Electrical control system based on electricity demand

Abstract

The invention discloses an electrical control system based on power demand, which relates to the technical field of industrial automation and comprises a power demand acquisition module, a power demand analysis module, a control strategy module and an energy-saving effect evaluation module. The electricity demand acquisition module is used for collecting and acquiring electricity demand information of various kinds of electric equipment through monitoring equipment; and the electricity demand analysis module is electrically connected with the electricity demand acquisition module. According to the electrical control system provided by the invention, the electricity demand acquisition module can accurately collect and acquire the electricity demand information of various kinds of electric equipment in real time, so that the system can comprehensively and meticulously understand the actual operation condition of the electric equipment, and a reliable data basis is provided for subsequent analysis and processing; collected electricity consumption demand information can be deeply analyzed and processed, and electricity consumption laws and trends are analyzed by identifying electricity consumption peak and low peak periods.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial automation, and specifically relates to an electrical control system based on electricity demand. Background Art

[0002] In the technical field of industrial automation, the energy efficiency management and optimization of electrical control systems have always been a research hotspot. Traditional electrical control systems often operate based on fixed parameters and preset rules, lacking the ability to dynamically respond to actual electricity demand. With the continuous increase in industrial electricity consumption and the rise in energy costs, how to intelligently adjust the operation of electrical control systems according to the actual demand of electrical equipment to achieve energy conservation and consumption reduction has become an urgent problem to be solved;

[0003] Existing electrical control systems have deficiencies in obtaining electricity demand. Usually, they can only rely on manual setting or simple sensor monitoring, and cannot comprehensively and accurately reflect the actual electricity consumption of various electrical equipment. In addition, in the analysis and processing of electricity demand, traditional systems also lack advanced data processing technologies and algorithm support, making it difficult to identify peak and off-peak electricity consumption periods, as well as the electricity consumption patterns and trends of different equipment. This leads to the lack of a scientific basis for formulating and implementing control strategies and the inability to achieve refined electricity management. In response to this, we propose an electrical control system based on electricity demand. Summary of the Invention

[0004] To solve the above technical problems, an electrical control system based on electricity demand is provided, and this technical solution solves the above problems.

[0005] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0006] An electrical control system based on electricity demand, comprising: an electricity demand acquisition module, an electricity demand analysis module, a control strategy module, and an energy-saving effect evaluation module;

[0007] The electricity demand acquisition module is used to collect and obtain the electricity demand information of various electrical equipment through monitoring devices;

[0008] The electricity demand analysis module is electrically connected to the electricity demand acquisition module, and the electricity demand analysis module is used to analyze and process the electricity demand information collected by the electricity demand acquisition module, identify peak and off-peak electricity consumption periods, and analyze the electricity consumption patterns and trends of different equipment;

[0009] The control strategy module is connected to the electricity demand analysis module. The control strategy module is used to generate corresponding electrical control strategies according to the results of the electricity demand analysis module and execute the electrical control strategies generated by the control strategy module to control electricity consumption by controlling the switching of electrical equipment and adjusting the power;

[0010] The energy-saving effect evaluation module and the control strategy module, where the energy-saving effect evaluation module is used to evaluate the energy-saving effect after the control strategy is implemented on the electrical control system.

[0011] Preferably, the power demand acquisition module includes: a sensor unit and a data acquisition unit;

[0012] The sensor unit is used to monitor the current, voltage, and power of the electrical equipment in real time;

[0013] The data acquisition unit is used to collect the data monitored by the sensor unit and transmit it to the power demand analysis module.

[0014] Preferably, the data acquisition unit performs data verification on the collected data:

[0015] Based on the sensor unit, the current, voltage, and power data of the electrical equipment are obtained;

[0016] The collected data is subjected to preliminary processing, and the preliminary processing includes: noise removal and smoothing processing;

[0017] The verification standard is set through the factory equipment parameters of the electrical equipment, and the processed data is compared with the set verification standard:

[0018] For voltage, its verification formula is:

[0019] U min ≤U≤U max

[0020] In the formula, U represents voltage, and U min and U max respectively represent the set lower voltage limit;

[0021] For current, its verification formula is:

[0022] Z min ≤Z≤Z max

[0023] In the formula, Z represents voltage, and Z min and Z max respectively represent the set lower voltage limit;

[0024] For power, the power is indirectly verified according to the verification results of voltage and current;

[0025] If data anomalies are found, an instruction is sent to re-collect the data; if no data anomalies are found, it is transmitted to the power demand analysis module.

