CN113835351A - Intelligent household power utilization optimization control system and method based on multi-terminal cooperative architecture - Google Patents
Intelligent household power utilization optimization control system and method based on multi-terminal cooperative architecture Download PDFInfo
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Abstract
The invention discloses an intelligent household power utilization optimization control system based on a multi-terminal collaborative framework, which is characterized by comprising a cloud management and control platform, intelligent household clusters and a Web application server, wherein the intelligent household clusters are communicated with the cloud management and control platform, and each intelligent household cluster is connected with a plurality of intelligent household and environment data collectors. The invention also provides a corresponding method, and by implementing the embodiment of the invention, the electricity utilization optimization control of the intelligent home can be realized, the comfortable feeling of electricity utilization of users is enhanced, the interactivity is improved, and the electricity fee can be saved.
Description
Technical Field
The invention belongs to the technical field of digital home intelligent home, and particularly relates to an intelligent home power optimization control system and method based on a multi-terminal cooperative architecture.
Background
Nowadays, the economy is developed vigorously, people pursue higher and higher self life quality, and intelligent household equipment is integrated into each family. More and more consumers interact with household appliances through intelligent APP for home control, such as control switch, operation mode selection and the like. In order to save electricity and construct a green low-carbon society, the step price and the time-of-use price are mostly adopted. At present, consumers are required to know the electricity charge generally from monthly bills. For the implementation of the step electricity price and the time-of-use electricity price, the electricity consumption condition cannot be well predicted by the consumer, and the daily electricity consumption cannot be better planned. Some smart home APPs can control the on-off of an electric appliance, but there is no perfect strategy to accurately plan the electricity consumption behavior by combining the electricity price. With the rise and development of the household energy management system, different optimization strategies are established, but the strategies do not have an aspect platform for consumers to use.
Meanwhile, most of the existing smart homes are interconnected in the same Wi-Fi, if each smart home is directly regulated and controlled uniformly, the information access cost is greatly increased due to the action, the cloud computing also faces the problem of dimension disaster, and the privacy protection of a user is often difficult to guarantee.
Disclosure of Invention
The invention aims to solve the technical problem of providing an intelligent household power consumption optimization control system and method based on a multi-terminal cooperative architecture, which can realize power consumption optimization control of an intelligent household, enhance the comfortable feeling of user power consumption, improve interactivity and save power charge.
In order to solve the technical problems, the invention provides an intelligent household power optimization control system based on a multi-terminal cooperative architecture, which comprises a cloud management and control platform, intelligent household clusters and a Web application server, wherein the intelligent household clusters are communicated with the cloud management and control platform, and each intelligent household cluster is connected with a plurality of intelligent household and environment data collectors; wherein:
the cloud management and control platform is used for receiving the aggregation models from the intelligent home clusters, calculating the aggregation models by combining time-of-use electricity price information and current environment data after receiving the setting requirements, sent by a user through the Web application server, for the selected intelligent home, obtaining an optimal control scheme for the intelligent home, generating a regulation and control instruction for each intelligent home, and issuing the regulation and control instruction through the intelligent home clusters;
the intelligent home cluster is used for constructing a polymerization model for each intelligent home in the cluster, uploading the polymerization model to the cloud management and control platform, simultaneously responding to a regulation and control instruction sent by the cloud management and control platform on line, and decomposing the regulation and control instruction to each intelligent home in the cluster; the cloud management and control platform is used for collecting current environment data through the environment data collector and uploading the current environment data to the cloud management and control platform;
and the Web application server is used for receiving the setting requirements of the user on various smart homes, uploading the setting requirements to the cloud management and control platform, receiving the optimization control scheme of the cloud management and control platform and displaying the optimization control scheme.
Preferably, the smart home comprises: at least one of an air conditioner, a refrigerator, a washing machine, an electric lamp, and a water heater; the environmental data includes: at least one of infrared sensing data, temperature and humidity data, photosensitive data and room position data.
