An integrated energy station energy consumption management optimization system and storage medium

Abstract

The application discloses a kind of comprehensive energy station energy consumption management optimization system and storage medium, it is related to energy consumption management technical field, including monitoring module, management platform, the monitoring module includes multiple input monitoring unit, multiple output monitoring unit and influence factor monitoring unit;Each input monitoring unit is respectively used to monitor the energy input of corresponding energy consumption equipment, and generates the energy consumption data of corresponding energy consumption equipment;Each output monitoring unit is respectively used to monitor the energy output of corresponding energy consumption equipment, and generates the energy generation data of corresponding energy consumption equipment;The influence factor monitoring unit monitors the environmental factor change of the region where each energy consumption equipment is located, and generates corresponding influence factor data.The application is by associating environmental factor with energy input and energy generation data, quantifies the influence degree of each environmental factor, and is optimized on this basis, to realize the real-time regulation of each energy consumption equipment when relevant environmental factor changes.

Description

Technical Field

[0001] This invention relates to the field of energy management technology, specifically to an integrated energy station energy management optimization system and storage medium. Background Technology

[0002] With the widespread application of integrated energy stations, the efficient management and optimization of various energy-consuming equipment has become an important research direction for improving energy utilization and reducing energy waste. Currently, integrated energy stations mainly rely on energy consumption monitoring systems and production output monitoring systems to monitor the energy input and production capacity of energy-consuming equipment in real time. However, existing technologies typically focus on the static relationship analysis between energy consumption data and production capacity generation data, failing to fully consider the time delays and environmental factors that exist during dynamic operation.

[0003] A search revealed a Chinese patent (CN116993232A) that discloses an energy consumption management optimization method and system for an integrated energy station. This patent includes acquiring multiple air conditioning control subsystems within a central air conditioning energy station. Each subsystem corresponds to a control loop and is bidirectionally connected to a data acquisition card. It collects status data, outputs an energy consumption status dataset, stores it in a data center, compares energy consumption data, obtains matching indicators for every two air conditioning control subsystems, classifies and outputs multiple subsystems, matches them with units in an energy consumption processing module, establishes multiple data transmission channels, performs optimization processing, generates energy consumption control parameters, and manages the central air conditioning energy station.

[0004] In the existing technology, energy-consuming equipment has a certain time delay in actual operation. The existing technology is poor at identifying and quantifying the time delay, making it difficult to accurately assess the dynamic response relationship between energy input and production output. Furthermore, when environmental factors affect energy-consuming equipment, if they cannot be predicted and controlled in advance, it can easily lead to energy waste or insufficient output. Therefore, the present invention provides an integrated energy station energy consumption management optimization system and storage medium. Summary of the Invention

[0005] The purpose of this invention is to provide an integrated energy station energy consumption management and optimization system and storage medium to solve the problems mentioned in the background art.

[0006] This invention can be achieved through the following technical solution: an integrated energy station energy consumption management and optimization system, including a monitoring module and a management platform;

[0007] The monitoring module includes multiple input monitoring units, multiple output monitoring units, and influencing factor monitoring units. Each input monitoring unit and each output monitoring unit is matched with a set of energy-consuming devices.

[0008] Each input monitoring unit is used to monitor the energy input of the corresponding energy-consuming device and generate energy consumption data for the corresponding energy-consuming device;

[0009] Each output monitoring unit is used to monitor the output capacity of the corresponding energy-consuming equipment and generate the corresponding energy-consuming equipment's output capacity data.

[0010] The influencing factor monitoring unit is used to monitor changes in environmental factors in the area where each energy-consuming device is located and generate corresponding influencing factor data;

[0011] The management platform includes a data receiving module, an information processing module, and an energy consumption management module;

[0012] The data receiving module is used to receive energy consumption data and production capacity generation data of each energy-consuming device, and calculate the energy consumption data and production capacity generation data of each energy-consuming device to obtain the energy consumption ratio of the corresponding energy-consuming device.

