CN118192300B - Simulation platform-based energy data management system and method - Google Patents
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Abstract
The invention discloses an energy data management system and method based on a simulation platform, and belongs to the technical field of energy management, wherein the system comprises a data acquisition module, an energy analysis and optimization module, a simulation prediction module, an automatic control module and a data visualization module, wherein the data acquisition module is responsible for acquiring real-time energy data from various data sources; the energy analysis optimizing module is responsible for analyzing the acquired energy data and adjusting the operation parameters of the equipment according to the analysis result, the simulation predicting module is responsible for establishing a simulation model of an energy system based on the acquired energy data so as to predict the energy consumption condition, the automatic control module is responsible for optimizing and regulating the energy consumption through controlling the operation state and the parameters of the equipment, and the data visualization module is responsible for carrying out visual display on the acquired data and the analysis result so as to provide a user-friendly interface and realize man-machine interaction.
Description
Technical Field
The invention relates to the technical field of energy management, in particular to an energy data management system and method based on a simulation platform.
Background
The energy source refers to substances or energy forms which exist in the natural world and can be converted and used for various activities such as production, transportation, illumination, heat supply, driving machinery and the like, the energy source is an important substance foundation for supporting the development and survival of the human society, the energy source exists in various forms including but not limited to fossil energy, nuclear energy, water energy, wind energy, solar energy, biological energy and the like, the energy source is widely used in various fields of production and life in the development process of the human society, such as industrial production, transportation, home heating, electric power production and the like, the utilization of the energy source directly influences the development of the social economy and the sustainability of the environment, and therefore, the efficient utilization of the energy source and the development and the utilization of renewable energy source become one of important issues of the current society and environmental protection.
In the whole life cycle link of traditional refrigeration machine room construction, the operation parameter adjustment of refrigeration equipment generally relies on manual intervention or fixed time interval, lacks instantaneity and individuality to can't carry out accurate prediction and adjustment to the energy consumption, have the extravagant problem of energy.
Accordingly, there is a need for a more advanced and integrated system for energy data management to solve these problems, and the present invention is directed to a simulation platform-based energy data management system and method that provides a new and more efficient solution.
Disclosure of Invention
The invention aims to provide an energy data management system and method scheme based on a simulation platform, so as to solve the problems in the background technology.
In order to achieve the aim, the invention adopts the following technical scheme that the energy data management system based on the simulation platform comprises a data acquisition module, an energy analysis optimization module, a simulation prediction module, an automatic control module and a data visualization module;
the system comprises a data acquisition module, an energy analysis optimizing module, an automatic control module, a data visualization module and a user interactive graphical interface, wherein the data acquisition module is used for acquiring operation data and environment data of different equipment from a refrigeration machine room and transmitting the acquired data to the energy analysis optimizing module and the simulation predicting module, the energy analysis optimizing module is used for analyzing the acquired energy data, outputting an instruction for adjusting the operation parameters of the equipment according to an analysis result and transmitting instruction information to the automatic control module, the simulation predicting module is used for establishing a simulation model of an energy system based on the acquired energy data so as to predict energy consumption conditions and transmitting the predicted energy consumption conditions to the data visualization module, and the automatic control module is used for optimizing and regulating the refrigeration machine room equipment by controlling the operation state and the parameters of the equipment and for visually displaying the acquired energy data and the analysis result thereof and simultaneously providing the acquired energy data and the analysis result thereof to the user interactive graphical interface.
The data acquisition module comprises an equipment data acquisition unit and an environment data acquisition unit;
The equipment data acquisition unit is used for acquiring output power, working efficiency and running time of different equipment, and the environment data acquisition unit is responsible for acquiring temperature data in a refrigerating machine room and temperature data of chilled water demand.
The equipment data acquisition unit is connected to various refrigeration equipment to acquire key parameters such as output power, working efficiency and running time in real time. These data are critical to understanding the performance and operating conditions of the device. By monitoring and analyzing the equipment data, the system can discover equipment faults in time, optimize equipment operation parameters to improve efficiency, and can make a more effective maintenance plan to prolong the service life of the equipment. The environment data acquisition unit is responsible for acquiring environment data of the refrigerating machine room, including temperature data and demand temperature data of chilled water. The system can adjust the operation parameters of the refrigeration equipment according to the actual demand by monitoring and analyzing the environmental data in real time so as to keep the temperature in the refrigeration machine room stable and ensure that the demand temperature of the chilled water is satisfied. In addition, the environmental data may also be used to predict future energy consumption, helping to formulate more efficient energy management strategies.
