An energy-saving quantity prediction and energy efficiency optimization method in a simulation environment

Abstract

The present application relates to the technical field of energy-saving optimization, and particularly relates to a method for energy-saving quantity prediction and energy efficiency optimization in a simulation environment, which realizes real-time correction by defining a dynamic simulation-site mismatch factor, comparing simulated cooling capacity with measured cooling capacity, and ensuring that energy-saving prediction is closer to the real operation state on site; designs a thermal inertia-external driving response index to quantize the dynamic response of passenger flow disturbance to cooling capacity demand, thereby explicitly depicting the heat storage effect and control lag of the building and improving the physical rationality of the prediction. The simulation consistency-hydraulic matching index and the thermal inertia response index are fused into core features, and a coupling penalty factor is introduced to form a measurable credibility function, thereby providing a risk boundary for energy-saving prediction. Based on the corrected simulation energy consumption difference, the energy-saving quantity point estimate is calculated in combination with the core features, which not only outputs the instant energy-saving prediction value, but also can be expanded to interval prediction to meet the operation and maintenance decision and energy-saving audit requirements.

Description

Technical Field

[0001] This invention relates to the field of energy-saving optimization technology, and in particular to a method for predicting energy savings and optimizing energy efficiency under a simulation environment. Background Technology

[0002] With the increasing energy consumption of large public buildings, commercial complexes, and rail transit hubs, the energy consumption of central air conditioning systems accounts for a significant proportion of the total building energy consumption. To improve energy efficiency, existing research typically relies on energy consumption simulation platforms (such as EnergyPlus and TRNSYS) to pre-evaluate energy-saving strategies and guide operational optimization accordingly.

[0003] However, existing technologies still have the following prominent problems: There is a mismatch between simulation and on-site conditions. Due to factors such as equipment aging, changes in pipeline resistance, and the thermal inertia of the building envelope, traditional simulation results often deviate significantly from the actual measured load, making it difficult to directly realize energy-saving predictions; The thermal inertia of buildings is not characterized. Existing methods are mostly based on instantaneous load analysis, lacking dynamic modeling of "exogenous drivers (such as changes in passenger flow) → building thermal response → cooling load fluctuations," making it difficult to reflect time delays and heat storage effects; A credibility evaluation mechanism is lacking. Even if the simulated energy consumption difference is obtained, existing solutions cannot provide credibility indicators for the prediction results, resulting in a lack of risk boundaries in energy-saving assessments during actual operation; The optimization strategy and prediction do not form a closed loop. Most current methods only remain at the "prediction layer," lacking the ability to dynamically correct based on prediction credibility and drive control strategies in reverse, resulting in the inability to stably realize energy-saving potential.

[0004] Existing baseline regression, machine learning, or COP metrics all assume that the model matches the field or that the deviation is negligible. They have never embedded "simulation-field mismatch" as a measurable and observable dynamic indicator into the energy-saving prediction link, and have not yet quantified this characteristic of simulation-field mismatch. Summary of the Invention

[0005] The main objective of this invention is to provide a method for predicting energy savings and optimizing energy efficiency under a simulation environment. By defining a dynamic simulation-site mismatch factor, the method compares the simulated cooling capacity with the measured cooling capacity to achieve real-time correction, ensuring that the energy-saving prediction more closely reflects the actual on-site operating conditions. A thermal inertia-exogenous driven response index is designed to quantify the dynamic response of passenger flow disturbances to cooling demand, thereby explicitly characterizing the building's heat storage effect and control lag, improving the physical rationality of the prediction. The simulation consistency-hydraulic matching index and the thermal inertia response index are integrated as core features, and a coupling penalty factor is introduced to form a measurable credibility function, providing a risk boundary for energy-saving prediction. Based on the corrected simulation energy consumption difference, combined with the core features, an energy-saving point estimate is calculated, outputting both immediate energy-saving prediction values ​​and extending to interval predictions to meet the needs of operation and maintenance decision-making and energy-saving auditing.

