n hours, average air temperature in the first n hours and average illumination intensity in the first n hours; the hydrological information comprises the water speed, water level and water temperature of river water; the user information comprises the current user number and the temperature requirement; the equipment running state information comprises the number of current users and energy utilization requirements; the historical contemporaneous energy use information comprises historical contemporaneous weather information, hydrological information, user information and equipment running state information.
Wherein, the sampling frequency of the wind speed, the air temperature and the illumination intensity is once in 10 minutes, and the average value n is calculated and taken for 2 hours.
Step 2, cleaning the data collected by the data integration module by adopting a principal component analysis method, and extracting main key data influencing the river water source heat pump system;
the method comprises the following steps that 5 types of information including meteorological information, hydrological information, user information, equipment running state information and historical synchronous energy utilization information are provided in the step 1, the data are various, and the data are screened through a principal component analysis method at this time, so that main key data influencing a river water source heat pump system are extracted.
Step 3, evaluating the energy supply capability of the current river water source heat pump system by the river water energy supply level evaluation module according to main key data of meteorological information and hydrological information obtained after data cleaning;
the energy supply capacity of the river water energy supply level evaluation module is the maximum energy supply power of the river water source heat pump system in unit time.
The specific calculation formula of the maximum energy supply power of the river water source heat pump system in unit time is as follows:
PG＝|t1-ts|×Mt
wherein, t1Is the current temperature of river water source, tsFor the user to demand the temperature, MtIs the quality of the water supply per unit time.
Step 4, the user energy utilization level evaluation module evaluates the energy utilization capacity of the current user according to the weather information obtained after data cleaning and the main key data of the user information;
the energy utilization capacity of the user energy level evaluation module is the maximum energy utilization power required by the user per unit time.
The specific calculation formula of the maximum energy utilization power required by the user in unit time is as follows:
PY＝|t2-ts|×Nt
wherein, t2Is the current temperature, t, of the usersFor the user to demand temperature, NtThe energy required for the temperature change of 1 degree is provided for the user.
Step 5, the energy transmission loss evaluation module evaluates the energy transmission efficiency and the energy transmission loss of the river water source heat pump system according to main key data of the equipment running state information obtained after data cleaning;
the energy transmission efficiency and the energy transmission loss of the energy transmission loss evaluation module are the maximum energy transmission efficiency and the maximum energy transmission loss of the river source heat pump system in unit time.
The specific calculation formula of the maximum energy transmission efficiency of the river water source heat pump system in unit time is as follows:
maximum energy transmission efficiency is obtained by the user end/output power of the heat pump end
The specific calculation formula of the energy transmission loss of the river water source heat pump system in unit time is as follows:
energy transmission loss is equal to heat pump end output power-user end obtained power
Step 6, a data association rule mining module adopts a Bayesian network to establish a relation among results evaluated by a river water energy supply level evaluation module, a user energy consumption level evaluation module and an energy transmission loss evaluation module;
and (3) establishing an incidence relation model by adopting a Bayesian network, evaluating the relation among the three in different energy transmission control schemes (or the operation mode of the river water source heat pump system), and if the energy supply level is greater than the user energy consumption level plus the energy transmission loss, performing the step (7).
And 7, obtaining a control scheme for optimizing the river water source heat pump system by adopting a depth confidence network and by utilizing the energy supply capacity, the energy utilization capacity, the energy transmission efficiency and the energy transmission loss which are obtained by the current river water energy supply level evaluation module, the user energy utilization level evaluation module and the energy transmission loss evaluation module and combining main key data of historical contemporaneous energy utilization information which is acquired by the data integration module and subjected to data cleaning.
The optimization target of the control scheme for optimizing the river water source heat pump system is to increase the energy transmission efficiency of the river water source heat pump system to the maximum extent, reduce the energy transmission loss to the maximum extent and reduce the water consumption of a water source on the basis of meeting the requirements of users.
And constructing an optimization control model, wherein the optimization target is to improve the energy transmission efficiency, and the constraint conditions are to meet the user energy level, the river water energy supply level and the energy supply level > the user energy level + the transmission loss level. And performing optimization solution by adopting a deep belief network to obtain an optimal control scheme.
The invention has the advantages that compared with the prior art,
according to the river water source heat pump system optimization control system and method fusing multi-source state data and a deep belief network, state information of a current water source, a user and a heat pump system is tracked in real time, historical state information is fused, and a large-data deep fusion algorithm is adopted to realize optimization control of the river water source heat pump system, so that the aims of high energy transmission efficiency, accurate control precision and small system loss are achieved.
The present applicant has described and illustrated embodiments of the present invention in detail with reference to the accompanying drawings, but it should be understood by those skilled in the art that the above embodiments are merely preferred embodiments of the present invention, and the detailed description is only for the purpose of helping the reader to better understand the spirit of the present invention, and not for limiting the scope of the present invention, and on the contrary, any improvement or modification made based on the spirit of the present invention should fall within the scope of the present invention.