Preferably, a Web end interactive interface is adopted between the user and the Web application server for interaction; in the Web-end interactive interface, the input part at least comprises: the system comprises an air conditioner selection item, a use time range input item, a comfort temperature input item, an air conditioner mode selection item and a historical record query item; the display part at least comprises a three-dimensional bar chart of the temperature before and after optimization, a line chart of the electricity consumption used in corresponding time, a pie chart which is 24-hour electricity ranking, and a table of integration time, indoor and outdoor temperature, power consumption and cost.
Preferably, in the smart home cluster, the aggregation model of the air conditioners is constructed by the following formula:
the discretization operation model of the energy consumption and the temperature in the room is expressed as follows:
wherein T is 1, 2.·, T; t is the total time interval, and delta T is the scheduling time interval;
is the indoor temperature in the jth house at the moment t;
the thermal power consumption in the jth house at the moment t; cMIs thermal mass specific heat capacity; gjIs the thermal mass in the jth house;
the thermal power in the house is limited by the rated thermal power range and the climbing limit of the radiator respectively and jointly by the formulas (4.a) and (4.b), and the indoor temperature is limited by the formula (4. c); finally, the discretization operation model in the jth house in the formula (1) is obtained and is shown in a formula (4. d):
in the formula, COP is the energy efficiency ratio of the corresponding air conditioner;
the heating value (cooling capacity) of the jth air conditioner at the time t;
the heat power of the jth air conditioner at the moment t; in that
The minimum heating power and the maximum heating power of the jth air conditioner are obtained;
is the lowest and highest indoor temperatures acceptable by the user in the jth house;
represents the maximum thermal power ramp rate of the jth house heat dissipation (TCR); suseThe air conditioner is used, 1 is used, and 0 is not used;
is the indoor initial temperature; alpha is alphaj，βj，γjIs the equivalent coefficient parameter of the jth house.
Accordingly, in another aspect of the present invention, an intelligent household power optimization control method based on a multi-terminal cooperation architecture is further provided, which is implemented by using the foregoing system, and is characterized in that the method includes the following steps:
step S10, the Web application server receives setting requirements of various smart homes from users and uploads the requirements to the cloud management and control platform;
step S11, after receiving a setting requirement for a selected smart home sent by a user through a Web application server, a cloud management and control platform calls a pre-stored aggregation model for the smart home, calculates by combining time-of-use electricity price information and current environment data, obtains an optimized control scheme for the smart home and generates a corresponding regulation and control instruction, and issues the regulation and control instruction through a smart home cluster;
step S12, enabling the intelligent home cluster to respond to a regulation and control instruction issued by the cloud management and control platform on line and decompose the regulation and control instruction into the intelligent homes in the cluster;
and step S13, the Web application server receives the optimized control scheme from the cloud management and control platform and displays the optimized control scheme to the user.
Preferably, further comprising:
step S20, the intelligent home cluster pre-constructs a polymerization model for each intelligent home in the cluster and uploads the polymerization model to a cloud management and control platform;
and step S21, the intelligent home cluster uploads the real-time environment data collected by the environment data collector to the cloud management and control platform.
Preferably, further comprising:
a user interacts with a Web application server through a Web end interactive interface, and specifically, the user performs input operation of an air conditioner selection item, a use time range input item, a comfort temperature input item, an air conditioner mode selection item and a historical record query item in the Web end interactive interface; and displaying the following information on a display portion thereof: the three-dimensional bar chart of the temperature before and after optimization, the line chart of the electricity consumption used at the corresponding time and the pie chart are tables of 24-hour electricity ranking, integration time, indoor and outdoor temperature, power consumption and cost.
