Comprehensive energy station energy consumption management optimization system and storage medium

Abstract

The invention discloses an integrated energy station energy consumption management optimization system and a storage medium, and relates to the technical field of energy consumption management, the integrated energy station energy consumption management optimization system comprises a monitoring module and a management platform, the monitoring module comprises a plurality of input monitoring units, a plurality of output monitoring units and an influence factor monitoring unit; each input monitoring unit is used for monitoring the energy input quantity of the corresponding energy consumption equipment and generating energy consumption data of the corresponding energy consumption equipment; each output monitoring unit is used for monitoring the capacity output quantity of the corresponding energy consumption equipment and generating capacity generation data of the corresponding energy consumption equipment; the influence factor monitoring unit monitors the environmental factor change of the area where each energy consumption device is located, and generates corresponding influence factor data. According to the method, the environmental factors are associated with the energy input and capacity generation data, the influence degree of each environmental factor is quantified, and optimization regulation and control are performed on the basis, so that real-time regulation and control of each energy consumption device are realized when the related environmental factors change.

Description

Technical Field

[0001] The present invention relates to the technical field of energy consumption management, and in particular to an energy consumption management optimization system and storage medium for an integrated energy station. Background Art

[0002] With the widespread application of integrated energy stations, efficient management and optimization of various energy-consuming equipment has become an important research direction to improve energy utilization and reduce energy waste. At present, integrated energy stations mainly rely on energy consumption monitoring systems and capacity output monitoring systems to monitor the energy input and capacity generation of energy-consuming equipment in real time. However, existing technologies usually focus on the static relationship analysis of energy consumption data and capacity generation data, and fail to fully consider the time delay and environmental factors in dynamic operation.

[0003] After searching, a Chinese patent (CN116993232A) discloses an energy consumption management optimization method and system for an integrated energy station. The patent includes obtaining multiple air-conditioning control subsystems in a central air-conditioning energy station, each subsystem corresponds to a control loop, and is bidirectionally connected to a data acquisition card, performing status data acquisition, outputting energy consumption status data sets, inputting the data center for storage, performing energy consumption data comparison, obtaining data matching indicators corresponding to every two air-conditioning control subsystems, classifying and outputting multiple types of subsystems, matching them with units of an energy consumption processing module, establishing multiple data transmission channels, performing optimization processing, generating energy consumption control parameters, and controlling the central air-conditioning energy station.

[0004] In the prior art, energy-consuming equipment has a certain time delay in actual operation. The prior art is poor in identifying and quantifying time delays, and it is difficult to accurately evaluate the dynamic response relationship between energy input and production output. In addition, when environmental factors affect energy-consuming equipment, if they cannot be predicted and regulated in advance, it is easy to cause energy waste or insufficient output. Therefore, the present invention provides an energy consumption management optimization system and storage medium for an integrated energy station. Summary of the invention

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

[0006] The present invention can be implemented by the following technical solutions: an integrated energy station energy consumption management optimization system, including a monitoring module and a management platform;

[0007] The monitoring module includes multiple input monitoring units, multiple output monitoring units and an influencing factor monitoring unit, and each input monitoring unit and each output monitoring unit are respectively matched with a group of energy consumption equipment;

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

[0009] Each output monitoring unit is used to monitor the capacity output of the corresponding energy consuming equipment and generate capacity generation data of the corresponding energy consuming equipment;

[0010] The influencing factor monitoring unit is used to monitor the 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 the energy consumption data and capacity generation data of each energy consuming device, and calculate the energy consumption data and 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 data;

[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 consumption management module dynamically optimizes each energy consumption device based on changes in environmental factors in a future time period and in combination with influencing factors.

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

[0018] And through the formula: Calculate the impact factor β of the corresponding environmental factor j ;

[0019] Where, X j (t) is the value of the jth environmental factor at time t;

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

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

[0022] μP out The mean of the data generated for capacity;

[0023] m is the sample size.

