Energy consumption data real-time monitoring method for smart park

Abstract

The invention relates to the technical field of data processing, in particular to an energy consumption data real-time monitoring method for a smart park, and the method comprises the steps: collecting power energy consumption data, and carrying out the preliminary prediction based on historical data through employing an ARIMA model; historical data are divided into a plurality of segments through mutation point detection, the cumulative change rate and the variation coefficient of each segment are calculated, and then the change trend indexes and trend types of the segments are determined; in combination with the similarity between the current data and each segment, judging the change trend type of the current data; dynamically adjusting an autoregression coefficient and a moving average coefficient of the ARIMA model according to a trend type and residual information; and finally, predicting by using the adjusted model, and realizing energy consumption abnormity monitoring through the deviation between a predicted value and an actual value. According to the method, the accuracy of energy consumption prediction in a changing complex scene is improved, and the accuracy of energy consumption monitoring is improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and more particularly to a method for real-time monitoring of energy consumption data in a smart park. Background Art

[0002] In the overall operation system of the smart park, power consumption monitoring occupies a key position. Real-time monitoring and accurate prediction of power consumption data are the core elements for optimizing energy allocation, reducing operating costs and improving the overall operating efficiency of the park. It not only helps to rationally plan energy supply and avoid energy waste, but also can detect potential energy-related problems in advance and ensure the stable operation of the park.

[0003] Time series prediction algorithms are usually used in the process of data anomaly monitoring. The ARIMA (Autoregressive Integrated Moving Average) prediction model is a typical representative of time series prediction methods. The core idea is to predict future data based on historical data, and judge whether the data is abnormal based on the difference between the predicted value and the actual value, thereby realizing data anomaly monitoring.

[0004] However, the scenarios in smart parks are extremely complex and full of uncertainties. For example, when a large-scale equipment fails suddenly, the power consumption will change dramatically in an instant; the temporary holding of large-scale events will cause the power demand in the park to rise sharply in a short period of time, and the power consumption will also increase; the interruption and restoration of energy supply will cause abnormal fluctuations in power consumption. These emergencies cause sudden changes in power consumption, which seriously undermines the stability of the data. The ARIMA model is highly dependent on the stability and regular periodicity of the data, and has a lag effect on the impact of these emergencies. It is difficult to accurately capture the actual change trend of the data, resulting in a large deviation between the prediction results and the actual power consumption data, affecting the accuracy of power consumption data monitoring. Summary of the invention

[0005] In order to solve the problem that the ARIMA prediction model in the prior art has a delayed response when processing sudden changes in power consumption data, resulting in large deviations in the prediction results and inability to accurately monitor power consumption, the present invention proposes a real-time monitoring method for energy consumption data in a smart park. The method includes: Collect the power consumption data of the smart park in real time, and use the ARIMA forecasting model to obtain the predicted value of each power consumption data based on the historical power consumption data of each power consumption data; Taking the power consumption data at any time as the current power consumption data, performing mutation point detection on the historical power consumption data of the current power consumption data, and dividing the historical power consumption data into multiple segments according to the positions of the mutation points; Calculate the cumulative change rate and coefficient of variation of the power consumption data for each segment respectively, determine the change trend index for each segment based on the cumulative change rate and coefficient of variation, and determine the change trend type for each segment according to the change trend index; Determine the change trend type of the current power consumption data based on the similarity between the current power consumption data and each segment; dynamically adjust the autoregressive coefficient and moving average coefficient of the ARIMA prediction model according to the change trend type of the current power consumption data, the residual, and the residual of the historical power consumption data; where the residual is the difference between the actual value and the predicted value; Predict the predicted value of the next power consumption data of the current power consumption data based on the adjusted ARIMA model, and perform energy consumption anomaly monitoring according to the difference between the predicted value and the actual value.

[0006] This technical solution first constructs an ARIMA model based on historical power consumption data to achieve preliminary prediction, providing a basis for subsequent error feedback; secondly, introduces a mutation point detection mechanism to divide non-stationary data into multiple segments with stable structures, improving the ability to identify internal change patterns of the data; furthermore, constructs a change trend index by combining the cumulative change rate and coefficient of variation of the segments, and divides them into different trend types, providing a standard for similarity matching of the current data trend; subsequently, determines the change trend type of the current power consumption data based on similarity, and dynamically adjusts the autoregressive coefficient and moving average coefficient of the ARIMA model in combination with the residual of the current power consumption data and the residual of the historical power consumption data, realizing trend perception and error-driven optimization of the prediction model parameters; finally, makes a more accurate prediction of future energy consumption data based on the adjusted model, and completes real-time monitoring of energy consumption anomalies through the comparison between the predicted value and the actual value, improving the adaptability and prediction accuracy of the traditional prediction model for sudden and non-stationary power consumption data, and improving the accuracy of power consumption data monitoring.