[0006] The technical solution of the present invention is as follows:

[0007] Firstly, a method for predicting energy savings and optimizing energy efficiency under a simulation environment is proposed. This method includes the following steps:

[0008] S1. Within the preset evaluation window, simultaneously acquire real-time chilled water mass flow rate, real-time chilled water return temperature, real-time chilled water supply temperature, real-time total system power, real-time outdoor wet-bulb temperature, and real-time passenger flow intensity, and acquire the passenger flow intensity change within the evaluation window, while also acquiring the real-time predicted cooling load in the digital twin environment.

[0009] S2. Based on the real-time chilled water mass flow rate, real-time chilled water return temperature, and real-time chilled water supply temperature, the real-time measured cooling load is obtained; the relative energy mismatch rate between the real-time predicted cooling load and the real-time measured cooling load within the evaluation window is calculated to obtain the dynamic simulation-on-site mismatch factor.

[0010] S3. Based on dynamic simulation-on-site mismatch factor, real-time chilled water return temperature and real-time chilled water supply temperature, obtain simulation consistency-hydraulic matching index. Based on real-time measured cooling load and passenger flow intensity change within the evaluation window, obtain thermal inertia-exogenous driven response index. Based on simulation consistency-hydraulic matching index and thermal inertia-exogenous driven response index, calculate coupled energy efficiency consistency index.

[0011] S4. Obtain the difference between the baseline mode energy consumption and the energy consumption of the system in the digital twin environment as the simulation energy consumption difference. Correct the simulation energy consumption difference through dynamic simulation-on-site mismatch factor, and calculate the energy saving point estimate at different times by combining the credibility function. Adjust the energy saving strategy based on the energy saving point estimate.

[0012] A further improvement of the present invention is that the real-time passenger flow intensity in S1 is the ratio of the real-time area population count to the access control throughput.

[0013] A further improvement of the present invention is that step S2 includes the following specific steps:

[0014] S21. Based on the real-time chilled water mass flow rate, real-time chilled water return temperature, and real-time chilled water supply temperature, the real-time measured cooling load is obtained. The calculation formula for the real-time measured cooling load is as follows:

[0015] ;

[0016] in, This represents the measured cooling load at time t. This indicates the density of chilled water. This indicates the specific heat capacity of chilled water. This represents the chilled water mass flow rate at time t. This represents the chilled water return temperature at time t. This indicates the chilled water supply temperature at time t;

[0017] S22. Calculate the relative energy mismatch rate between the real-time predicted cooling load and the real-time measured cooling load within the evaluation window to obtain the dynamic simulation-on-site mismatch factor. The calculation formula for the dynamic simulation-on-site mismatch factor is as follows:

[0018] ;

[0019] in, The simulation-on-site mismatch factor at time t. This represents the predicted cooling load at time t. This represents the integral cumulative value of the measured cooling load at time t within an evaluation window. It represents the cumulative integral difference between the predicted cooling load and the measured cooling load at time t within an evaluation window.

[0020] A further improvement of this invention is that the calculation formula for the simulation consistency-hydraulic matching index in S3 is as follows:

[0021] ;

[0022] in, The simulation consistency-hydraulic matching index at time t is represented. This indicates the design temperature difference.

[0023] A further improvement of this invention is that the calculation formula for the thermal inertia-exogenous driven response index in S3 is as follows:

[0024] ;

[0025] in, The thermal inertia-exogenous driven response exponent at time t is represented. This indicates the duration of the evaluation window, with a value of 5 minutes. This represents the measured change in cooling load within the evaluation window period. This indicates the change in passenger flow intensity within the evaluation window period. Indicates the calibration coefficient. The disturbance coefficient is 0.0001.

[0026] A further improvement of this invention is that the calculation formula for the coupling energy efficiency consistency index in S3 is as follows:

[0027] ;

[0028] in, Representing time t, , These are the weighting factors, , This is the coupling penalty factor.

[0029] A further improvement of this invention is that the calculation method for the energy-saving point estimate in S4 is as follows:

[0030] ;

[0031] in, This represents the estimated nodal energy point value at time t. This represents the difference in simulated energy consumption at time t. The difference in simulated energy consumption after mismatch correction at time t is represented.