Preferably, in step S20, the smart home cluster constructs an aggregation model of air conditioners by:
step S200, determining a discretization operation model of energy consumption and temperature in the room as the following formula:
wherein, T is 1, 2.. times.t; t is the total time interval, and delta T is the scheduling time interval;
is the indoor temperature in the jth house at the moment t;
for the j-th house at time tInternal thermal power consumption; cMIs thermal mass specific heat capacity; gjIs the thermal mass in the jth house;
step S201, respectively obtaining the heat consumption of the building envelope structure by adopting the following formula
Heat loss by infiltration
And ventilation heat loss
Wherein,
and
heat loss coefficients corresponding to building envelope heat consumption, infiltration heat consumption and ventilation heat consumption are respectively set;
the outdoor ambient temperature at time t can be measured by a temperature sensor;
step S202, respectively adopting formulas (4.a) and (4.b) to limit the thermal power in the house by the rated thermal power range and the climbing limit of the radiator, and adopting formula (4.c) to limit the indoor temperature; finally, the discretization operation model in the jth house in the formula (1) is obtained and is shown in a formula (4. d):
in the formula, COP is the energy efficiency ratio of the corresponding air conditioner;
the heating value (cooling capacity) of the jth air conditioner at the time t;
the heat power of the jth air conditioner at the moment t; in that
The minimum heating power and the maximum heating power of the jth air conditioner are obtained;
is the lowest and highest indoor temperatures acceptable by the user in the jth house;
represents the maximum thermal power ramp rate of the jth house heat dissipation (TCR); suseThe air conditioner is used, 1 is used, and 0 is not used;
is the indoor initial temperature; alpha is alphaj，βj，γjIs the equivalent coefficient parameter of the jth house.
The implementation of the invention has the following beneficial effects:
the invention provides an intelligent household power optimization control system and method based on a multi-terminal cooperative framework. The intelligent household power utilization optimization control can be realized, the power utilization comfort feeling of a user is enhanced, the interactivity is improved, and the electric charge can be saved.
Meanwhile, in the embodiment of the invention, a platform for intuitively knowing the electricity consumption and the environmental conditions of the electric appliance is provided for the user by contacting the user with the electric appliance at home. After the user inputs the subjective feeling of the user, the Web can be visually displayed, so that the user can know the electricity utilization condition at any time and plan the future electricity utilization. Therefore, the behavior of saving electricity of the user can be further promoted, and the national call for energy conservation, emission reduction and low-carbon life can be responded.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is within the scope of the present invention for those skilled in the art to obtain other drawings based on the drawings without inventive exercise.
Fig. 1 is a schematic structural diagram of an embodiment of an intelligent household power optimization control system based on a multi-terminal cooperative architecture, provided by the invention;
fig. 2 is a schematic main flow diagram of an embodiment of an intelligent household power optimization control system based on a multi-terminal cooperative architecture, provided by the present invention;
FIG. 3 is a schematic diagram of an initial interface in an example of the Web-side interactive interface referred to in FIG. 2;
FIG. 4 is a schematic diagram of an example of the Web-side interactive interface shown in FIG. 2 after data is loaded;
FIG. 5 is a schematic diagram of an optimized interface in an example of the Web-side interactive interface shown in FIG. 2;
FIG. 6 is a schematic diagram of an interface for displaying history information in an example of the Web-side interactive interface shown in FIG. 2.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, a schematic structural diagram of an embodiment of an intelligent household power optimization control system based on a multi-terminal cooperative architecture is shown; in this embodiment, the multi-peer coordination architecture is a cloud-cluster-peer architecture. Specifically, the system comprises: the system comprises a cloud management and control platform 1, a plurality of intelligent home clusters 2 and a Web application server 4, wherein the intelligent home clusters 2 are communicated with the cloud management and control platform 1, and each intelligent home cluster 2 is connected with a plurality of intelligent homes 3 and an environmental data collector 5; wherein:
the cloud management and control platform 1 is used for receiving the aggregation models from the intelligent home clusters 2, calculating the aggregation models by combining time-of-use electricity price information and current environmental data after receiving a setting requirement which is sent by a user through the WEB application server 4 and aims at a selected intelligent home, obtaining an optimized control scheme for the intelligent home, generating a regulation and control instruction for each intelligent home 3, and issuing the regulation and control instruction through the intelligent home clusters 2; it can be understood that the cloud management and control platform located at the cloud end is a decision center of the whole framework, and the cloud management and control platform can dynamically aggregate smart homes. Analyzing various intelligent homes in the cluster on line to obtain an optimized control scheme, and generating a regulation and control instruction to issue; wherein the time of use electricity price signal can be obtained from a power system trading platform connected with the time of use electricity price signal.