[0026] Preferably, the power demand analysis module includes: a peak and valley identification unit, a pattern analysis and trend prediction unit;

[0027] The peak and trough recognition unit is used to statistically analyze the power consumption within a cycle based on the received power consumption data, and identify the peak power consumption period and the low power consumption period;

[0028] The regular pattern analysis and trend prediction unit is used to find out the power consumption pattern of the equipment and predict the change trend of the power consumption of the equipment within a certain period in the future by establishing a model.

[0029] Preferably, identifying the peak power consumption period and the low power consumption period specifically includes:

[0030] Statistically analyze the power consumption data within several time periods, and calculate the average power consumption within each time period. Among them, the calculation formula for the average power consumption is:

[0031] E avg = ∑E v ÷ w

[0032] In the formula, E avg represents the average power consumption, E v represents the power consumption at each time point, and w represents the number of time points within the time period;

[0033] Set a power consumption threshold. When the average power consumption within a certain time period is greater than the threshold, it is determined as the peak power consumption period. When the average power consumption is less than the threshold, it is determined as the low power consumption period.

[0034] Preferably, finding out the power consumption pattern of the equipment and predicting the change trend of the power consumption of the equipment within a certain period in the future by establishing a model specifically includes:

[0035] Collect the historical power consumption data of the equipment, including current, voltage, power, and timestamp information;

[0036] Clean and preprocess the data, remove outliers and noise, and divide the data into a test set and a validation set;

[0037] Build a power consumption model based on the processed data, train it through the test set, and use the validation set to verify the results generated by the training;

[0038] Use the trained model to analyze the power consumption pattern of the equipment, input the latest data into the trained model, and predict the change trend of the power consumption of the equipment within a certain period in the future;

[0039] Use evaluation indicators to evaluate the prediction performance of the model.

[0040] Preferably, the expression for building the power consumption model is:

[0041]

[0042] Wherein, represents the predicted power consumption value at time t, c represents the constant term, and θ j represent the autoregressive coefficient and the moving average coefficient respectively, y t-i represents the actual power consumption at time t-i, ∈ t-j represents the random error term at time t-j, ∈ t represents the random error term at the current time, p represents the order of the autoregressive term, and q represents the order of the moving average term.

[0043] Preferably, the evaluation metrics specifically include: using the root mean square error and the mean absolute error to evaluate the prediction performance of the model;

[0044] Among them, the formula for calculating the root mean square error is:

[0045]

[0046] Wherein, RMSE represents the root mean square error, m represents the number of samples, y j represents the actual power consumption, represents the predicted power consumption;

[0047] Among them, the formula for calculating the mean absolute error is:

[0048]

[0049] Wherein, MAE represents the mean absolute error.

[0050] Preferably, the control strategy module includes: an equipment classification unit, a strategy generation unit, and a strategy optimization unit;

[0051] The equipment classification unit is used to classify electrical equipment into different categories according to factors such as the type, power size, usage frequency, and importance of the electrical equipment;

[0052] The strategy generation unit is used to formulate corresponding control strategies for different categories of electrical equipment by combining the information of peak and off-peak power consumption periods. During peak power consumption periods, non-critical equipment is preferentially turned off or its power is reduced, and during off-peak power consumption periods, some equipment is started for maintenance or charging. The control strategies include the on-off control strategy and the power adjustment strategy of the equipment. The formula for the power adjustment strategy is:

[0053] F new = F old ×(1 - α)

[0054] Wherein, F new represents the adjusted power, F old represents the original power of the equipment, α represents the power adjustment coefficient, and 0 ≤ α ≤ 1;

[0055] The strategy optimization unit is used to optimize and adjust the control strategy according to the feedback information of the energy-saving effect evaluation module, so as to improve the energy-saving effect.