[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 the energy consumption data and the capacity generation data, thereby evaluating the relationship between the input and output in different time periods, which helps to understand the response speed and efficiency in the operation of the system, especially in the dynamic optimization process, and can provide an important reference for optimization control, including the following steps:

[0025] S1. Monitor and record the change of energy consumption data over time through the input monitoring unit;

[0026] Monitor and record the changes of capacity generation data over time through the output monitoring unit;

[0027] S2. Confirm the time difference Δt, which represents the time delay from energy input to capacity output. The monitored energy consumption data enters the system at time point t1, and the capacity generation data enters the system at time point t2, then Δt = t2-t1;

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

[0029] Where P out (t2) and P out (t1) represents the capacity generation data at time t2 and time t1 respectively;

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

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

[0032] A further technical improvement of the present 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, and the formula is adjusted to:

[0033] Where N is the number of time points for monitoring the fluctuation of generated data and energy consumption data, t i and t i-1 are two consecutive time points, P out (t i ) and E in (t i) is the fluctuation of capacity generation data and energy consumption data at the corresponding time point.

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

[0035] Q1. Input monitoring unit obtains energy consumption data E through time series in (t);

[0036] The output monitoring unit obtains the capacity generation data P through time series out (t);

[0037] And both the energy consumption data and the 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 formulated as follows:

[0039] Where τ is the time lag variable, T is the energy consumption data E in (t) and capacity generation data P out (t) the total duration;

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

[0041] Q3. By finding the cross-correlation function The maximum value of 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 fluctuation of energy consumption data and capacity generation data to check the time difference, including:

[0044] Y1, collect time series of energy consumption data and capacity generation data;

[0045] The adjacent time difference method is used to calculate the fluctuation of energy consumption data and the fluctuation of capacity generation data;

[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, the occurrence time point H1 is recorded;

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

[0049] Y3, calculate the time difference ΔH between the occurrence time point H1 and the occurrence time point H2;

[0050] Y3. Record the fluctuation time difference ΔH when multiple energy consumption data and production capacity generation data fluctuate i , and calculate the average fluctuation time difference

[0051] Y4. Average volatility time difference Compare it with the time difference Δt to judge the rationality of the time difference Δt. 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, the influence of environmental factor changes on the fluctuation of the production capacity generation data is eliminated by introducing a regression model;

[0053] The formula is:

[0054] In the formula, β0 is a constant term, which means that when the energy consumption data E in (t) and environmental factors X j When (t) is 0, the basic value of capacity generation data;

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

[0056] ω1 is the regression coefficient, which indicates the influence of energy consumption data on capacity generation data;

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

[0058] X j (t) is the value of the jth environmental factor at time t;

[0059] β j is the influencing factor, which indicates the influence of the jth environmental factor on the capacity generation 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 of an integrated energy station energy consumption management optimization system, wherein the storage medium is used to store a management platform in the energy consumption management optimization system as well as energy consumption data, capacity generation data and influencing factor data.

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

[0064] The present invention associates environmental factors with energy input and capacity generation data, quantifies the impact of various environmental factors, and optimizes and regulates on this basis, so as to achieve real-time regulation of various energy-consuming devices when relevant environmental factors change, ensure the stability of capacity output, and avoid response lag and energy waste;

[0065] In addition, the present invention monitors the fluctuations of energy consumption data and capacity generation data, sets fluctuation thresholds, automatically identifies the time points when the fluctuations occur, and then calculates the time delay between energy input and capacity output. By identifying the time delay, it helps the system understand the response characteristics of energy-consuming equipment in operation and predict the impact of energy input on capacity output in advance. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0067] Figure 1 It is a system block diagram of the present invention. DETAILED DESCRIPTION

[0068] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0069] Example 1

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

[0071] The monitoring module includes multiple input monitoring units, multiple output monitoring units and an influencing factor monitoring unit, and each input monitoring unit and each output monitoring unit are respectively matched with a group of energy consumption equipment;