The intelligent home cluster 2 is used for constructing a polymerization model for each intelligent home 3 in the cluster, uploading the polymerization model to the cloud management and control platform 1, simultaneously responding to a regulation and control instruction sent by the cloud management and control platform 1 on line, and decomposing the regulation and control instruction to each intelligent home 3 in the cluster; the cloud management and control platform 1 is used for collecting current environment data through the environment data collector 5 and uploading the current environment data to the cloud management and control platform 1; it can be understood that the smart home cluster 2 is a "cluster" hierarchy formed by cluster control logic layers of smart homes, and plays a role in online aggregation of various resources in the cluster in the whole control framework.
And the Web application server 4 is used for receiving the setting requirements of the user on various smart homes 3, uploading the setting requirements to the cloud management and control platform 1, receiving the optimization control scheme of the cloud management and control platform 1, and displaying the optimization control scheme.
Wherein, smart home includes: at least one of an air conditioner, a refrigerator, a washing machine, an electric lamp, and a water heater; the environmental data includes: at least one of infrared sensing data, temperature and humidity data, photosensitive data and room position data. It can be understood that the smart home and the environmental data sensor arranged at the end side are the bottommost layer of the whole control framework, and are responsible for collecting and sensing various smart homes, communicating with the upper layer for real-time operation information, and automatically receiving and responding to a regulation and control instruction issued by a group level (an intelligent product cluster).
The method comprises the following steps that a Web end interactive interface is adopted between a user and a Web application server for interaction; in the Web-end interactive interface, the input part at least comprises: the system comprises an air conditioner selection item, a use time range input item, a comfort temperature input item, an air conditioner mode selection item and a historical record query item; the display part at least comprises a three-dimensional bar chart of the temperature before and after optimization, a line chart of the electricity consumption used in corresponding time, a pie chart which is 24-hour electricity ranking, and a table of integration time, indoor and outdoor temperature, power consumption and cost.
It can be understood that an aggregation model of each smart home needs to be constructed in advance in the smart home cluster. In one specific example, the aggregation model of the air conditioner may be constructed by the following formula:
the discretization operation model of the energy consumption and the temperature in the room is expressed as follows:
wherein T is 1, 2.·, T; t is the total time interval, and delta T is the scheduling time interval;
is the indoor temperature in the jth house at the moment t;
the thermal power consumption in the jth house at the moment t; cMIs thermal mass specific heat capacity; gjIs the thermal mass in the jth house;
the thermal power in the house is limited by the rated thermal power range and the climbing limit of the radiator respectively and jointly by the formulas (4.a) and (4.b), and the indoor temperature is limited by the formula (4. c); finally, the discretization operation model in the jth house in the formula (1) is obtained and is shown in a formula (4. d):
in the formula, COP is the energy efficiency ratio of the corresponding air conditioner;
the heating value (cooling capacity) of the jth air conditioner at the time t;
the heat power of the jth air conditioner at the moment t; in that
The minimum heating power and the maximum heating power of the jth air conditioner are obtained;
is the lowest and highest indoor temperatures acceptable by the user in the jth house;
represents the maximum thermal power ramp rate of the jth house heat dissipation (TCR); suseThe air conditioner is used, 1 is used, and 0 is not used;
is the indoor initial temperature; alpha is alphaj，βj，γjIs the equivalent coefficient parameter of the jth house.
It can be understood that the modeling processes of other smart homes are similar to this, and are not described in detail.