[0056] Preferably, the energy-saving effect evaluation module includes: a data comparison unit, an energy-saving calculation unit, and an effect evaluation unit;

[0057] The data comparison unit is used to obtain the power consumption data before and after the implementation of the control strategy by the electrical control system, including the power consumption and the power consumption rate, and compare the two;

[0058] The energy-saving calculation unit is used to calculate the energy-saving rate according to the comparison result. The formula for calculating the energy-saving rate is:

[0059]

[0060] In the formula, η represents the energy-saving rate, H before represents the power consumption before the implementation of the control strategy, H after represents the power consumption after the implementation of the control strategy;

[0061] The effect evaluation unit is used to evaluate the energy-saving effect according to the energy-saving rate index. When the energy-saving rate is lower than the set threshold, it sends an optimization signal to the control strategy module to prompt it to adjust the control strategy. At the same time, it stores and analyzes the energy-saving effect data to provide a reference basis for subsequent system optimization.

[0062] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0063] The electrical control system proposed by the present invention can collect and obtain the power consumption demand information of various electrical equipment in real time and accurately through the power consumption demand acquisition module, enabling the system to comprehensively and carefully understand the actual operating conditions of the electrical equipment, providing a reliable data basis for subsequent analysis and processing, and being able to deeply analyze and process the collected power consumption demand information. By identifying the peak power consumption period and the low peak power consumption period, and analyzing the power consumption rules and trends of different equipment, it can more accurately grasp the changing characteristics of the power consumption demand, providing strong support for formulating scientific control strategies, achieving the goal of energy conservation and consumption reduction, improving the reliability and stability of the electrical equipment, being able to accurately calculate the energy-saving rate, and quantitatively evaluating the energy-saving effect according to the energy-saving rate index, providing a strong basis for system optimization and improvement, providing an intuitive display of the energy-saving effect for users, and enhancing user satisfaction and trust. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 It is a system module composition diagram of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0065] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments in the following description are only examples, and other obvious variations can be conceived by those skilled in the art.

[0066] Referring to Figure 1 As shown, an electrical control system based on electricity demand includes: an electricity demand acquisition module, an electricity demand analysis module, a control strategy module, and an energy-saving effect evaluation module;

[0067] The electricity demand acquisition module plays a fundamental and crucial role in the entire system. This module is dedicated to comprehensively collecting and accurately obtaining the electricity demand information of various electrical devices through advanced monitoring equipment. The sensor unit therein is like the sharp "scout" of the system, always vigilant, and continuously monitors the current, voltage, and power of electrical devices in real time. These sensors work with extremely high precision and frequency, not missing any subtle changes. Whether it is the high-power operating state of large industrial equipment or the low-power working mode of small office appliances, the sensor unit can accurately capture the relevant data. For example, in a factory workshop, the current sensor can accurately measure the current consumption of various mechanical equipment, the voltage sensor closely monitors the voltage fluctuations of the power supply line, and the power sensor calculates the actual power usage of the equipment in real time. The data acquisition unit is like an efficient "information carrier", which is responsible for promptly collecting the data monitored by the sensor unit. This process requires a high degree of accuracy and reliability to ensure the integrity and authenticity of the data. The data acquisition unit will adopt advanced data transmission technologies to quickly transmit the collected data to the electricity demand analysis module, providing a solid data foundation for subsequent analysis and processing work.