The energy analysis optimizing module comprises a data cleaning and processing unit, an energy data analyzing unit and an operation parameter adjusting unit;
the data cleaning and processing unit is used for cleaning and processing the collected temperature data in the refrigerating machine room, the energy data analysis unit is used for analyzing the collected energy data and identifying the mode and trend of energy consumption based on a time sequence prediction algorithm, and the operation parameter adjustment unit is used for adjusting the operation parameters of the refrigerating machine room equipment in real time according to the result of the energy data analysis.
The simulation prediction module comprises an energy simulation modeling unit and an energy consumption prediction unit;
the energy simulation modeling unit is used for establishing a simulation model of an energy system based on the acquired energy data, and comprises equipment and a system topological structure; the energy consumption prediction unit is used for utilizing the established simulation model and predicting the energy consumption condition.
The energy simulation modeling unit is used for constructing a simulation model of the energy system by utilizing an advanced modeling technology based on the acquired real-time energy data. These models cover the topology, operating parameters and correlations among various energy devices and systems. Through simulation, a user can comprehensively understand the operation characteristics of the energy system, including interaction among devices, an energy transmission process, stability of the system and the like, so that important references are provided for subsequent energy optimization and decision. The energy consumption prediction unit predicts the energy consumption in a future period by using the established simulation model and combining the historical energy data and the future prediction information. Through simulation of the energy system, the prediction unit can simulate the energy consumption conditions under different scenes, predicts the change trend of the future energy demand and the magnitude of the energy consumption according to the system state, external environment factors, user demands and other factors, and helps users to make corresponding energy management decisions, including resource configuration, scheduling arrangement, energy saving optimization strategies and the like.
The automatic control module comprises an equipment operation control unit and an automatic decision unit;
the automatic decision unit is used for acquiring an energy consumption prediction result in real time, and automatically ending the work of the refrigeration machine room equipment if the energy consumption prediction result is greater than the preset standard energy consumption.
The data visualization module comprises a data display unit and a user interaction interface unit;
the data display unit is used for displaying the collected energy data, analysis results and prediction information, and the user interaction interface unit is used for carrying out man-machine interaction so that a user can inquire, analyze and operate the energy data.
The data display unit is an important component for displaying the energy data, the analysis result and the prediction information acquired from the data acquisition module to a user in a visual form such as a graph, a chart and the like. Through various visualization modes, such as a line graph, a bar graph, a pie chart and the like, a user can intuitively understand the energy use condition, trend change and the performance of key indexes, so that the condition of energy consumption and the potential of optimization are better understood. The user interaction interface unit is a window for the user to interact with the system, and the user can inquire, analyze and operate the energy data through various operations and instructions on the interface, so that personalized energy management and optimization are realized. The interface is generally provided with a friendly graphical interface and an intuitive operation mode, so that a user can easily browse data, adjust parameters, generate reports and the like, and thus the user can participate in the energy management process better.
An energy data management method based on a simulation platform, the energy data management method comprises the following steps:
s1, collecting equipment data and environment data in a refrigerating machine room, and cleaning and processing the collected environment data;
S2, calculating adjustment parameters based on the environment data, and automatically adjusting output power parameters of the equipment based on the adjustment parameters and the equipment data;
S3, in the simulation platform, predicting the future energy consumption condition of the refrigerating machine room according to the adjusted output power parameters;
and S4, visually displaying the acquired energy data and the analysis result thereof.
In step S1, the collecting device data and environment data in the refrigeration room adopts the following manners:
firstly, determining the starting time of equipment to be acquired in a refrigeration machine room, acquiring equipment data in real time based on the starting time of the equipment to be acquired, acquiring the equipment data from monitoring equipment in real time, and acquiring environmental data from local data storage equipment in real time;
the data cleaning and processing of the collected environmental data comprises the data cleaning and processing of temperature data in a refrigerating machine room, wherein the data cleaning is performed by using Cheng Cai as a dynamic interval interpolation method:
Let the temperature data set collected in the refrigeration room be { x 1,x2,…,xn }, where x 1 represents the first temperature data in the temperature data set in the refrigeration room, x n represents the nth temperature data in the temperature data set in the refrigeration room, for the ith data missing value x i, i=1, 2,..n, a dynamic interval is first defined [ L, R ], where L represents the number of steps of searching to the left, R represents the number of steps of searching to the right, searching for the nearest two non-data missing values x j and x u in the interval [ L, R ], where j < i < u, the calculation formula for calculating the missing value x i according to the non-data missing values x j and x u is as follows:
And finally, filling the missing value with data to obtain a temperature data set in the cleaned refrigerating machine room.