[0013] The data receiving module identifies the energy consumption ratio of each energy-consuming device according to the time series, and obtains the energy consumption fluctuation data of each energy-consuming device;

[0014] The data receiving module identifies the data of each influencing factor according to the time series and obtains the change data of each influencing factor.

[0015] The information processing module matches the energy consumption fluctuation data of each energy-consuming device with each change data, and calculates the influence factor of each influencing factor data on the energy consumption fluctuation data.

[0016] The energy management module dynamically optimizes each energy-consuming device based on changes in environmental factors over a future time period and in conjunction with influencing factors.

[0017] A further technical improvement of the present invention is that when the monitored energy consumption data is stable, that is, when the monitored energy consumption data is less than the preset energy consumption fluctuation threshold, the information processing module extracts the production capacity generation data when a single environmental factor changes in the big data.

[0018] And through the formula: The influence factor β of the corresponding environmental factors was calculated. j ;

[0019] In the formula, X j (t) represents the value of the j-th environmental factor at time t;

[0020] μX j Environmental factor X j The mean;

[0021] P out (t) represents the value of the capacity generation data at time t;

[0022] μP out This represents the average of the capacity generation data.

[0023] m is the number of samples.

[0024] A further technical improvement of the present invention is that: the information processing module calculates the impact delay by monitoring the time difference between energy consumption data and production capacity generation data, thereby assessing the relationship between input and output in different time periods. This helps to understand the response speed and efficiency of the system during operation, especially in the dynamic optimization process, and can provide an important reference for optimization control, including the following steps:

[0025] S1. Monitor and record changes in energy consumption data over time by inputting the monitoring unit;

[0026] The output monitoring unit monitors and records changes in production capacity generation data over time.

[0027] S2. Confirmation time difference Δt represents the time delay between energy input and production output. If energy consumption data enters the system at time point t1 and production output data enters at time point t2, then Δt = t2 - t1.

[0028] S3, through formula Calculate the impact delay Δs;

[0029] In the formula, P out (t2) and P out (t1) represents the capacity generation data at times t2 and t1, respectively;

[0030] E in (t2) and E in (t1) represents the energy consumption data at times t2 and t1, respectively;

[0031] When optimizing energy-consuming equipment, the energy management module optimizes based on the advance setting of the impact delay.

[0032] A further technical improvement of this invention is that the information processing module averages the impact delays of multiple time points to reduce the impact of fluctuations in production capacity generation data and energy consumption data. The formula is adjusted as follows:

[0033] In the formula, N represents the number of time points for monitoring and generating data fluctuations and energy consumption data, and t i and t i-1 These are two consecutive time points, P out (t i ) and E in (t iThe data represents fluctuations in production capacity and energy consumption at the corresponding time points.

[0034] A further technical improvement of the present invention is that the information processing module calculates the similarity between energy consumption data and production capacity generation data through cross-correlation analysis to calculate the time difference Δt, including the following steps:

[0035] Q1. The input monitoring unit acquires energy consumption data E through time series. in (t);

[0036] The output monitoring unit acquires production capacity generation data P through time series analysis. out (t);

[0037] Furthermore, both energy consumption data and production capacity generation data include multiple timestamps;

[0038] Q2. Calculate energy consumption data E in (t) and capacity generation data P out The cross-correlation function between (t) is expressed by the following formula:

[0039] In the formula, τ is the time lag variable, and T is the energy consumption data E. in (t) and capacity generation data P out The total time length of (t);

[0040] It is a cross-correlation function used to quantify the similarity of two signals at different time lags;

[0041] Q3. By finding the cross-correlation function The maximum value is used to determine the energy consumption data E. in (t) and capacity generation data P out The optimal time lag τ between (t) max That is, the time difference Δt;

[0042] The meaning is to find the lag time τ that maximizes the cross-correlation function.

[0043] A further technical improvement of the present invention is that: the information processing module compares the fluctuations of energy consumption data and production capacity generation data to verify the time difference, including:

[0044] Y1, Time series of energy consumption data and production capacity generation data;

[0045] The fluctuations in energy consumption data and production capacity generation data are calculated using the adjacent time difference method.