[0068] The electricity consumption demand analysis module is closely electrically connected to the electricity consumption demand acquisition module, and undertakes the important task of deeply analyzing and processing the rich electricity consumption demand information collected by the electricity consumption demand acquisition module. Among them, the peak and trough identification unit is like a shrewd "time analyst". According to the large amount of electricity consumption data received, it conducts a comprehensive and systematic statistical analysis of the electricity consumption within a specific period. It will use complex algorithms and models to carefully analyze the electricity consumption situation in each time period. For example, by statistically analyzing the electricity consumption hour by hour for 24 hours a day, it can find out the peak hours with significantly higher electricity consumption than other periods, and the low peak hours with relatively low electricity consumption. During the identification process, the peak and trough identification unit will consider various factors, such as the operation conditions of different types of equipment in different time periods, seasonal electricity consumption changes, etc. For a commercial office building, the working hours during the day are usually the peak electricity consumption periods because various office equipment, lighting systems, etc. are running; while the nights and weekends are the low peak electricity consumption periods. The regular pattern analysis and trend prediction unit is like a wise "future prophet". It deeply explores the electricity consumption patterns of equipment by establishing a precise model. This model will comprehensively consider factors such as the type of equipment, usage frequency, power size, etc., to find out the electricity consumption patterns of different equipment. For example, some equipment starts and stops regularly within a specific time period, while others dynamically adjust their electricity consumption according to the changes in production tasks. At the same time, this unit will also use advanced prediction algorithms based on historical electricity consumption data to accurately predict the change trend of the electricity consumption of equipment in the future for a period of time. This is crucial for formulating reasonable electricity consumption strategies. For example, equipment maintenance or charging can be arranged in the low peak hours in advance to avoid increasing the electricity consumption burden during the peak hours.

[0069] The control strategy module closely cooperates with the electricity demand analysis module. According to the detailed results of the electricity demand analysis module, it generates corresponding electrical control strategies and executes them firmly. This module is like the "commander" of the system. By controlling the switches of electrical equipment and flexibly adjusting the power, it realizes precise control of electricity consumption. The equipment classification unit plays an important role first. It comprehensively considers multiple factors such as the type, power size, usage frequency, and importance of electrical equipment, and classifies electrical equipment in detail. For example, equipment can be classified into key equipment and non-key equipment, high-power equipment and low-power equipment, frequently used equipment and occasionally used equipment, etc. Such classification helps to formulate more targeted control strategies. The strategy generation unit is like a strategy "designer". For different categories of electrical equipment, it closely combines the key information of peak and off-peak electricity consumption periods and carefully formulates corresponding control strategies. During peak periods, to ensure the stability of power supply and the normal operation of key equipment, non-key equipment is preferentially turned off or its power is reasonably reduced. For example, during the peak electricity consumption period in a factory, the power supply of some auxiliary equipment can be temporarily turned off, or the brightness of the lighting system can be reduced. During off-peak periods, some equipment can be started for maintenance or charging to make full use of the power resources during off-peak periods. The control strategies include the switch control strategy and power adjustment strategy of equipment, and these strategies can be flexibly adjusted according to the actual situation. For example, when the electricity demand suddenly increases, standby equipment can be quickly started or the power of key equipment can be increased; when the electricity demand decreases, unnecessary equipment can be turned off in time or the power of equipment can be reduced.

[0070] The energy-saving effect evaluation module is closely related to the control strategy module and is responsible for comprehensively evaluating the energy-saving effect after the control strategy of the electrical control system is implemented. The data comparison unit is like a rigorous "data inspector". It actively obtains the electricity consumption data before and after the control strategy of the electrical control system is implemented, including important indicators such as electricity consumption and electricity power, and carefully compares the two. Through the comparison, the electricity consumption changes before and after the implementation of the control strategy can be intuitively seen. The energy-saving calculation unit is like an accurate "calculator". Based on the comparison results, it uses scientific calculation formulas to accurately calculate the energy-saving rate. This energy-saving rate is an important indicator to measure the effectiveness of the control strategy. The effect evaluation unit objectively and accurately evaluates the energy-saving effect according to the energy-saving rate index. If the energy-saving rate reaches the expected target, it indicates that the control strategy has achieved good results; if the energy-saving rate is lower than the set threshold, the effect evaluation unit will promptly send an optimization signal to the control strategy module to prompt it to adjust and optimize the control strategy. At the same time, this module will also properly store and deeply analyze the energy-saving effect data, providing valuable reference for subsequent system optimization. For example, by analyzing the energy-saving effects of different time periods and different types of equipment, the direction and potential for further optimization can be found.