In step S2, the adjustment parameter is calculated based on the temperature data in the following calculation manner:
Firstly, the output power of the current refrigeration equipment is P cu, the temperature of chilled water actually required in a refrigeration machine room is T in, the temperature of the refrigeration machine room is P out, and the calculation formula of the adjustment parameter k is as follows:
wherein, the adjusting parameter k is a parameter for adjusting the output power of the energy supply equipment, e is a natural constant, alpha is a control parameter for adjusting the influence degree of temperature difference change on the adjusting parameter k, and alpha is E [0,1];
The output power parameter P out of the automation control device based on the adjustment parameter and the device data is calculated by adopting the following formula:
Pout＝Pcu×(1+k)。
In step S3, in the simulation platform, the predicted energy consumption condition according to the adjusted output power data is calculated by using the following formula:
wherein E is the total energy consumption of the equipment in the refrigeration machine room, b is the number of the equipment in the refrigeration machine room, N a is the working efficiency of the equipment at the a-th station, P out_a is the output power data adjusted by the equipment at the a-th station, deltat a is the running time of the equipment at the a-th station, eta a is the energy consumption coefficient of the equipment at the unit output power;
In step S4, the visual display of the collected energy data and analysis results includes displaying the environmental data and the energy prediction situation in the refrigeration process, where the environmental data is represented in a graph form and the energy prediction situation is represented in a statistical graph form.
Compared with the prior art, the invention has the following beneficial effects:
1. The system adjusts the operation parameters of the equipment according to the analysis result, and realizes the intelligent optimization of energy consumption. Compared with the traditional static energy management method, the system can dynamically adjust the operation parameters of the equipment according to the real-time data and the analysis result, and improves the energy utilization efficiency;
2. The simulation prediction module can establish a simulation model of the energy system based on the collected energy data and predict the energy consumption condition in a future period of time. The energy manager can make adjustment and optimization in advance, and the situation that energy supply and demand are not matched is avoided;
3. The automatic control module can realize the optimization and regulation of energy consumption by controlling the running state and parameters of the equipment. Compared with the traditional manual control method, the system can realize more accurate and timely energy control, and improves the efficiency and accuracy of energy management;
4. The data visualization module can perform visual display on the collected data and analysis results, and a user-friendly interface is provided for realizing man-machine interaction. This enables the energy manager to more intuitively understand the energy usage and analysis results, thereby better making energy management policies and decisions.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a schematic diagram of the energy data management system module based on the simulation platform;
FIG. 2 is a schematic flow chart of steps of an energy data management method based on a simulation platform.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1-2, the present invention provides the following technical solutions:
An energy data management system based on a simulation platform comprises a data acquisition module, an energy analysis optimization module, a simulation prediction module, an automation control module and a data visualization module;
The data acquisition module is responsible for acquiring operation data and environment data of different equipment from the refrigeration machine room and transmitting the acquired data to the energy analysis optimizing module and the simulation predicting module, the energy analysis optimizing module is responsible for analyzing the acquired energy data, outputting an instruction for adjusting the operation parameters of the equipment according to an analysis result and transmitting instruction information to the automatic control module, the simulation predicting module is responsible for establishing a simulation model of an energy system based on the acquired energy data so as to predict the energy consumption condition and transmitting the predicted energy consumption condition to the data visualization module, the automatic control module is responsible for optimizing and regulating the refrigeration machine room equipment through controlling the operation state and the parameters of the equipment, and the data visualization module is responsible for visually displaying the acquired energy data and the analysis result thereof and simultaneously providing the acquired energy data and the analysis result thereof to a user interactive graphical interface.
The data acquisition module comprises an equipment data acquisition unit and an environment data acquisition unit;
the equipment data acquisition unit is used for acquiring output power, working efficiency and running time of different equipment, and the environment data acquisition unit is responsible for acquiring temperature data in the refrigerating machine room and the demand temperature data of chilled water.