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

[0073] Each output monitoring unit is used to monitor the capacity output of the corresponding energy consuming equipment and generate capacity generation data of the corresponding energy consuming equipment;

[0074] The influencing factor monitoring unit is used to monitor the 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 time), 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 the energy consumption data and capacity generation data of each energy consuming device, and calculate the energy consumption data and 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 data;

[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 consumption management module dynamically optimizes each energy consumption device based on the changes in environmental factors in the future time period and in combination with influencing factors;

[0081] In this embodiment, the environmental factor changes can be obtained through the weather station of the corresponding area;

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

[0083] And through the formula: Calculate the impact factor β of the corresponding environmental factor j ;

[0084] Where, X j (t) is the value of the jth environmental factor at time t;

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

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

[0087] μP out The mean of the data generated for capacity;

[0088] m is the sample size;

[0089] In this embodiment, the temperature data X1, humidity data X2 and wind speed data X3 in the future time period X are obtained through the weather station in the corresponding area, and the information processing module uses the formula: Extrapolate forecast capacity generation data for future time period X This makes it easier to manage energy consumption by optimizing the energy input of corresponding energy-consuming equipment;

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

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

[0092] Monitor and record the changes of capacity generation data over time through the output monitoring unit;

[0093] S2. Confirm the time difference Δt, which represents the time delay from energy input to capacity output. The monitored energy consumption data enters the system at time point t1, and the capacity generation data enters the system at time point t2, then Δt = t2-t1;

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

[0095] Q1. Input monitoring unit obtains energy consumption data E through time series in (t);

[0096] The output monitoring unit obtains the capacity generation data P through time series out (t);

[0097] And both the energy consumption data and the 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 formulated as follows:

[0099] Where τ is the time lag variable, T is the energy consumption data Ein (t) and capacity generation data P out (t) the total duration;

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

[0101] Q3. By finding the cross-correlation function The maximum value of 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 of is to find the lag time τ that maximizes the cross-correlation function;

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

[0104] Where P out (t2) and P out (t1) represents the capacity generation data at time t2 and time t1 respectively;

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

[0106] Furthermore, the information processing module compares the fluctuation of energy consumption data and capacity generation data to check the time difference, including:

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

[0108] Calculate energy consumption data E using adjacent time difference method in The fluctuation of (t) is given by: ΔE in (t) = E in (t)-E in (t-1);

[0109] In the formula, ΔE in (t) is the energy consumption data E at time t in Fluctuation of (t);

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

[0111] ΔP out (t) is the capacity generation data P at time t out Fluctuation of (t);

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

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

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

[0115] Y3. Calculate the fluctuation time difference between the occurrence time point H1 and the 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) The fluctuation time difference ΔH during fluctuation i , and calculate the average time difference

[0118] Y4. Average volatility time difference Compare it with the time difference Δt to judge the rationality of the time difference Δt. The closer the two are, the more reasonable the time delay is.

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

[0120] Example 2

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

[0122] The monitoring module includes multiple input monitoring units, multiple output monitoring units and an influencing factor monitoring unit, and each input monitoring unit and each output monitoring unit are respectively matched with a group of energy consumption equipment;

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

[0124] Each output monitoring unit is used to monitor the capacity output of the corresponding energy consuming equipment and generate capacity generation data of the corresponding energy consuming equipment;

[0125] The influencing factor monitoring unit is used to monitor the 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 time), 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 the energy consumption data and capacity generation data of each energy consuming device, and calculate the energy consumption data and 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 data;

[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 consumption management module dynamically optimizes each energy consumption device based on the changes in environmental factors in the future time period and in combination with influencing factors;

[0132] In this embodiment, the environmental factor changes can be obtained through the weather station of the corresponding area;

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

[0134] And through the formula: Calculate the impact factor β of the corresponding environmental factor j ;