[0071] In summary, through the collaborative efforts of various modules, this electrical control system based on electricity demand realizes the intelligent management of electrical equipment and the efficient utilization of energy. It can not only help enterprises and institutions reduce electricity costs and improve energy utilization efficiency, but also make important contributions to the goal of achieving sustainable development. In the future, with the continuous progress of technology and the increasing demand for energy management, this electrical control system is expected to be more widely applied and further developed.

[0072] The data acquisition unit conducts data verification on the collected data:

[0073] The data acquisition unit obtains the current, voltage, and power data of electrical equipment based on the sensor unit. As the source of data, the accuracy and stability of the sensor unit directly affect subsequent analysis and decision-making. These current, voltage, and power data reflect the operating status and energy consumption of electrical equipment.

[0074] Preliminary processing is performed on the collected data. This step mainly includes noise removal and smoothing processing. In the actual environment, the collected data is affected by various interference factors, resulting in noise in the data. The purpose of noise removal is to remove this noise from the data through a series of algorithms and technologies to improve the quality of the data. Smoothing processing is to make the data smoother and reduce data fluctuations for better subsequent analysis.

[0075] The verification standard is set through the factory equipment parameters of electrical equipment. Each electrical equipment has a series of parameters when it leaves the factory, and these parameters can be used as the verification standard for data. Comparing the processed data with the set verification standard is to ensure that the collected data is within a reasonable range. If data anomalies are found, it indicates that the data is incorrect or severely interfered. At this time, the data acquisition unit will send an instruction to re-collect the data to ensure the accuracy of the data:

[0076] For voltage, its verification formula is:

[0077] U min ≤U≤U max

[0078] In the formula, U represents voltage, and U min and U max respectively represent the set lower voltage limit;

[0079] For current, its verification formula is:

[0080] Z min ≤Z≤Z max

[0081] In the formula, Z represents voltage, and Z minand Z max respectively represent the set lower voltage limit;

[0082] For power, the power is indirectly verified according to the verification results of voltage and current;

[0083] If it is found that the data is normal, it will be transmitted to the power consumption demand analysis module. The power consumption demand analysis module will conduct in-depth analysis based on these data to understand the power consumption demand of electrical equipment and provide a basis for optimizing the power consumption strategy.

[0084] In short, the data acquisition unit ensures the accuracy and reliability of the data through strict data verification and preliminary processing of the collected data, providing a solid foundation for subsequent power consumption demand analysis.

[0085] Identifying peak and off-peak power consumption periods specifically includes:

[0086] Collect power consumption data for several time periods, which can be set according to actual needs. For example, it can be different time periods in a day, different dates in a week, or different stages in a month. The power consumption data usually includes power consumption, current, and voltage, which reflect the usage of electrical equipment during a specific time period.

[0087] Conduct statistical analysis on the power consumption data for each time period. An important indicator is to calculate the average power consumption for each time period. The average power consumption can be obtained by dividing the total power consumption in that time period by the length of the time period. For example, if a time period is one hour, then divide the total power consumption in that hour by one hour to get the average power consumption for that hour. Among them, the calculation formula for the average power consumption is:

[0088] E avg = ∑E v ÷ w

[0089] In the formula, E avg represents the average power consumption, E v represents the power consumption at each time point, and w represents the number of time points in the time period;

[0090] Set the power consumption threshold. The setting of this threshold needs to consider multiple factors, such as historical power consumption data, types and quantities of electrical equipment, seasonal changes, etc. Generally speaking, a reasonable power consumption threshold can be determined by analyzing the power consumption data in the past period.

[0091] When the average power consumption in a certain period of time is greater than the threshold, it is determined to be a peak period. During peak periods of power consumption, the power demand is large, which will lead to problems such as increased pressure on the power grid and rising electricity prices. At this time, some measures can be taken to reduce power consumption, such as adjusting the use time of power-consuming equipment and optimizing the operation mode of power-consuming equipment.