Claims (9)
1. A river water source heat pump system optimization control method based on multi-source data and DBN is characterized by comprising the following steps:
step 1, collecting data including meteorological information, hydrological information, user information, equipment running state information and historical contemporaneous energy utilization information;
step 2, cleaning the acquired data, and extracting key data influencing the river water source heat pump system;
step 3, evaluating the energy supply capacity of the current river water source heat pump system, the energy utilization capacity of a user, the energy transmission efficiency and the energy transmission loss of the river water source heat pump system according to the key data obtained after data cleaning;
step 4, evaluating the relation among energy supply capacity, user energy consumption capacity and energy transmission loss in different control schemes; if the energy supply level is greater than the user energy consumption level + energy transmission loss, performing step 5;
and 5, constructing an optimal control model by combining key data of historical contemporaneous energy consumption information, and obtaining an optimal control scheme of the river source heat pump system by adopting a depth confidence network optimization solution.
2. The river water source heat pump system optimization control method based on multi-source data and DBNs of claim 1, wherein in the step 2,
and cleaning the acquired data by adopting a principal component analysis method.
3. The river water source heat pump system optimization control method based on multi-source data and DBNs according to claim 1, wherein the step 3 specifically comprises the following steps:
step 3.1, evaluating the energy supply capacity of the current river water source heat pump system according to key data of meteorological information and hydrological information obtained after data cleaning;
step 3.2, evaluating the energy utilization capacity of the current user according to the weather information obtained after data cleaning and key data of the user information;
and 3.3, evaluating the energy transmission efficiency and energy transmission loss of the river water source heat pump system according to the key data of the equipment running state information obtained after data cleaning.
4. The river water source heat pump system optimization control method based on multi-source data and DBN according to claim 3, wherein in the step 3.1,
the energy supply capacity of the river water source heat pump system is the maximum energy supply power of the river water source heat pump system in unit time, and the following calculation formula is satisfied:
PG＝|t1-ts|×Mt
wherein, t1Is the current temperature of river water source, tsFor the user to demand the temperature, MtIs the quality of the water supply per unit time.
5. The river water source heat pump system optimization control method based on multi-source data and DBN according to claim 3, wherein in the step 3.2,
the energy utilization capacity of the user is the maximum energy utilization power required by the user in unit time, and the following calculation formula is satisfied:
PY＝|t2-ts|×Nt
wherein, t2Is the current temperature, t, of the usersFor the user to demand temperature, NtThe energy required for the temperature change of 1 degree is provided for the user.
6. The river water source heat pump system optimization control method based on multi-source data and DBN according to claim 3, wherein in the step 3.3,
the energy transmission efficiency is the maximum energy transmission efficiency of the river water source heat pump system in unit time, and the following calculation formula is satisfied:
maximum energy transmission efficiency is obtained by the user end/output power of the heat pump end
The energy transmission loss is the energy transmission loss of the river water source heat pump system in unit time, and the following calculation formula is satisfied:
energy transmission loss is the output power of the heat pump end, and the user end obtains the power.
7. The river water source heat pump system optimization control method based on multi-source data and DBNs of claim 1, wherein in the step 4,
and establishing an incidence relation model by adopting a Bayesian network, evaluating the relation among energy supply capacity, user energy consumption capacity and energy transmission loss in different control schemes.
8. The river water source heat pump system optimization control method based on multi-source data and DBNs of claim 1, wherein in the step 5,
and constructing an optimization control model, wherein the optimization target is energy transmission efficiency, and the constraint conditions are that the user energy level and the river water energy supply level are met.
9. A river water source heat pump system optimization control system based on multi-source data and DBN is characterized by comprising a data integration module, a data cleaning module, a river water energy supply level evaluation module, a user energy utilization level evaluation module, an energy transmission loss evaluation module, an association rule mining module and a river water source heat pump system optimization control module;
the data integration module is used for acquiring data information of the river water source heat pump system, wherein the data information comprises meteorological information, hydrological information, user information, equipment running state information and historical contemporaneous energy utilization information;
the data cleaning module is used for cleaning the acquired data by adopting a principal component analysis method and extracting key data influencing the river water source heat pump system;
the river water energy supply level evaluation module is used for evaluating the energy supply capacity of the current river water source heat pump system according to main key data of meteorological information and hydrological information obtained after data cleaning;
the user energy level evaluation module is used for evaluating the energy utilization capacity of the current user according to the weather information obtained after data cleaning and the main key data of the user information;
the energy transmission loss evaluation module is used for evaluating the energy transmission efficiency and energy transmission loss of the river water source heat pump system according to main key data of equipment running state information obtained after data cleaning;
the association rule mining module is used for establishing an association relation among results evaluated by the river water energy supply level evaluation module, the user energy consumption level evaluation module and the energy transmission loss evaluation module by adopting a Bayesian network;
and the optimized control module of the river water source heat pump system constructs an optimized control model by combining key data of historical synchronous energy consumption information after data cleaning, and obtains an optimized control scheme of the river water source heat pump system by adopting a depth confidence network optimization solution.
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