[0046] Y2, preset energy consumption fluctuation threshold and capacity generation fluctuation threshold;

[0047] When the fluctuation of energy consumption data exceeds the energy consumption fluctuation threshold, record the time point H1 when the occurrence occurs.

[0048] When the fluctuation of capacity generation data exceeds the capacity generation fluctuation threshold, record the occurrence time point H2;

[0049] Y3. Calculate the time difference ΔH between the occurrence time point H1 and the occurrence time point H2;

[0050] Y3. Record the time difference ΔH of fluctuations in multiple energy consumption data and production capacity data. i And calculate the average fluctuation time difference.

[0051] Y4, Average fluctuation time difference Compare the time difference Δt with the time delay to determine its reasonableness. The closer the two are, the more reasonable the time delay is.

[0052] A further technical improvement of the present invention is that: when the information processing module performs the time difference Δt test, it introduces a regression model to eliminate the impact of changes in environmental factors on fluctuations in production capacity generation data;

[0053] The formula is:

[0054] In the formula, β0 is a constant term, representing the energy consumption data E in (t) and environmental factors X j When (t) are both 0, this is the base value for capacity generation data;

[0055] β1E in (t-ΔH) indicates that the production capacity data at time t is affected by the energy consumption data at time t-Δt;

[0056] ω1 is the regression coefficient, representing the degree of influence of energy consumption data on production capacity data;

[0057] ΔH is the time difference of fluctuation between occurrence time point H1 and occurrence time point H2;

[0058] X j (t) represents the value of the j-th environmental factor at time t;

[0059] β j , where j is the influencing factor, representing the degree of influence of the j-th environmental factor on the production capacity data;

[0060] n represents the total number of environmental factors;

[0061] δ(t) is the error term.

[0062] The present invention also discloses a storage medium for an integrated energy station energy consumption management and optimization system. The storage medium is used to store the management platform, energy consumption data, production capacity generation data and influencing factor data in the energy consumption management and optimization system.

[0063] Compared with the prior art, the present invention has the following beneficial effects:

[0064] This invention quantifies the impact of each environmental factor by correlating it with energy input and production data, and then optimizes and controls it accordingly. This allows for real-time control of various energy-consuming devices when relevant environmental factors change, ensuring the stability of production output and avoiding response lag and energy waste.

[0065] Furthermore, by monitoring the fluctuations in energy consumption data and production capacity data, setting fluctuation thresholds, and automatically identifying the time points when fluctuations occur, this invention calculates the time delay between energy input and production capacity output. By identifying the time delay, the system can better understand the response characteristics of energy-consuming equipment during operation and predict the impact of energy input on production capacity output in advance. Attached Figure Description

[0066] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0067] Figure 1 This is a system block diagram of the present invention. Detailed Implementation

[0068] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided.

[0069] Example 1

[0070] Please see Figure 1 As shown, the present invention provides an integrated energy station energy consumption management and optimization system, including a monitoring module and a management platform;

[0071] The monitoring module includes multiple input monitoring units, multiple output monitoring units, and influencing factor monitoring units. Each input monitoring unit and each output monitoring unit is matched with a set of energy-consuming devices.

[0072] Each input monitoring unit is used to monitor the energy input of the corresponding energy-consuming device and generate energy consumption data for the corresponding energy-consuming device;

[0073] Each output monitoring unit is used to monitor the output capacity of the corresponding energy-consuming equipment and generate the corresponding energy-consuming equipment's output capacity data.

[0074] The influencing factor monitoring unit is used to monitor changes in environmental factors in the area where each energy-consuming device is located, and generate corresponding influencing factor data, including temperature data, humidity data, wind data, solar radiation data (including light intensity, light angle and light duration), and precipitation data (including rain and snow).