[0092] When the average power consumption is less than the threshold, it is determined to be a low-peak period. During the low-peak period, the power demand is relatively small, and some preferential policies, such as time-of-use electricity prices, can be considered to reduce the cost of electricity. At the same time, some high-power electrical equipment can also be operated during this period to make full use of power resources.

[0093] In short, statistical analysis of electricity consumption data over several time periods and setting electricity consumption thresholds to determine peak and off-peak periods can help better manage electricity demand, improve the efficiency of power resource utilization, and reduce electricity costs.

[0094] By building a model, we can find out the power consumption patterns of the equipment and predict the power consumption trend of the equipment in the future based on the historical power consumption data. Specifically, we can:

[0095] It is necessary to collect the historical power consumption data of the equipment, including current, voltage, power and timestamp information. These data can fully reflect the power consumption of the equipment at different time points, providing a basis for subsequent analysis and prediction. By recording various electrical parameters during the operation of the equipment, rich power consumption information can be obtained, so as to better understand the power consumption behavior of the equipment.

[0096] Clean and preprocess the collected data. In the actual data collection process, outliers and noise may occur due to various reasons. Outliers are caused by sensor failure, data transmission errors, etc., while noise comes from factors such as environmental interference. In order to ensure the quality and reliability of the data, these outliers and noise need to be removed. There are many methods for data cleaning and preprocessing, such as using statistical methods to detect outliers and remove them, or removing noise through filtering technology. At the same time, the cleaned and preprocessed data is divided into a test set and a validation set. The test set is used for model training, while the validation set is used to verify the results generated by the training to ensure the accuracy and reliability of the model.

[0097] Build an electricity consumption model based on the processed data. This process requires selecting appropriate model architectures and algorithms to adapt to the electricity consumption characteristics and data features of the equipment. Machine learning algorithms such as linear regression, decision trees, random forests, etc., or deep learning algorithms such as neural networks, etc., can be considered. Train the model using a test set and continuously adjust the model's parameters to improve the model's prediction performance. During the training process, techniques such as cross-validation can be used to prevent overfitting and ensure that the model has good generalization ability.

[0098] Use the trained model to analyze the electricity consumption pattern of the equipment. Input the latest data into the trained model, and the model can predict the change trend of the equipment's electricity consumption in the future period based on historical data and the currently input data. This is very helpful for the electricity consumption management and energy-saving optimization of the equipment, and can make advance electricity consumption plans to avoid power shortages and excessive electricity bills during peak electricity consumption periods.

[0099] Evaluate the prediction performance of the model using evaluation metrics. The evaluation metrics can include root mean square error, mean absolute error, coefficient of determination, etc. By comparing the model's prediction results with the actual data and calculating the values of the evaluation metrics, the accuracy and reliability of the model can be objectively evaluated. If the evaluation results are not satisfactory, the model's parameters can be further adjusted or different model architectures can be selected to improve the model's prediction performance.

[0100] The expression for building the electricity consumption model is:

[0101]

[0102] In the formula, represents the predicted value of the electricity consumption at time t, c represents the constant term, and θ j represent the autoregressive coefficient and the moving average coefficient respectively, y t-i represents the actual electricity consumption at time t - i, ∈ t-j represents the random error term at time t - j, ∈ t represents the random error term at the current time, p represents the order of the autoregressive term, and q represents the order of the moving average term.

[0103] The use of evaluation metrics specifically includes: using the root mean square error and the mean absolute error to evaluate the prediction performance of the model;

[0104] Among them, the formula for calculating the root mean square error is:

[0105]

[0106] In the formula, RMSE represents the root mean square error, m represents the number of samples, y j represents the actual electricity consumption, Represents the predicted power consumption;

[0107] Among them, the calculation formula for the mean absolute error is:

[0108]

[0109] In the formula, MAE represents the mean absolute error.

[0110] Its power adjustment strategy formula is:

[0111] F new = F old ×(1 - α)

[0112] In the formula, F new represents the adjusted power, F old represents the original power of the device, α represents the power adjustment coefficient, 0 ≤ α ≤ 1;

[0113] The strategy optimization unit is used to optimize and adjust the control strategy according to the feedback information of the energy-saving effect evaluation module to improve the energy-saving effect.