The equipment data acquisition unit is connected to various refrigeration equipment to acquire key parameters such as output power, working efficiency and running time in real time. These data are critical to understanding the performance and operating conditions of the device. By monitoring and analyzing the equipment data, the system can discover equipment faults in time, optimize equipment operation parameters to improve efficiency, and can make a more effective maintenance plan to prolong the service life of the equipment. The environment data acquisition unit is responsible for acquiring environment data of the refrigerating machine room, including temperature data and demand temperature data of chilled water. The system can adjust the operation parameters of the refrigeration equipment according to the actual demand by monitoring and analyzing the environmental data in real time so as to keep the temperature in the refrigeration machine room stable and ensure that the demand temperature of the chilled water is satisfied. In addition, the environmental data may also be used to predict future energy demands, helping to formulate more efficient energy management strategies.
The energy analysis optimizing module comprises a data cleaning and processing unit, an energy data analyzing unit and an operation parameter adjusting unit;
The system comprises a data cleaning and processing unit, an energy data analysis unit and an operation parameter adjustment unit, wherein the data cleaning and processing unit is used for cleaning and processing the collected temperature data in the refrigerating machine room, the energy data analysis unit is used for analyzing the collected energy data and identifying the mode and trend of energy consumption based on a time sequence prediction algorithm, and the operation parameter adjustment unit is used for adjusting the operation parameters of the refrigerating machine room equipment in real time according to the result of the energy data analysis.
The simulation prediction module comprises an energy simulation modeling unit and an energy consumption prediction unit;
the energy simulation modeling unit is used for establishing a simulation model of an energy system based on the acquired energy data, and comprises equipment and a system topological structure; the energy consumption prediction unit is used for utilizing the established simulation model and predicting the energy consumption condition.
The energy simulation modeling unit is used for constructing a simulation model of the energy system by utilizing an advanced modeling technology based on the acquired real-time energy data. These models cover the topology, operating parameters and correlations among various energy devices and systems. Through simulation, a user can comprehensively understand the operation characteristics of the energy system, including interaction among devices, an energy transmission process, stability of the system and the like, so that important references are provided for subsequent energy optimization and decision. The energy consumption prediction unit predicts the energy consumption in a future period by using the established simulation model and combining the historical energy data and the future prediction information. Through simulation of the energy system, the prediction unit can simulate the energy consumption conditions under different scenes, predicts the change trend of the future energy demand and the magnitude of the energy consumption according to the system state, external environment factors, user demands and other factors, and helps users to make corresponding energy management decisions, including resource configuration, scheduling arrangement, energy saving optimization strategies and the like.
The automatic control module comprises an equipment operation control unit and an automatic decision unit;
The automatic decision unit is used for acquiring an energy consumption prediction result in real time, and automatically ending the work of the refrigeration machine room equipment if the energy consumption prediction result is greater than the preset standard energy consumption.
The data visualization module comprises a data display unit and a user interaction interface unit;
the data display unit is used for displaying the collected energy data, analysis results and prediction information, and the user interaction interface unit is used for carrying out man-machine interaction so that a user can inquire, analyze and operate the energy data.
The data display unit is an important component for displaying the energy data, the analysis result and the prediction information acquired from the data acquisition module to a user in a visual form such as a graph, a chart and the like. Through various visualization modes, such as a line graph, a bar graph, a pie chart and the like, a user can intuitively understand the energy use condition, trend change and the performance of key indexes, so that the condition of energy consumption and the potential of optimization are better understood. The user interaction interface unit is a window for the user to interact with the system, and the user can inquire, analyze and operate the energy data through various operations and instructions on the interface, so that personalized energy management and optimization are realized. The interface is generally provided with a friendly graphical interface and an intuitive operation mode, so that a user can easily browse data, adjust parameters, generate reports and the like, and thus the user can participate in the energy management process better.
An energy data management method based on a simulation platform comprises the following steps:
s1, collecting equipment data and environment data in a refrigerating machine room, and cleaning and processing the collected environment data;
S2, calculating adjustment parameters based on the environment data, and automatically adjusting output power parameters of the equipment based on the adjustment parameters and the equipment data;
S3, in the simulation platform, predicting the future energy consumption condition of the refrigerating machine room according to the adjusted output power parameters;
and S4, visually displaying the acquired energy data and the analysis result thereof.