[0075] The management platform includes a data receiving module, an information processing module, and an energy consumption management module;

[0076] The data receiving module is used to receive energy consumption data and production capacity generation data of each energy-consuming device, and calculate the energy consumption data and production capacity generation data of each energy-consuming device to obtain the energy consumption ratio of the corresponding energy-consuming device.

[0077] The data receiving module identifies the energy consumption ratio of each energy-consuming device according to the time series, and obtains the energy consumption fluctuation data of each energy-consuming device;

[0078] The data receiving module identifies the data of each influencing factor according to the time series and obtains the change data of each influencing factor.

[0079] The information processing module matches the energy consumption fluctuation data of each energy-consuming device with each change data, and calculates the influence factor of each influencing factor data on the energy consumption fluctuation data.

[0080] The energy management module dynamically optimizes each energy-consuming device based on changes in environmental factors over a future time period and in conjunction with influencing factors.

[0081] In this embodiment, changes in environmental factors can be obtained through meteorological stations in the corresponding area;

[0082] When the monitored energy consumption data is stable, that is, when the monitored energy consumption data is less than the preset energy consumption fluctuation threshold, the information processing module extracts the production capacity generation data when a single environmental factor changes in the big data.

[0083] And through the formula: The influence factor β of the corresponding environmental factors was calculated. j ;

[0084] In the formula, X j (t) represents the value of the j-th environmental factor at time t;

[0085] μX j Environmental factor X j The mean;

[0086] P out(t) represents the value of the capacity generation data at time t;

[0087] μP out This represents the average of the capacity generation data.

[0088] m is the number of samples;

[0089] In this embodiment, temperature data X1, humidity data X2, and wind speed data X3 for a future time period X are obtained from meteorological stations in the corresponding area. The information processing module then uses the following formula: Inferring the projected production capacity for future time period X This facilitates energy management by optimizing the energy input of corresponding energy-consuming devices;

[0090] The information processing module calculates the impact delay by monitoring the time difference between energy consumption data and production capacity generation data, thereby assessing the relationship between input and output in different time periods. This helps to understand the response speed and efficiency of the system during operation, especially in the dynamic optimization process, and can provide important reference for optimization control, including the following steps:

[0091] S1. Monitor and record the change of energy consumption data over time by inputting the monitoring unit, where t represents time;

[0092] The output monitoring unit monitors and records changes in production capacity generation data over time.

[0093] S2. Confirmation time difference Δt represents the time delay between energy input and production output. If energy consumption data enters the system at time point t1 and production output data enters at time point t2, then Δt = t2 - t1.

[0094] The method for calculating the time difference Δt includes the following steps:

[0095] Q1. The input monitoring unit acquires energy consumption data E through time series. in (t);

[0096] The output monitoring unit acquires production capacity generation data P through time series analysis. out (t);

[0097] Furthermore, both energy consumption data and production capacity generation data include multiple timestamps;

[0098] Q2. Calculate energy consumption data E in (t) and capacity generation data P out The cross-correlation function between (t) is expressed by the following formula:

[0099] In the formula, τ is the time lag variable, and T is the energy consumption data E.in (t) and capacity generation data P out The total time length of (t);

[0100] It is a cross-correlation function used to quantify the similarity of two signals at different time lags;

[0101] Q3. By finding the cross-correlation function The maximum value is used to determine the energy consumption data E. in (t) and capacity generation data P out The optimal time lag τ between (t) max That is, the time difference Δt;

[0102] The meaning is to find the lag time τ that maximizes the cross-correlation function;

[0103] S3, through formula Calculate the impact delay Δs;

[0104] In the formula, P out (t2) and P out (t1) represents the capacity generation data at times t2 and t1, respectively;

[0105] E in (t2) and E in (t1) represents the energy consumption data at times t2 and t1, respectively;

[0106] Furthermore, the information processing module compares the fluctuations in energy consumption data and production capacity data to verify the time difference, including:

[0107] Y1, Collect energy consumption data E in (t) and capacity generation data P out The time series of (t);

[0108] Energy consumption data E was calculated using the adjacent time difference method. in The fluctuation of (t) is given by the formula: ΔE in (t)=E in (t)-E in (t-1);