[0135] Where, X j (t) is the value of the jth environmental factor at time t;

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

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

[0138] μP outThe mean of the data generated for capacity;

[0139] m is the sample size;

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

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

[0142] Monitor and record the changes of capacity generation data over time through the output monitoring unit;

[0143] S2. Confirm the time difference Δt, which represents the time delay from energy input to capacity output. The monitored energy consumption data enters the system at time point t1, and the capacity generation data enters the system at time point t2, then Δt = t2-t1;

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

[0145] Q1. Input monitoring unit obtains energy consumption data E through time series in (t);

[0146] The output monitoring unit obtains the capacity generation data P through time series out (t);

[0147] And both the energy consumption data and the 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 formulated as follows:

[0149] Where τ is the time lag variable, T is the energy consumption data E in (t) and capacity generation data P out (t) the total duration;

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

[0151] Q3. By finding the cross-correlation function The maximum value of 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 of is to find the lag time τ that maximizes the cross-correlation function;

[0153] Compared with Example 1, S3 in Example 2 is: the information processing module delays the impact of multiple time points for average calculation to reduce the impact of fluctuations in production capacity generation data and energy consumption data, and the formula is adjusted to:

[0154] Where N is the number of time points for monitoring the fluctuation of generated data and energy consumption data, t i and t i-1 are two consecutive time points, P out (t i ) and E in (t i ) is the fluctuation of capacity generation data and energy consumption data at the corresponding time point;

[0155] Furthermore, the information processing module compares the fluctuation of energy consumption data and capacity generation data to check the time difference, including:

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

[0157] Calculate energy consumption data E using adjacent time difference method in The fluctuation of (t) is given by: ΔE in (t) = E in (t)-E in (t-1);

[0158] In the formula, ΔE in (t) is the energy consumption data E at time t in Fluctuation of (t);

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

[0160] ΔP out (t) is the capacity generation data P at time t out Fluctuation of (t);

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

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

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

[0164] Y3. Calculate the time difference between the occurrence time point H1 and the 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 it with the time difference Δt to judge the rationality of the time difference Δt. The closer the two are, the more reasonable the time difference Δt is.

[0168] When the information processing module performs the time difference Δt test, the impact of environmental factor changes on the fluctuation of production capacity generation data is eliminated by introducing a regression model;

[0169] The formula is:

[0170] In the formula, β0 is a constant term, which means that when the energy consumption data E in (t) and environmental factors X j When (t) is 0, the basic value of capacity generation data;

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

[0172] ω1 is the regression coefficient, which indicates the influence of energy consumption data on capacity generation data;

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

[0174] X j (t) is the value of the jth environmental factor at time t;

[0175] β j is the influencing factor, which indicates the influence of the jth environmental factor on the capacity generation data;

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

[0177] δ(t) is the error term;

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

[0179] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention without departing from the content of the technical solution of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. An integrated energy station energy consumption management and optimization system, including a monitoring module and a management platform, characterized in that: The monitoring module includes multiple input monitoring units, multiple output monitoring units and an influencing factor monitoring unit, and each input monitoring unit and each output monitoring unit are respectively matched with a group of energy consumption equipment; Each input monitoring unit is used to monitor the energy input amount of the corresponding energy consuming equipment and generate energy consumption data of the corresponding energy consuming equipment; Each output monitoring unit is used to monitor the capacity output of the corresponding energy consuming equipment and generate capacity generation data of the corresponding energy consuming equipment; The influencing factor monitoring unit is used to monitor the 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 the energy consumption data and capacity generation data of each energy-consuming device, and perform calculations to obtain the energy consumption ratio of the corresponding energy-consuming device; And 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 data; 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; The energy consumption management module dynamically optimizes each energy consumption device based on changes in environmental factors in a future time period and in combination with influencing factors.