[0007] Furthermore, the process of dynamically adjusting the autoregressive coefficient and moving average coefficient of the ARIMA prediction model is as follows: judge whether the prediction error of the current power consumption data is 0. If it is 0, retain the initial autoregressive coefficient and initial moving average coefficient of the ARIMA prediction model; if it is not 0, further judge the change trend type of the current power consumption data. If the change trend type is a sharp rise type or a sharp drop type, adjust the initial autoregressive coefficient. If the change trend type is a fluctuating type, adjust the initial moving average coefficient.

[0008] This technical solution provides a set of dynamic adjustment processes. By introducing a prediction error judgment mechanism and a trend type recognition mechanism, it realizes the differential adjustment of the parameters of the ARIMA model. This differential adjustment mechanism ensures that the prediction model can adaptively adjust the parameters of the prediction model when facing power consumption data with different change trends, thereby improving the accuracy of the prediction results.

[0009] Further, the similarity between the current power consumption data and each segment is determined based on the following method: Let the lengths of each segment of the current power consumption data be , until , where is the total number of segments; starting from the current power consumption data, in the reverse order, divide forward according to the lengths of , until to obtain matched segments to be matched, and all segments and segments to be matched are corresponding based on length; calculate the DTW distance between each segment to be matched and its corresponding segment, and take the reciprocal of the DTW distance as the similarity between each segment to be matched and its corresponding segment.

[0010] This technical solution constructs segments to be matched with the same length as the segments, and uses the dynamic time warping algorithm to measure the similarity index between it and the historical segments, so as to achieve trend matching under different time scales on the basis of time series alignment. It not only fully retains the structural characteristics of the segments, avoids comparison deviations caused by inconsistent lengths, but also improves the accuracy of identifying the change trend of the current power consumption data.

[0011] Further, the change trend type of each segment is determined by the quartile method based on the change trend indicators of all segments, including: when the change trend indicator of a certain segment is less than the first quartile of the change trend indicators of all segments, this segment is determined to be a sharp drop type; when the change trend indicator of a certain segment is greater than the third quartile of the change trend indicators of all segments, this segment is determined to be a sharp rise type; when the change trend indicator of a certain segment is greater than or equal to the first quartile and less than or equal to the third quartile, this segment is determined to be a fluctuating type.

[0012] Further, the method for detecting mutation points in the historical power consumption data of the current power consumption data is: calculate the absolute value of the first-order difference value between every two adjacent power consumption data in the historical power consumption data to construct a first-order difference sequence; set the dynamic threshold range as the mean value of the first-order difference sequence Using 1.5 times the standard deviation, detect the extreme points of the first-order difference sequence, identify the extreme points outside the dynamic threshold range as mutation points, and identify the extreme points within the dynamic threshold range as non-mutation points and eliminate them.

[0013] Furthermore, the method for monitoring energy consumption anomalies based on the difference between the predicted value and the actual value is as follows: Calculate the relative error between the predicted value and the actual value of the next power consumption data of the current power consumption data; if the relative error exceeds the preset relative error range, it is determined that the next power consumption data is abnormal; if the relative error does not exceed the preset relative error range, it is determined that the next power consumption data is not abnormal.

[0014] Furthermore, the change trend index of each segment is determined by the ratio of the cumulative change rate of the power consumption data of the segment to the coefficient of variation, where the coefficient of variation is the ratio of the standard deviation to the mean of the power consumption data of the segment.

[0015] Furthermore, the adjustment of the initial autoregressive coefficient is based on the following formula: ; In the formula, is the adjusted autoregressive coefficient of the prediction model at time, is the initial autoregressive coefficient of the prediction model at time, is the change trend type of the power consumption data at time, is the residual of the power consumption data at time, is the sign function, is the mean of the absolute values of the residuals of all historical power consumption data at time, is the standard deviation of the absolute values of the residuals of all historical power consumption data at time,

[0016] This technical solution realizes the dynamic optimization of the autoregressive term of the prediction model by integrating the directionality, amplitude deviation degree and trend type factor of the current residual, enabling the prediction model to adaptively amplify or reduce the autoregressive coefficient, thereby enhancing the response ability to the trend change of power consumption data, and improving the accuracy and robustness of the prediction by introducing the dual regulation of trend drive and error drive.