[0114] The energy-saving rate calculation formula is:

[0115]

[0116] In the formula, η represents the energy-saving rate, H before represents the power consumption before implementing the control strategy, H after represents the power consumption after implementing the control strategy.

[0117] The usage process of the present invention is as follows: obtaining the demand information of the electrical equipment, analyzing and processing the electrical data, generating and executing the electrical control strategy, evaluating the energy-saving effect, and optimizing and adjusting the control strategy.

[0118] To sum up, the advantages of the present invention are as follows: The power consumption demand acquisition module collects the current, voltage, and power parameters of the electrical equipment, providing a reliable data basis for subsequent analysis. The power consumption demand analysis module analyzes the collected information, identifies peak and off-peak periods, grasps the power consumption pattern and trend, and provides support for the control strategy. The control strategy generation module intelligently generates a strategy based on the analysis results. The strategy execution module precisely controls the electrical equipment, turning off non-critical equipment or reducing power during peak periods, and starting partial equipment maintenance or charging during off-peak periods. The energy-saving effect evaluation module compares the electrical data before and after the control strategy, calculates the energy-saving rate, and quantitatively evaluates the energy-saving effect, providing an intuitive display for system optimization and users.

[0119] The foregoing has shown and described the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and what is described in the above embodiments and the specification is only the principle of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements fall within the scope of the present invention claimed. The scope of protection required by the present invention is defined by the appended claims and their equivalents.

Claims

1. An electrical control system based on power demand, characterized in that: include: Power demand acquisition module, power demand analysis module, control strategy module, energy-saving effect evaluation module; The power demand acquisition module is used to collect and obtain power demand information of various power-consuming devices through monitoring equipment; The power demand analysis module is electrically connected to the power demand acquisition module, and the power demand analysis module is used to analyze and process the power demand information collected by the power demand acquisition module, identify peak power consumption periods and off-peak power consumption periods, and analyze power consumption patterns and trends of different devices; A control strategy module and the power demand analysis module, wherein the control strategy module is used to generate a corresponding electrical control strategy according to the result of the power demand analysis module and execute the electrical control strategy generated by the control strategy module, and control the power consumption by controlling the switch of the electrical equipment and adjusting the power; The energy-saving effect evaluation module and the control strategy module are used to evaluate the energy-saving effect after the electrical control system implements the control strategy.

2. An electrical control system based on power demand according to claim 1, characterized in that: The electricity demand acquisition module includes: a sensor unit and a data acquisition unit; The sensor unit is used to monitor the current, voltage and power of the electrical equipment in real time; The data acquisition unit is used to collect the data monitored by the sensor unit and transmit it to the power demand analysis module.

3. An electrical control system based on power demand according to claim 2, characterized in that: The data acquisition unit performs data verification on the collected data: Acquire current, voltage and power data of electrical equipment based on sensor units; Performing preliminary processing on the collected data, wherein the preliminary processing includes: noise removal and smoothing; Set the calibration standard through the factory equipment parameters of the power-consuming equipment, and compare the processed data with the set calibration standard: For voltage, the calibration formula is: IN min ≤U≤U max In the formula, U represents voltage, U min and U max Respectively represent the set voltage lower limit; For current, the verification formula is: WITH min ≤Z≤Z max In the formula, Z represents voltage, Z min and Z max Respectively represent the set voltage lower limit; For power, the power is indirectly verified based on the verification results of voltage and current; If the data is found to be abnormal, an instruction is sent to re-collect the data; if the data is found to be normal, it is transmitted to the power demand analysis module.

4. The electrical control system based on power demand according to claim 1, characterized in that: The electricity demand analysis module includes: peak and valley identification unit, rule analysis and trend prediction unit; The peak and valley identification unit is used to perform statistical analysis on the power consumption within a cycle based on the received power consumption data, and identify the peak and low peak periods of power consumption; The rule analysis and trend prediction unit is used to find out the power consumption rules of the equipment by establishing a model and to predict the power consumption trend of the equipment in the future based on the historical power consumption data.