Fig. 2 shows a main flow diagram of an embodiment of an intelligent household power optimization control method based on a multi-terminal coordination architecture according to the present invention. In the present embodiment, it is implemented by using the system described in the foregoing fig. 1, and the method includes the following steps:
step S10, the Web application server receives setting requirements of various smart homes from users and uploads the requirements to the cloud management and control platform;
step S11, after receiving a setting requirement for a selected smart home sent by a user through a Web application server, a cloud management and control platform calls a pre-stored aggregation model for the smart home, calculates by combining time-of-use electricity price information and current environment data, obtains an optimized control scheme for the smart home and generates a corresponding regulation and control instruction, and issues the regulation and control instruction through a smart home cluster;
step S12, enabling the intelligent home cluster to respond to a regulation and control instruction issued by the cloud management and control platform on line and decompose the regulation and control instruction into the intelligent homes in the cluster;
and step S13, the Web application server receives the optimized control scheme from the cloud management and control platform and displays the optimized control scheme to the user.
More specifically, the method further comprises the following steps:
step S20, the intelligent home cluster pre-constructs a polymerization model for each intelligent home in the cluster and uploads the polymerization model to a cloud management and control platform;
and step S21, the intelligent home cluster uploads the real-time environment data collected by the environment data collector to the cloud management and control platform.
Preferably, in step S20, the smart home cluster constructs an aggregation model of air conditioners by:
step S200, determining a discretization operation model of energy consumption and temperature in the room as the following formula:
wherein, t is 1, 2.,t; t is the total time interval, and delta T is the scheduling time interval;
is the indoor temperature in the jth house at the moment t;
the thermal power consumption in the jth house at the moment t; cMIs thermal mass specific heat capacity; gjIs the thermal mass in the jth house;
step S201, respectively obtaining the heat consumption of the building envelope structure by adopting the following formula
Heat loss by infiltration
And ventilation heat loss
Wherein,
and
heat loss coefficients corresponding to building envelope heat consumption, infiltration heat consumption and ventilation heat consumption are respectively set;
the outdoor ambient temperature at time t can be measured by a temperature sensor;
step S202, respectively adopting formulas (4.a) and (4.b) to limit the thermal power in the house by the rated thermal power range and the climbing limit of the radiator, and adopting formula (4.c) to limit the indoor temperature; finally, the discretization operation model in the jth house in the formula (1) is obtained and is shown in a formula (4. d):
in the formula, COP is the energy efficiency ratio of the corresponding air conditioner;
the heating value (cooling capacity) of the jth air conditioner at the time t;
the heat power of the jth air conditioner at the moment t; in that
The minimum heating power and the maximum heating power of the jth air conditioner are obtained;
is the lowest and highest indoor temperatures acceptable by the user in the jth house;
represents the maximum thermal power ramp rate of the jth house heat dissipation (TCR); suseThe air conditioner is used, 1 is used, and 0 is not used;
is the indoor initial temperature; alpha is alphaj，βj，γjIs the equivalent coefficient parameter of the jth house.
More specifically, the method further comprises the following steps:
a user interacts with a Web application server through a Web end interactive interface, and specifically, the user performs input operation of an air conditioner selection item, a use time range input item, a comfort temperature input item, an air conditioner mode selection item and a historical record query item in the Web end interactive interface; and displaying the following information on a display portion thereof: the three-dimensional bar chart of the temperature before and after optimization, the line chart of the electricity consumption used at the corresponding time and the pie chart are tables of 24-hour electricity ranking, integration time, indoor and outdoor temperature, power consumption and cost.
The following describes a Web-side interactive interface according to the present invention with a specific example:
the main function of the Web-side interactive interface is to provide an interactive platform for users. The administrator may publish it on a local area network or a mapping on a wide area network, which the user may access. The method has the advantages that the 24-hour indoor temperature and the corresponding time electricity price are displayed on the Web-end interactive interface. After the user combines the required requirements, the user can select the use mode of the electric appliance and input the expected comfort temperature and the use time. And the final prediction calculation result is transmitted back to a WEB terminal interactive interface, so that a user can know the power utilization condition in real time, and the use comfort is improved.