2. The energy consumption management optimization system for an integrated energy station according to claim 1 is characterized in that: When the monitored energy consumption data is stable, the information processing module extracts the capacity generation data when a single environmental factor changes in the big data; And through the formula: Calculate the impact factor β of the corresponding environmental factor j ; Where, X j (t) is the value of the jth environmental factor at time t; μX j Environmental factor X j The mean value of P out (t) is the value of the capacity generation data at time t; μP out is the mean of the capacity generation data; m is the number of samples.

3. The energy consumption management optimization system for an integrated energy station according to claim 1 is characterized in that: The information processing module calculates the impact delay by monitoring the time difference between energy consumption data and capacity generation data, thereby evaluating the relationship between input and output in different time periods, including the following steps: S1. Monitor and record the change of energy consumption data over time through the input monitoring unit; Monitor and record the changes of capacity generation data over time through the output monitoring unit; S2, monitoring energy consumption data entering the system at time point t1, capacity generation data at time point t2, and calculating the time difference Δt by Δt=t2-t1; S3, through the formula Calculate the impact delay Δs; Where P out (t2) and P out (t1) represents the capacity generation data at time t2 and time t1 respectively; E in (t2) and E in (t1) represents the energy consumption data at time t2 and time t1 respectively.

4. The energy consumption management optimization system for an integrated energy station according to claim 3 is characterized in that: When optimizing energy-consuming devices, the energy consumption management module optimizes based on the advance amount of the impact delay setting.

5. The energy consumption management optimization system for an integrated energy station according to claim 3 is characterized in 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 to: Where N is the number of time points for monitoring the fluctuation of generated data and energy consumption data, t i and t i-1 are two consecutive time points, P out (t i ) and E in (t i ) is the fluctuation of capacity generation data and energy consumption data at the corresponding time point.

6. The energy consumption management optimization system for an integrated energy station according to claim 3 is characterized in that: The information processing module calculates the similarity between the energy consumption data and the capacity generation data through cross-correlation analysis to calculate the time difference Δt, including the following steps: Q1. Input monitoring unit obtains energy consumption data E through time series in (t); The output monitoring unit obtains the capacity generation data P through time series out (t); And both the energy consumption data and the capacity generation data include multiple timestamps; Q2. Calculate energy consumption data E in (t) and capacity generation data P out The cross-correlation function between (t) is formulated as follows: Where τ is the time lag variable, T is the energy consumption data E in (t) and capacity generation data P out (t) the total duration; is the cross-correlation function, which is used to quantify the similarity of two signals at different time lags; Q3. By finding the cross-correlation function The maximum value of energy consumption data E in (t) and capacity generation data P out The optimal time lag τ between (t) max , which is the time difference Δt.

7. The energy consumption management optimization system for an integrated energy station according to claim 6, characterized in that: The information processing module compares the fluctuation of energy consumption data and capacity generation data to check the time difference, including: Y1, collect time series of energy consumption data and capacity generation data; The adjacent time difference method is used to calculate the fluctuation of energy consumption data and the fluctuation of capacity generation data; Y2, preset energy consumption fluctuation threshold and capacity generation fluctuation threshold; When the fluctuation of energy consumption data exceeds the energy consumption fluctuation threshold, the occurrence time point H1 is recorded; When the fluctuation of the capacity generation data exceeds the capacity generation fluctuation threshold, the occurrence time point H2 is recorded; Y3, calculate the time difference ΔH between the occurrence time point H1 and the occurrence time point H2; Y3. Record the fluctuation time difference ΔH when multiple energy consumption data and production capacity generation data fluctuate i , and calculate the average fluctuation time difference Y4. Average volatility time difference Compare with the time difference Δt to determine the rationality of the time difference Δt.

8. A storage medium for an integrated energy station energy consumption management optimization system, characterized in that: The storage medium is used to store the management platform in the energy consumption management optimization system in rights 1-7 as well as energy consumption data, capacity generation data and influencing factor data.

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