5. An electrical control system based on power demand according to claim 4, characterized in that: Identify peak and off-peak hours for electricity consumption, including: Perform statistical analysis on the electricity consumption data in several time periods and calculate the average electricity consumption in each time period. The average electricity consumption calculation formula is: AND avg =∑E v ÷w In the formula, E avg Indicates the average power consumption, E v represents the power consumption at each time point, and w represents the number of time points in the time period; Set a power consumption threshold. When the average power consumption in a certain time period is greater than the threshold, it is determined to be a peak power consumption period. When the average power consumption is less than the threshold, it is determined to be a low power consumption period.

6. The electrical control system based on power demand according to claim 4, characterized in that: By building a model, we can find out the power consumption patterns of the equipment and predict the power consumption trend of the equipment in the future based on the historical power consumption data. Specifically, we can: Collect historical power usage data of the device, including current, voltage, power and timestamp information; Clean and preprocess the data to remove outliers and noise, and divide the data into test and validation sets; Build an electricity consumption model based on the processed data, train it with the test set, and verify the training results with the validation set; Use the trained model to analyze the power consumption patterns of the equipment, input the latest data into the trained model, and predict the power consumption trend of the equipment in the future. Use evaluation metrics to assess the predictive performance of the model.

7. An electrical control system based on power demand according to claim 6, characterized in that: The expression for constructing the electricity consumption model is: In the formula, represents the predicted power consumption at time t, c represents the constant term, and θ j Represent the autoregressive coefficient and the moving average coefficient, y t-i represents the actual power consumption at time ti, ∈ t-j represents the random error term at time tj, ∈ t represents the random error term at the current time, p represents the order of the autoregressive term, and q represents the order of the moving average term.

8. The electrical control system based on power demand according to claim 6, characterized in that: The evaluation indicators used specifically include: using root mean square error and mean absolute error to evaluate the prediction performance of the model; The root mean square error calculation formula is: In the formula, RMSE represents the root mean square error, m represents the number of samples, and y j Indicates the actual power consumption. Indicates the predicted electricity consumption; The mean absolute error calculation formula is: Where MAE stands for mean absolute error.

9. The electrical control system based on power demand according to claim 1, characterized in that: The control strategy module includes: equipment classification unit, strategy generation unit and strategy optimization unit; The equipment classification unit is used to classify electrical equipment into different categories according to factors such as the type, power, frequency of use and importance of the electrical equipment; The strategy generation unit is used to formulate corresponding control strategies for different types of power-consuming equipment, combined with information from peak and off-peak periods. Non-critical equipment is shut down or its power is reduced during peak periods, and some equipment is started for maintenance or charging during off-peak periods. The control strategy includes the switch control strategy and power regulation strategy of the equipment. The power regulation strategy formula is: F new =F old ×(1-a) In the formula, F new Indicates the regulated power, F old Indicates the original power of the equipment, α indicates the power adjustment coefficient, 0≤α≤1; The strategy optimization unit is used to optimize and adjust the control strategy according to the feedback information of the energy-saving effect evaluation module to improve the energy-saving effect.

10. The electrical control system based on power demand according to claim 1, characterized in that: The energy-saving effect evaluation module includes: a data comparison unit, an energy-saving calculation unit and an effect evaluation unit; The data comparison unit is used to obtain the power consumption data before and after the electrical control system implements the control strategy, including power consumption and power consumption, and compare the two; The energy-saving calculation unit is used to calculate the energy-saving rate according to the comparison result. The energy-saving rate calculation formula is: In the formula, η represents the energy saving rate, H before represents the power consumption before the implementation of the control strategy, H after Indicates the power consumption after the control strategy is implemented; The effect evaluation unit is used to evaluate the energy-saving effect according to the energy-saving rate index. When the energy-saving rate is lower than the set threshold, an optimization signal is sent to the control strategy module to prompt it to adjust the control strategy. At the same time, the energy-saving effect data is stored and analyzed to provide a reference basis for subsequent system optimization.

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