[0032] Secondly, a computer-readable storage medium is proposed, on which a computer program is stored. When the computer program is executed by a processor, it implements the above-mentioned method for energy saving prediction and energy efficiency optimization in a simulation environment.

[0033] Thirdly, an electronic device is proposed, including a memory for storing instructions and a processor for executing the instructions, causing the device to perform the above-described method for energy saving prediction and energy efficiency optimization in a simulation environment.

[0034] The technical effects of this invention are as follows:

[0035] A method for predicting energy savings and optimizing energy efficiency under a simulation environment is constructed. By defining a dynamic simulation-site mismatch factor, the simulated cooling capacity is compared with the measured cooling capacity to achieve real-time correction, ensuring that the energy-saving prediction is closer to the actual on-site operating conditions. A thermal inertia-exogenous driven response index is designed to quantify the dynamic response of passenger flow disturbances to cooling demand, thereby explicitly characterizing the building's heat storage effect and control lag, improving the physical rationality of the prediction. The simulation consistency-hydraulic matching index and the thermal inertia response index are integrated as core features, and a coupling penalty factor is introduced to form a measurable credibility function, providing a risk boundary for energy-saving prediction. Based on the corrected simulation energy consumption difference, combined with the core features, the estimated energy savings points are calculated, outputting both immediate energy-saving prediction values ​​and extending to interval predictions to meet the needs of operation and maintenance decision-making and energy-saving auditing. Attached Figure Description

[0036] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0037] Figure 1This is a flowchart illustrating a method for predicting energy savings and optimizing energy efficiency under a simulation environment, as described in Embodiment 1 of the present invention. Detailed Implementation

[0038] Example 1

[0039] This embodiment proposes a method for energy-saving prediction and energy efficiency optimization under a simulation environment. By defining a dynamic simulation-site mismatch factor, it compares the simulated predicted cooling capacity with the measured cooling capacity to achieve real-time correction, ensuring that the energy-saving prediction is closer to the actual on-site operating conditions. A thermal inertia-exogenous driven response index is designed to quantify the dynamic response of passenger flow disturbances to cooling demand, thereby explicitly characterizing the building's heat storage effect and control lag, improving the physical rationality of the prediction. The simulation consistency-hydraulic matching index and the thermal inertia response index are integrated as core features, and a coupling penalty factor is introduced to form a measurable credibility function, providing a risk boundary for energy-saving prediction. Based on the corrected simulation energy consumption difference, combined with the core features, the estimated energy-saving points are calculated, outputting both immediate energy-saving prediction values ​​and extending to interval predictions to meet the needs of operation and maintenance decision-making and energy-saving auditing. Specifically, such as... Figure 1 As shown in the figure, the energy-saving prediction and energy efficiency optimization method under a simulation environment proposed in this embodiment includes the following specific steps:

[0040] S1. Within the preset evaluation window, simultaneously acquire real-time chilled water mass flow rate, real-time chilled water return temperature, real-time chilled water supply temperature, real-time total system power, real-time outdoor wet-bulb temperature, and real-time passenger flow intensity, and acquire the passenger flow intensity change within the evaluation window, while also acquiring the real-time predicted cooling load in the digital twin environment.

[0041] S2. Based on the real-time chilled water mass flow rate, real-time chilled water return temperature, and real-time chilled water supply temperature, the real-time measured cooling load is obtained; the relative energy mismatch rate between the real-time predicted cooling load and the real-time measured cooling load within the evaluation window is calculated to obtain the dynamic simulation-on-site mismatch factor.

[0042] S3. Based on dynamic simulation-on-site mismatch factor, real-time chilled water return temperature and real-time chilled water supply temperature, obtain simulation consistency-hydraulic matching index. Based on real-time measured cooling load and passenger flow intensity change within the evaluation window, obtain thermal inertia-exogenous driven response index. Based on simulation consistency-hydraulic matching index and thermal inertia-exogenous driven response index, calculate coupled energy efficiency consistency index.

[0043] S4. Obtain the difference between the baseline mode energy consumption and the energy consumption of the system in the digital twin environment as the simulation energy consumption difference. Correct the simulation energy consumption difference through dynamic simulation-on-site mismatch factor, and calculate the energy saving point estimate at different times by combining the credibility function. Adjust the energy saving strategy based on the energy saving point estimate.