In a specific implementation, a separate desktop or Web application can be created directly based on Matlab Compiler. By using MATLAB Web App Server, the method can support integration with authentication standards such as OpenID Connect and LDAP. This allows the designer to directly control access to the Web application. The user can run the Web application directly from the browser without installing any other software. In addition, multiple applications developed using different versions of MATLAB and Simulink may also be hosted and shared on the server.
Taking an air conditioner as an example, the input part comprises air conditioner selection, use time range input, comfort temperature input, air conditioner mode selection and the like. Selecting a different date will correspond to displaying the temperature condition for that date. The display part comprises a three-dimensional bar chart of the temperature before and after optimization, a line chart of the electricity consumption used at the corresponding time, a 24-hour electricity ranking chart, and a table of integration time, indoor and outdoor temperature, power consumption and cost. The specific pattern is shown in fig. 3.
After the user opens the Web-end interactive interface, the user can select the electric appliance to be used to load data. After the load data key is pressed, the Web-side interface displays the outdoor temperatures at different time points corresponding to the dates, as shown in fig. 4.
After the data is loaded, the user can customize the requirements of the electric appliance. The user inputs the range of the using time and the range of the comfort temperature, after the using mode of the electric appliance is selected, the background of the optimization key is pressed, the optimal value meeting the requirements can be obtained through calculation according to the requirements, and the optimal value is displayed in the interface. As shown in fig. 5, after the mode selection heating mode with the temperature selection of 23-28 ℃ and the usage time set to 7 am to 12 pm and 1 pm to 11 pm, click for optimization. The corresponding display interface displays various data, such as an optimized and original temperature graph, a 24-hour power consumption ranking pie graph, a power utilization curve graph and various data charts. The power utilization condition when algorithm optimization is not carried out is represented by the larger span in the power utilization curve graph, and the planning result after cloud algorithm optimization is represented by the smaller span. The temperature histogram shows the outdoor original temperature 24 hours on that date, the temperature after optimization and the temperature for optimization, respectively.
As shown in fig. 6, the past day record is stored in the cloud management and control end, and the Web-end interactive interface can display the record of the previous optimized electric energy. When the Week key is pressed, the electricity consumption graph responds to and displays the condition of optimizing the electricity consumption power for one Week. The user can know the power utilization behavior in the past period.
The implementation of the invention has the following beneficial effects:
the invention provides an intelligent household power optimization control system and method based on a multi-terminal cooperative framework. The intelligent household power utilization optimization control can be realized, the power utilization comfort feeling of a user is enhanced, the interactivity is improved, and the electric charge can be saved.
Meanwhile, in the embodiment of the invention, a platform for intuitively knowing the electricity consumption and the environmental conditions of the electric appliance is provided for the user by contacting the user with the electric appliance at home. After the user inputs the subjective feeling of the user, the Web can be visually displayed, so that the user can know the electricity utilization condition at any time and plan the future electricity utilization. Therefore, the behavior of saving electricity of the user can be further promoted, and the national call for energy conservation, emission reduction and low-carbon life can be responded.
While the invention has been described in connection with what is presently considered to be the most practical and preferred embodiment, it is to be understood that the invention is not to be limited to the disclosed embodiment, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
Claims (8)
1. An intelligent household power utilization optimization control system based on a multi-terminal collaborative architecture is characterized by comprising a cloud management and control platform, intelligent household clusters and a Web application server, wherein the intelligent household clusters are communicated with the cloud management and control platform; wherein:
the cloud management and control platform is used for receiving the aggregation models from the intelligent home clusters, calculating the aggregation models by combining time-of-use electricity price information and current environment data after receiving the setting requirements, sent by a user through the Web application server, for the selected intelligent home, obtaining an optimal control scheme for the intelligent home, generating a regulation and control instruction for each intelligent home, and issuing the regulation and control instruction through the intelligent home clusters;
the intelligent home cluster is used for constructing a polymerization model for each intelligent home in the cluster, uploading the polymerization model to the cloud management and control platform, simultaneously responding to a regulation and control instruction sent by the cloud management and control platform on line, and decomposing the regulation and control instruction to each intelligent home in the cluster; the cloud management and control platform is used for collecting current environment data through the environment data collector and uploading the current environment data to the cloud management and control platform;
and the Web application server is used for receiving the setting requirements of the user on various smart homes, uploading the setting requirements to the cloud management and control platform, receiving the optimization control scheme of the cloud management and control platform and displaying the optimization control scheme.