[0109] In the formula, ΔE in (t) represents the energy consumption data E at time t. in The fluctuation of (t);

[0110] The capacity generation data P is calculated using the adjacent time difference method. out The fluctuation of (t) is given by the formula: ΔP out (t)=Pout (t)-P out (t-1);

[0111] ΔP out (t) represents the production capacity generation data P at time t. out The fluctuation of (t);

[0112] Y2, preset energy consumption fluctuation threshold and capacity generation fluctuation threshold;

[0113] When energy consumption data E in Once the fluctuation of (t) exceeds the energy consumption fluctuation threshold, record the occurrence time point H1;

[0114] When the capacity generation data P out Once the fluctuation of (t) exceeds the capacity generation fluctuation threshold, record the occurrence time point H2;

[0115] Y3. Calculate the fluctuation time difference between occurrence time point H1 and occurrence time point H2;

[0116] ΔH = H2 - H1;

[0117] Y3, Record multiple energy consumption data E in (t) and capacity generation data P out (t) Fluctuation time difference ΔH i And calculate the average time difference.

[0118] Y4, Average fluctuation time difference Compare the time difference Δt with the time delay to determine its reasonableness. The closer the two are, the more reasonable the time delay is.

[0119] When optimizing energy-consuming equipment, the energy management module optimizes based on the advance setting of the impact delay.

[0120] Example 2

[0121] An integrated energy station energy consumption management and optimization system includes a monitoring module and a management platform;

[0122] The monitoring module includes multiple input monitoring units, multiple output monitoring units, and influencing factor monitoring units. Each input monitoring unit and each output monitoring unit is matched with a set of energy-consuming devices.

[0123] Each input monitoring unit is used to monitor the energy input of the corresponding energy-consuming device and generate energy consumption data for the corresponding energy-consuming device;

[0124] Each output monitoring unit is used to monitor the output capacity of the corresponding energy-consuming equipment and generate the corresponding energy-consuming equipment's output capacity data.

[0125] The influencing factor monitoring unit is used to monitor changes in environmental factors in the area where each energy-consuming device is located, and generate corresponding influencing factor data, including temperature data, humidity data, wind data, solar radiation data (including light intensity, light angle and light duration), and precipitation data (including rain and snow).

[0126] The management platform includes a data receiving module, an information processing module, and an energy consumption management module;

[0127] The data receiving module is used to receive energy consumption data and production capacity generation data of each energy-consuming device, and calculate the energy consumption data and production capacity generation data of each energy-consuming device to obtain the energy consumption ratio of the corresponding energy-consuming device.

[0128] The data receiving module identifies the energy consumption ratio of each energy-consuming device according to the time series, and obtains the energy consumption fluctuation data of each energy-consuming device;

[0129] The data receiving module identifies the data of each influencing factor according to the time series and obtains the change data of each influencing factor.

[0130] The information processing module matches the energy consumption fluctuation data of each energy-consuming device with each change data, and calculates the influence factor of each influencing factor data on the energy consumption fluctuation data.

[0131] The energy management module dynamically optimizes each energy-consuming device based on changes in environmental factors over a future time period and in conjunction with influencing factors.

[0132] In this embodiment, changes in environmental factors can be obtained through meteorological stations in the corresponding area;

[0133] When the monitored energy consumption data is stable, that is, when the monitored energy consumption data is less than the preset energy consumption fluctuation threshold, the information processing module extracts the production capacity generation data when a single environmental factor changes in the big data.

[0134] And through the formula: The influence factor β of the corresponding environmental factors was calculated. j ;

[0135] In the formula, X j (t) represents the value of the j-th environmental factor at time t;

[0136] μX j Environmental factor X j The mean;

[0137] P out (t) represents the value of the capacity generation data at time t;

[0138] μP outThis represents the average of the capacity generation data.