[0017] Further, the adjustment of the initial moving average coefficient is based on the following formula: ; In the formula, is the adjusted initial moving average coefficient of the prediction model at moment, is the initial moving average coefficient of the prediction model at moment, is the standard deviation of the absolute value of the residual of the power consumption data of the first segment before the power consumption data at is the standard deviation of the absolute value of the residual of the power consumption data of the second segment before the power consumption data at

[0018] This technical solution is based on the change of the residual fluctuation levels of two adjacent historical segments. By introducing the ratio of the square difference of the standard deviation to the square of the standard deviation of the previous segment, the dynamic correction of the moving average term is realized. The correction strategy combines the change of the historical residuals of the power consumption data, endows the model with the adaptive ability to the local fluctuation intensity, and improves the prediction stability and abnormal recognition accuracy of the prediction model in the case of power consumption data fluctuation.

[0019] Further, the method for determining the change trend type of the current power consumption data is as follows: If the similarity between a certain segment to be matched and its corresponding segment is the largest, the change trend type of the corresponding segment is used as the change trend type of the current power consumption data.

[0020] The present invention has the following effects: By introducing a trend recognition mechanism based on segmentation at mutation points and a dynamic parameter adjustment method driven by residuals, the present invention realizes the rapid response and adaptive correction of the ARIMA prediction model to mutation data in the complex energy consumption scenario of a smart park, improves the processing ability of the prediction model for non-stationary data, and by dynamically adjusting the model parameters, improves the accuracy of the prediction results and enhances the accuracy and reliability of power consumption anomaly monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is a schematic flowchart of the method of the present invention; Figure 2 is a schematic flowchart of the method for dynamically adjusting parameters in step S5 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention.

[0023] A real-time monitoring method for energy consumption data in a smart park provided by the present invention is as follows Figure 1 As shown in S1: Real-time collect the power energy consumption data of the smart park, and use the prediction model to obtain the predicted value of each power energy consumption data based on the initial parameters.

[0024] The power energy consumption data of the smart park is usually affected by various factors such as weather, holidays, and peak periods of equipment use, so it may show obvious sudden increases, sudden decreases, or fluctuating trends. Especially when the equipment load suddenly increases or the air conditioning or heating load suddenly increases due to weather changes, the power energy consumption data will fluctuate violently.

[0025] To accurately monitor the power energy consumption data, it is set to collect the power energy consumption data once a minute to improve the real-time perception ability of the energy consumption fluctuations in the park. Then, taking any moment as the current moment, the power energy consumption data at the current moment is used as the current power energy consumption data, and all the power energy consumption data collected within one day before the current moment is used as all the historical power energy consumption data of the current power energy consumption data, that is, 1440 historical power energy consumption data before each current power energy consumption data.

[0026] Next, these 1440 data points are divided into multiple subsequences in chronological order, with 50 data in each group, and a total of 28 groups of historical samples are obtained for the training of the prediction model.

[0027] The core idea of the prediction model is to obtain the predicted value of each power energy consumption data based on all the historical power energy consumption data of each power energy consumption data. This prediction model consists of an autoregressive part and a moving average part.

[0028] The core of the autoregressive part is the autoregressive coefficient , which is used to measure the linear correlation degree between the current data and the historical data. In the energy consumption scenario of the smart park, the power energy consumption data changes complexly and is affected by various factors. The autoregressive coefficient determines the strength of the linear relationship between the current observation value and the past observation values. The larger the coefficient, the greater the influence of the current value by the past values, and the higher the dependence degree of the model on the historical data; conversely, the smaller the coefficient, the smaller the influence of the historical data on the current value. A suitable autoregressive coefficient can make the prediction result of the model stable and converge to the true value. For example, in the prediction of power energy consumption data, if the autoregressive coefficient is too large, the predicted value may overly rely on the historical rising data, resulting in the continuous overestimation of the future energy consumption; if the coefficient is too small, the model will underutilize the historical data, and the prediction result may be too smooth to capture the changes in energy consumption in time.