2. The system of claim 1, wherein the smart home comprises: at least one of an air conditioner, a refrigerator, a washing machine, an electric lamp, and a water heater; the environmental data includes: at least one of infrared sensing data, temperature and humidity data, photosensitive data and room position data.
3. The system of claim 2, wherein a Web-side interactive interface is employed for interaction between the user and the Web application server; in the Web-end interactive interface, the input part at least comprises: the system comprises an air conditioner selection item, a use time range input item, a comfort temperature input item, an air conditioner mode selection item and a historical record query item; the display part at least comprises a three-dimensional bar chart of the temperature before and after optimization, a line chart of the electricity consumption used in corresponding time, a pie chart which is 24-hour electricity ranking, and a table of integration time, indoor and outdoor temperature, power consumption and cost.
4. The system of claim 3, wherein in the smart home cluster, the aggregate model of air conditioners is constructed by the following formula:
the discretization operation model of the energy consumption and the temperature in the room is expressed as follows:
wherein T is 1, 2.·, T; t is the total time interval, and delta T is the scheduling time interval;
is the indoor temperature in the jth house at the moment t;
the thermal power consumption in the jth house at the moment t; cMIs thermal mass specific heat capacity; gjIs the thermal mass in the jth house;
the thermal power in the house is limited by the rated thermal power range and the climbing limit of the radiator respectively and jointly by the formulas (4.a) and (4.b), and the indoor temperature is limited by the formula (4. c); finally, the discretization operation model in the jth house in the formula (1) is obtained and is shown in a formula (4. d):
in the formula, COP is the energy efficiency ratio of the corresponding air conditioner;
the heating value (cooling capacity) of the jth air conditioner at the time t;
the heat power of the jth air conditioner at the moment t; in that
The minimum heating power and the maximum heating power of the jth air conditioner are obtained;
is the lowest and highest indoor temperatures acceptable by the user in the jth house;
represents the maximum thermal power ramp rate of the jth house heat dissipation (TCR); suseThe air conditioner is used, 1 is used, and 0 is not used;
is the indoor initial temperature; alpha is alphaj，βj，γjIs the equivalent coefficient parameter of the jth house.
5. An intelligent household power optimization control method based on a multi-terminal collaborative framework is realized by the system of any one of claims 1 to 4, and is characterized by comprising the following steps:
step S10, the Web application server receives setting requirements of various smart homes from users and uploads the requirements to the cloud management and control platform;
step S11, after receiving a setting requirement for a selected smart home sent by a user through a Web application server, a cloud management and control platform calls a pre-stored aggregation model for the smart home, calculates by combining time-of-use electricity price information and current environment data, obtains an optimized control scheme for the smart home and generates a corresponding regulation and control instruction, and issues the regulation and control instruction through a smart home cluster;
step S12, enabling the intelligent home cluster to respond to a regulation and control instruction issued by the cloud management and control platform on line and decompose the regulation and control instruction into the intelligent homes in the cluster;
and step S13, the Web application server receives the optimized control scheme from the cloud management and control platform and displays the optimized control scheme to the user.
6. The method of claim 5, further comprising:
step S20, the intelligent home cluster pre-constructs a polymerization model for each intelligent home in the cluster and uploads the polymerization model to a cloud management and control platform;
and step S21, the intelligent home cluster uploads the real-time environment data collected by the environment data collector to the cloud management and control platform.