[0139] m is the number of samples;

[0140] The information processing module calculates the impact delay by monitoring the time difference between energy consumption data and production capacity generation data, thereby assessing the relationship between input and output in different time periods. This helps to understand the response speed and efficiency of the system during operation, especially in the dynamic optimization process, and can provide important reference for optimization control. Specifically, it includes the following steps:

[0141] S1. Monitor and record the change of energy consumption data over time by inputting the monitoring unit, where t represents time;

[0142] The output monitoring unit monitors and records changes in production capacity generation data over time.

[0143] S2. Confirmation time difference Δt represents the time delay between energy input and production output. If energy consumption data enters the system at time point t1 and production output data enters at time point t2, then Δt = t2 - t1.

[0144] The method for calculating the time difference Δt includes the following steps:

[0145] Q1. The input monitoring unit acquires energy consumption data E through time series. in (t);

[0146] The output monitoring unit acquires production capacity generation data P through time series analysis. out (t);

[0147] Furthermore, both energy consumption data and production capacity generation data include multiple timestamps;

[0148] Q2. Calculate energy consumption data E in (t) and capacity generation data P out The cross-correlation function between (t) is expressed by the following formula:

[0149] In the formula, τ is the time lag variable, and T is the energy consumption data E. in (t) and capacity generation data P out The total time length of (t);

[0150] It is a cross-correlation function used to quantify the similarity of two signals at different time lags;

[0151] Q3. By finding the cross-correlation function The maximum value is used to determine the energy consumption data E. in (t) and capacity generation data P outThe optimal time lag τ between (t) max That is, the time difference Δt;

[0152] The meaning is to find the lag time τ that maximizes the cross-correlation function;

[0153] Compared to Example 1, S3 in Example 2 is as follows: The information processing module averages the impact delays of multiple time points to reduce the impact of fluctuations in production capacity generation data and energy consumption data. The formula is adjusted as follows:

[0154] In the formula, N represents the number of time points for monitoring and generating data fluctuations and energy consumption data, and t i and t i-1 These are two consecutive time points, P out (t i ) and E in (t i This represents the fluctuations in production capacity and energy consumption data at the corresponding points in time.

[0155] Furthermore, the information processing module compares the fluctuations in energy consumption data and production capacity data to verify the time difference, including:

[0156] Y1, Collect energy consumption data E in (t) and capacity generation data P out The time series of (t);

[0157] Energy consumption data E was calculated using the adjacent time difference method. in The fluctuation of (t) is given by the formula: ΔE in (t)=E in (t)-E in (t-1);

[0158] In the formula, ΔE in (t) represents the energy consumption data E at time t. in The fluctuation of (t);

[0159] The capacity generation data P is calculated using the adjacent time difference method. out The fluctuation of (t) is given by the formula: ΔP out (t)=P out (t)-P out (t-1);

[0160] ΔP out (t) represents the production capacity generation data P at time t. out The fluctuation of (t);

[0161] Y2, preset energy consumption fluctuation threshold and capacity generation fluctuation threshold;

[0162] When energy consumption data E in Once the fluctuation of (t) exceeds the energy consumption fluctuation threshold, record the occurrence time point H1;

[0163] When the capacity generation data P out Once the fluctuation of (t) exceeds the capacity generation fluctuation threshold, record the occurrence time point H2;

[0164] Y3. Calculate the time difference between occurrence time point H1 and occurrence time point H2;

[0165] ΔH = H2 - H1;

[0166] Y3, Record multiple energy consumption data E in (t) and capacity generation data P out (t) Time difference ΔH during fluctuation i And calculate the average time difference.

[0167] Y4, Average Time Difference Compare the time difference Δt with the time difference to determine its reasonableness. The closer the two are, the more reasonable the time difference Δt is.

[0168] When performing the time difference Δt test, the information processing module introduces a regression model to eliminate the impact of changes in environmental factors on fluctuations in production capacity data.