[0029] The core of the moving average part is the moving average coefficient , mainly used to process the random fluctuation part in power consumption data. A larger moving average coefficient means that the model will pay more attention to recent residuals, be more sensitive to changes in new data, and can adjust the prediction results faster to adapt to new information. On the contrary, a smaller moving average coefficient will make the model respond more slowly to new information, and the prediction results will be relatively more stable. Therefore, when the energy supply in the park is unstable or the operating status of equipment changes frequently, resulting in fluctuations in power consumption data, the moving average coefficient can enable the model to adjust the prediction according to the degree of fluctuation, making the prediction results more in line with the actual energy consumption changes, improving the accuracy and stability of the prediction, and thus effectively coping with the uncertainty of power consumption data.

[0030] The specific formula is:

[0031] In this formula, is the predicted value at time The autoregressive part, by multiplying the observed values at the past time points by the corresponding autoregressive coefficients and summing them up, captures the long-term trend and autocorrelation in the time series. The moving average part is used to correct the predicted value, mainly to eliminate short-term fluctuations and random noise in the time series. Among them, is the number of autoregressive terms, equal to the number of historical data of selected when predicting data . is the actual value of the th data before data , is 's autoregressive coefficient, equivalent to a weight. The larger the autoregressive coefficient, the stronger the dependence of the prediction result on , and vice versa. is the number of moving average terms, equal to the number of residuals of the historical data of selected when predicting data . Set (empirical value) here to fully capture the long-term trend and short-term disturbances in the data. is the th residual (actual value minus predicted value) of the data before data , is 's moving average coefficient, is the 's random error term.

[0032] Specifically, based on 28 sets of historical samples, the autoregressive coefficient and the moving average coefficient are estimated by the maximum likelihood estimation method to obtain the initial parameters of the ARIMA model. and , a total of and are obtained.

[0033] Finally, 50 historical power consumption data before each current power consumption data are selected as inputs, and the predicted values are calculated using the initialized ARIMA model. When predicting the next power consumption data for each current power consumption data, it is still based on 50 historical power consumption data of the next power consumption data, and the autoregressive coefficient and the moving average coefficient are respectively and sequentially assigned as and , and then the predicted values are calculated. During the prediction process, the autoregressive coefficient of the current power consumption data is , and the moving average coefficient is .

[0034] Since it is considered that this prediction method has a lag effect on emergencies and is likely to lead to inaccurate prediction results, therefore, when predicting the next power consumption data, the present invention will analyze the change trend of the current power consumption data, and then dynamically adjust its autoregressive coefficient and moving average coefficient to obtain more accurate predicted values.

[0035] S2: Detect and segment the mutation points of the historical power consumption data of the current power consumption data.

[0036] To improve the accuracy and robustness of power consumption data prediction, relying solely on the overall historical data (historical power consumption data within a day) for modeling often makes it difficult to cope with drastic fluctuations or structural changes. In practical applications, the power load often mutates due to emergencies such as equipment start-stop, sudden weather changes, and changes in holiday electricity consumption habits. These mutations often break the original trend and periodicity, resulting in the failure of the prediction model trained based on global data. Therefore, it is necessary to identify the mutation points in the power consumption data before modeling and segment the historical data accordingly, so as to achieve more targeted model parameter adjustment and trend analysis.

[0037] In one embodiment, the mutation point detection and segmentation are performed according to the following method: Obtain all the historical power consumption data of the current power consumption data and sort them based on the chronological order. Among all the historical power consumption data, calculate the first-order difference values of every two adjacent historical power consumption data to obtain a first-order difference sequence composed of the first-order difference values. By calculating the first-order difference values, the instantaneous changes in the power load can be revealed.

[0038] Next, extreme point detection is performed on the first-order difference sequence. Considering the situation of "false extremes" formed by normal minor fluctuations in the data, the dynamic threshold range is set to the mean of the first-order difference sequence 1.5 times the standard deviation of the first-order difference sequence. According to the dynamic threshold range, non-significant mutation points are filtered out, that is, the extreme points within the dynamic threshold range are excluded because the mutation characteristics of these data are not significant. At the same time, the extreme points outside the dynamic threshold range are used as mutation points, and these data have significant mutation characteristics.

[0039] Finally, according to the positions of the mutation points of all historical power consumption data of the current power consumption, all historical power consumption data are divided into multiple segments. Specifically, each segment is between two adjacent mutation points.