7. The method of claim 6, further comprising:
a user interacts with a Web application server through a Web end interactive interface, and specifically, the user performs input operation of an air conditioner selection item, a use time range input item, a comfort temperature input item, an air conditioner mode selection item and a historical record query item in the Web end interactive interface; and displaying the following information on a display portion thereof: the three-dimensional bar chart of the temperature before and after optimization, the line chart of the electricity consumption used at the corresponding time and the pie chart are tables of 24-hour electricity ranking, integration time, indoor and outdoor temperature, power consumption and cost.
8. The method of claim 7, wherein in step S20, the smart home cluster constructs an aggregation model of air conditioners by:
step S200, determining a discretization operation model of energy consumption and temperature in the room as the following formula:
wherein, T is 1, 2.. times.t; t is the total time interval, and delta T is the scheduling time interval;
is the indoor temperature in the jth house at the moment t;
the thermal power consumption in the jth house at the moment t; cMIs thermal mass specific heat capacity; gjIs the thermal mass in the jth house;
step S201, respectively obtaining the heat consumption of the building envelope structure by adopting the following formula
Heat loss by infiltration
And ventilation heat loss
Wherein,
and
heat loss coefficients corresponding to building envelope heat consumption, infiltration heat consumption and ventilation heat consumption are respectively set;
the outdoor ambient temperature at time t can be measured by a temperature sensor;
step S202, respectively adopting formulas (4.a) and (4.b) to limit the thermal power in the house by the rated thermal power range and the climbing limit of the radiator, and adopting formula (4.c) to limit the indoor temperature; finally, the discretization operation model in the jth house in the formula (1) is obtained and is shown in a formula (4. d):
in the formula, COP is the energy efficiency ratio of the corresponding air conditioner;
the heating value (cooling capacity) of the jth air conditioner at the time t;
the heat power of the jth air conditioner at the moment t; in that
The minimum heating power and the maximum heating power of the jth air conditioner are obtained;
is the lowest and highest indoor temperatures acceptable by the user in the jth house;
represents the maximum thermal power ramp rate of the jth house heat dissipation (TCR); suseThe air conditioner is used, 1 is used, and 0 is not used;
is the indoor initial temperature; alpha is alphaj，βj，γjIs the equivalent coefficient parameter of the jth house.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104866250A (en) * | 2014-06-27 | 2015-08-26 | 广东美的环境电器制造有限公司 | Household appliance, control system and method of household appliance, mobile terminal and cloud server |
| CN105549485A (en) * | 2015-12-16 | 2016-05-04 | 华南理工大学 | Household intelligent power utilization control terminal based on DSP |
| CN107451931A (en) * | 2017-07-28 | 2017-12-08 | 河海大学 | The Optimization Scheduling of home intelligent power equipment |
| CN107995263A (en) * | 2017-11-20 | 2018-05-04 | 河海大学 | A kind of multiple terminals Intelligent housing platform based on cloud computing and Raspberry Pi |
| CN111222670A (en) * | 2018-11-27 | 2020-06-02 | 青岛海尔智能技术研发有限公司 | Intelligent household electricity optimization method and device and storage medium |
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Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104866250A (en) * | 2014-06-27 | 2015-08-26 | 广东美的环境电器制造有限公司 | Household appliance, control system and method of household appliance, mobile terminal and cloud server |
| CN105549485A (en) * | 2015-12-16 | 2016-05-04 | 华南理工大学 | Household intelligent power utilization control terminal based on DSP |
| CN107451931A (en) * | 2017-07-28 | 2017-12-08 | 河海大学 | The Optimization Scheduling of home intelligent power equipment |
| CN107995263A (en) * | 2017-11-20 | 2018-05-04 | 河海大学 | A kind of multiple terminals Intelligent housing platform based on cloud computing and Raspberry Pi |
| CN111222670A (en) * | 2018-11-27 | 2020-06-02 | 青岛海尔智能技术研发有限公司 | Intelligent household electricity optimization method and device and storage medium |
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