[0044] In this embodiment, the real-time passenger flow intensity in S1 is the ratio of the real-time area population count to the number of people passing through the access control system.

[0045] In this embodiment, step S2 includes the following specific steps:

[0046] S21. Based on the real-time chilled water mass flow rate, real-time chilled water return temperature, and real-time chilled water supply temperature, the real-time measured cooling load is obtained. The calculation formula for the real-time measured cooling load is as follows:

[0047] ;

[0048] in, This represents the measured cooling load at time t. This indicates the density of chilled water. This indicates the specific heat capacity of chilled water. This represents the chilled water mass flow rate at time t. This represents the chilled water return temperature at time t. This indicates the chilled water supply temperature at time t;

[0049] S22. Calculate the relative energy mismatch rate between the real-time predicted cooling load and the real-time measured cooling load within the evaluation window to obtain the dynamic simulation-on-site mismatch factor. The calculation formula for the dynamic simulation-on-site mismatch factor is as follows:

[0050] ;

[0051] in, The simulation-on-site mismatch factor at time t. This represents the predicted cooling load at time t. This represents the integral cumulative value of the measured cooling load at time t within an evaluation window. It represents the cumulative integral difference between the predicted cooling load and the measured cooling load at time t within an evaluation window.

[0052] In this embodiment, the calculation formula for the simulation consistency-hydraulic matching index in S3 is as follows:

[0053] ;

[0054] in, The simulation consistency-hydraulic matching index at time t is represented. This indicates the design temperature difference.

[0055] In this embodiment, the formula for calculating the thermal inertia-exogenous driven response index in S3 is as follows:

[0056] ;

[0057] in, The thermal inertia-exogenous driven response exponent at time t is represented. This indicates the duration of the evaluation window, with a value of 5 minutes. This represents the measured change in cooling load within the evaluation window period. This indicates the change in passenger flow intensity within the evaluation window period. Indicates the calibration coefficient. The disturbance coefficient is 0.0001.

[0058] In this embodiment, the calculation formula for the coupling energy efficiency consistency index in S3 is as follows:

[0059] ;

[0060] in, Representing time t, , These are the weighting factors, , This is the coupling penalty factor.

[0061] In this embodiment, the calculation method for the estimated value of the nodal energy point in S4 is as follows:

[0062] ;

[0063] in, This represents the estimated nodal energy point value at time t. This represents the difference in simulated energy consumption at time t. The difference in simulated energy consumption after mismatch correction at time t is represented.

[0064] The threshold and weight settings can be based on the default settings of this invention, or they can be set by the operator.

[0065] Example 2

[0066] This embodiment provides an electronic device, including a processor and a memory, wherein the memory stores a computer program that can be called by the processor; the processor executes the above-described method for energy saving prediction and energy efficiency optimization under a simulation environment by calling the computer program stored in the memory.

[0067] The electronic device can vary considerably depending on its configuration or performance. It may include one or more Central Processing Units (CPUs) and one or more memories, wherein the memory stores at least one computer program, which is loaded and executed by the processor to implement the energy-saving prediction and energy efficiency optimization method in a simulation environment provided in the above-described embodiment. The electronic device may also include other components for implementing its functions; for example, it may have wired or wireless network interfaces and input / output interfaces for data input and output. Details will not be elaborated upon in this embodiment.

[0068] Those skilled in the art will recognize that this invention can be implemented as a system, method, or computer program product. Therefore, this disclosure can be embodied in the following forms: it can be entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software, generally referred to herein as a "circuit," "module," or "system." Furthermore, in some embodiments, the invention can also be implemented as a computer program product contained in one or more computer-readable media, which includes computer-readable program code.

[0069] Any combination of one or more computer-readable media may be used. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device.

[0070] This invention is described with reference to flowchart illustrations and block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and block diagrams, as well as combinations of blocks in the flowchart illustrations and block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart. Figure 1 One or more processes and boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0071] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and boxes Figure 1 The steps of the function specified in one or more boxes.