In step S1, the following manner is adopted for collecting the equipment data and the environmental data in the refrigeration machine room:
firstly, determining the starting time of equipment to be acquired in a refrigeration machine room, acquiring equipment data in real time based on the starting time of the equipment to be acquired, acquiring the equipment data from monitoring equipment in real time, and acquiring environmental data from local data storage equipment in real time;
The data cleaning and processing of the collected environmental data comprises the data cleaning and processing of temperature data in a refrigerating machine room, wherein the data cleaning is performed by using the following dynamic interval interpolation method of Cheng Cai:
Let the temperature data set collected in the refrigeration room be { x 1,x2,…,xn }, where x 1 represents the first temperature data in the temperature data set in the refrigeration room, x n represents the nth temperature data in the temperature data set in the refrigeration room, for the ith data missing value x i, i=1, 2,..n, a dynamic interval is first defined [ L, R ], where L represents the number of steps of searching to the left, R represents the number of steps of searching to the right, searching for the nearest two non-data missing values x j and x u in the interval [ L, R ], where j < i < u, the calculation formula for calculating the missing value x i according to the non-data missing values x j and x u is as follows:
And finally, filling the missing value with data to obtain a temperature data set in the cleaned refrigerating machine room.
In step S2, the adjustment parameters are calculated based on the temperature data in the following calculation manner:
firstly, the output power of the current refrigeration equipment is P cu, the temperature of chilled water actually required in a refrigeration machine room is T in, the temperature of the refrigeration machine room is T out, and the calculation formula of the adjustment parameter k is as follows:
wherein, the adjusting parameter k is a parameter for adjusting the output power of the energy supply equipment, e is a natural constant, alpha is a control parameter for adjusting the influence degree of temperature difference change on the adjusting parameter k, and alpha is E [0,1];
The output power parameter P out of the automation control device based on the adjustment parameters and the device data is calculated using the following formula:
Pout＝Pcu×(1+k)。
In step S3, in the simulation platform, the predicted energy consumption condition according to the adjusted output power data is calculated using the following formula:
wherein E is the total energy consumption of the equipment in the refrigeration machine room, b is the number of the equipment in the refrigeration machine room, N a is the working efficiency of the equipment at the a-th station, P out_a is the output power data adjusted by the equipment at the a-th station, deltat a is the running time of the equipment at the a-th station, eta a is the energy consumption coefficient of the equipment at the unit output power;
In step S4, the collected data and the analysis result are visually displayed, including displaying the environmental data and the energy prediction situation in the refrigeration process, where the environmental data is represented in a graph form and the energy prediction situation is represented in a statistical graph form.
Embodiment one:
The dynamic interval is defined as [3,3], namely 3 data are searched leftwards and 3 data are searched rightwards, and the following temperature data sets {20,25, missing, 30, missing, 40,45} in the refrigerating machine room are obtained:
For the first missing value x 3, searching the two nearest non-missing values in the dynamic interval, namely x 2 =25 and x 4 =30, substituting the values into the dynamic interval interpolation formula to obtain:
For the second missing value x 5, searching the two nearest non-missing values in the dynamic interval, namely x 4 =30 and x 6 =40, substituting the values into the dynamic interval interpolation formula to obtain:
Thus, the new temperature data set obtained by the dynamic interval interpolation method is {20,25,27.5,30,35,40,45}.
Let the output power P cu =100 kW of the current refrigeration plant, the chilled water temperature T in =5 ℃ actually required in the refrigeration room, the refrigeration room temperature T out =25 ℃, the control parameter α=0.5, the number of devices being 2, wherein the working efficiency η 1 =0.9 of the device 1, the energy consumption coefficient N 1 =0.8 per unit output power, the operating time Δt 1 =10 hours, the working efficiency η 2 =0.85 of the device 2, the energy consumption coefficient N 2 =0.75 per unit output power, the operating time Δt 2 =8 hours, firstly, the adjustment parameter k is calculated:
Then, the output power parameter P out of the computing device:
Pout＝Pcu×(1+k)≈100.0045;
finally, the energy consumption situation is predicted based on the obtained output power data:
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be noted that the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited to the foregoing embodiments, but may be modified or substituted for some of the features described in the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
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