[0169] The formula is:

[0170] In the formula, β0 is a constant term, representing the energy consumption data E in (t) and environmental factors X j When (t) are both 0, this is the base value for capacity generation data;

[0171] β1E in (t-ΔH) indicates that the production capacity data at time t is affected by the energy consumption data at time t-Δt;

[0172] ω1 is the regression coefficient, representing the degree of influence of energy consumption data on production capacity data;

[0173] ΔH is the time difference of fluctuation between occurrence time point H1 and occurrence time point H2;

[0174] X j (t) represents the value of the j-th environmental factor at time t;

[0175] β j , where j is the influencing factor, representing the degree of influence of the j-th environmental factor on the production capacity data;

[0176] n represents the total number of environmental factors;

[0177] δ(t) is the error term;

[0178] When optimizing energy-consuming equipment, the energy management module optimizes based on the advance setting of the impact delay.

[0179] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A comprehensive energy station energy consumption management and optimization system, comprising a monitoring module and a management platform, characterized in that: The monitoring module includes multiple input monitoring units, multiple output monitoring units, and influencing factor monitoring units. Each input monitoring unit and each output monitoring unit is matched with a set of energy-consuming devices. Each input monitoring unit is used to monitor the energy input of the corresponding energy-consuming device and generate energy consumption data for the corresponding energy-consuming device; Each output monitoring unit is used to monitor the output capacity of the corresponding energy-consuming equipment and generate the corresponding energy-consuming equipment's output capacity data. The influencing factor monitoring unit is used to monitor changes in environmental factors in the area where each energy-consuming device is located and generate corresponding influencing factor data; The management platform includes a data receiving module, an information processing module, and an energy consumption management module; The data receiving module is used to receive energy consumption data and production capacity generation data of each energy-consuming device, and to calculate the energy consumption ratio of the corresponding energy-consuming device. Furthermore, the data receiving module identifies the energy consumption ratio of each energy-consuming device according to the time series, and obtains the energy consumption fluctuation data of each energy-consuming device; The data receiving module identifies the data of each influencing factor according to the time series and obtains the change data of each influencing factor. The information processing module matches the energy consumption fluctuation data of each energy-consuming device with each change data, and calculates the influence factor of each influencing factor data on the energy consumption fluctuation data. When the energy consumption data is stable, the information processing module extracts the production capacity generation data when a single environmental factor changes in the big data. And through the formula: The influencing factors of the corresponding environmental factors are calculated. ; In the formula, Let j be the value of the j-th environmental factor at time t; Environmental factors The mean; The value of the capacity generation data at time t; The mean of the capacity generation data is m; m is the sample size. The information processing module calculates the impact delay by monitoring the time difference between energy consumption data and production capacity generation data, thereby assessing the relationship between input and output in different time periods, including the following steps: S1. Monitor and record changes in energy consumption data over time by inputting the monitoring unit; The output monitoring unit monitors and records changes in production capacity generation data over time. S2. Monitor energy consumption data at specific time points. Upon entering the system, production capacity generation data is displayed at specific times. ,pass Calculate the time difference ; S3, through formula Calculate the impact of delay ; In the formula, and These respectively represent the capacity generation data in and Real-time production capacity data; and They represent in and Energy consumption data at any given moment; The energy management module dynamically optimizes each energy-consuming device based on changes in environmental factors over a future time period and in conjunction with influencing factors.

2. The integrated energy station energy consumption management and optimization system according to claim 1, characterized in that, When optimizing energy-consuming equipment, the energy management module optimizes based on the advance setting of the impact delay.

3. The integrated energy station energy consumption management and optimization system according to claim 1, characterized in that, The information processing module averages the impact delays from multiple time points to reduce the impact of fluctuations in production capacity generation data and energy consumption data. The formula is adjusted as follows: ; In the formula, N represents the number of time points for monitoring data fluctuations and energy consumption data. and These are two consecutive time points. and This represents fluctuations in production capacity and energy consumption data at corresponding points in time.

4. A storage medium for an integrated energy station energy consumption management and optimization system, characterized in that, The storage medium is used to store the management platform, energy consumption data, production capacity generation data, and influencing factor data in any one of the energy management optimization systems of claims 1-3.

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