[0072] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A method for predicting energy savings and optimizing energy efficiency under a simulation environment, characterized in that: The specific steps include the following: S1. Within the preset evaluation window, simultaneously acquire real-time chilled water mass flow rate, real-time chilled water return temperature, real-time chilled water supply temperature, real-time total system power, real-time outdoor wet-bulb temperature, and real-time passenger flow intensity, and acquire the passenger flow intensity change within the evaluation window, while also acquiring the real-time predicted cooling load in the digital twin environment. S2. Based on the real-time chilled water mass flow rate, real-time chilled water return temperature, and real-time chilled water supply temperature, the real-time measured cooling load is obtained. Calculate the relative energy mismatch rate between the real-time predicted cooling load and the real-time measured cooling load within the evaluation window to obtain the dynamic simulation-on-site mismatch factor. S3. Based on dynamic simulation-on-site mismatch factor, real-time chilled water return temperature and real-time chilled water supply temperature, obtain simulation consistency-hydraulic matching index. Based on real-time measured cooling load and passenger flow intensity change within the evaluation window, obtain thermal inertia-exogenous driven response index. Based on simulation consistency-hydraulic matching index and thermal inertia-exogenous driven response index, calculate coupled energy efficiency consistency index. S4. Obtain the difference between the baseline mode energy consumption and the energy consumption of the system in the digital twin environment as the simulation energy consumption difference. Correct the simulation energy consumption difference through dynamic simulation-on-site mismatch factor, and calculate the energy saving point estimate at different times by combining the credibility function. Adjust the energy saving strategy based on the energy saving point estimate. S21. Based on the real-time chilled water mass flow rate, real-time chilled water return temperature, and real-time chilled water supply temperature, the real-time measured cooling load is obtained. The calculation formula for the real-time measured cooling load is as follows: ； in, This represents the measured cooling load at time t. This indicates the density of chilled water. This indicates the specific heat capacity of chilled water. This represents the chilled water mass flow rate at time t. This represents the chilled water return temperature at time t. This indicates the chilled water supply temperature at time t; S22. Calculate the relative energy mismatch rate between the real-time predicted cooling load and the real-time measured cooling load within the evaluation window to obtain the dynamic simulation-on-site mismatch factor. The calculation formula for the dynamic simulation-on-site mismatch factor is as follows: ； in, The simulation-on-site mismatch factor at time t. This represents the predicted cooling load at time t. This represents the integral cumulative value of the measured cooling load at time t within an evaluation window. This represents the cumulative integral difference between the predicted cooling load and the measured cooling load at time t within an evaluation window. The formula for calculating the simulation consistency-hydraulic matching index in S3 is as follows: ； in, The simulation consistency-hydraulic matching index at time t is represented. Indicates the design temperature difference; The formula for calculating the thermal inertia-exogenous driven response index in S3 is as follows: ； in, The thermal inertia-exogenous driven response exponent at time t is represented. This indicates the duration of the evaluation window, with a value of 5 minutes. This represents the measured change in cooling load within the evaluation window period. This indicates the change in passenger flow intensity within the evaluation window period. Indicates the calibration coefficient. This is the disturbance coefficient, with a value of 0.0001; The formula for calculating the coupling energy efficiency consistency index in S3 is as follows: ； in, This represents the coupling energy efficiency consistency index at time t. , These are the weighting factors, , This is the coupling penalty factor; The method for calculating the estimated value of the nodal energy point in S4 is as follows: ； in, This represents the estimated nodal energy point value at time t. This represents the difference in simulated energy consumption at time t. The difference in simulated energy consumption after mismatch correction at time t is represented.

2. The method for predicting energy savings and optimizing energy efficiency in a simulated environment according to claim 1, characterized in that: The real-time passenger flow intensity in S1 is the ratio of the real-time area population count to the number of people passing through the access control system.

3. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the energy-saving prediction and energy efficiency optimization method in a simulation environment as described in any one of claims 1-2.

4. An electronic device, characterized in that, It includes a memory for storing instructions; and a processor for executing the instructions, causing the device to perform an energy-saving prediction and energy efficiency optimization method in a simulation environment as described in any one of claims 1 to 2.

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