[0040] In summary, through mutation point detection and segmentation, the structural change regions in the data can be effectively extracted, making the subsequent trend recognition, parameter dynamic adjustment, and anomaly response mechanisms more targeted and sensitive, and improving the adaptability of the overall prediction system to complex environmental changes.

[0041] S3: Calculate the change trend index based on the cumulative change rate and coefficient of variation of the power consumption data of each segment, and determine the change trend type of this segment.

[0042] In the energy consumption management scenario of a smart park, the power consumption data will show different types of change trends due to various factors, but mainly can be divided into three types: sudden increase type, sudden decrease type, and fluctuation type.

[0043] For example, when a large conference or event is held in the park, the electricity load may rise rapidly within a short period of time, showing a sudden increase type trend; when some electrical equipment suddenly shuts down or fails, the power consumption data will drop rapidly, showing a sudden decrease type; in addition, when the energy supply is unstable or the operating state of the equipment fluctuates frequently, although the power consumption data does not increase or decrease significantly, it will fluctuate frequently within a certain range, and at this time it is classified as the fluctuation type.

[0044] In order to conduct a refined analysis of the change trends of the historical power consumption data of the current power consumption data, in this step, first calculate the change trend index of each segment according to the cumulative change rate and coefficient of variation of the power consumption data included in each segment of the historical power consumption data, and then determine whether the change trend type of this segment belongs to the sudden increase type, sudden decrease type, or fluctuation type according to the change trend index.

[0045] In one embodiment, for any segment of the historical power consumption data, the change trend index of this segment is quantified by the ratio of the cumulative change rate to the coefficient of variation of the power consumption data of this segment, and is expressed by the formula:

[0046] In the formula, is the change trend index of the power consumption data for this segment, is the th power consumption data for this segment, is the serial number of the power consumption data for this segment, is the total number of the power consumption data for this segment, is the coefficient of variation of the power consumption data for this segment, which is equal to the ratio of the standard deviation to the mean of the power consumption data for this segment, is a constant to prevent the denominator from being 0, and its value is .

[0047] The numerator part of this formula is the cumulative change rate of all the power consumption data for this segment, which can more finely reflect the change trend of the power consumption data within the entire segment. If the cumulative change rate is positive and large, it indicates that the power consumption data as a whole shows an upward trend within this segment, and the upward amplitude is large; if the cumulative change rate is negative and the absolute value is large (the smaller the negative number), it indicates that the power consumption data as a whole shows a downward trend, and the downward amplitude is large; if the cumulative change rate is close to 0, it indicates that the overall change trend of the power consumption data within this segment is not obvious. The denominator part of this formula reflects the degree of fluctuation of the power consumption data within this segment through the coefficient of variation. The larger the coefficient of variation, the greater the degree of fluctuation of the power consumption data for this segment, and the lower the credibility of the change trend, and vice versa.

[0048] The change trend index for each segment is used to measure the strength of the change trend of the power consumption data for that segment. Specifically: If the cumulative change rate of the power consumption data for a segment is large, it indicates that there is an obvious upward trend in the power consumption data within that segment. If at the same time the degree of fluctuation is smaller, it indicates that the credibility of the upward trend is higher, and the more likely the change trend type for that segment is a sharp rise type, and the larger the change trend index. If the cumulative change rate of the power consumption data for a segment is small, it indicates that there is an obvious downward trend in the power consumption data within that segment. If at the same time the degree of fluctuation is smaller, it indicates that the credibility of the downward trend is higher, and the more likely the change trend type for that segment is a sharp drop type, and the smaller the change trend index.

[0049] In one embodiment, the determination method for the change trend type of each segment is as follows: Obtain the change trend indicators of all segments, which are divided by the quartile method; when the change trend indicator of a certain segment is less than the first quartile of the change trend indicators of all segments, this segment is determined to be a sharp decline type; when the change trend indicator of a certain segment is greater than the third quartile of the change trend indicators of all segments, this segment is determined to be a sharp rise type; the remaining segments are determined to be fluctuating types.

[0050] S4: Determine the change trend type of the current power consumption data by analyzing the similarity between the current power consumption data and each segment.

[0051] In order to quickly judge and dynamically respond to the change trend of the current power consumption data, and improve the accuracy and real-time performance of trend recognition, this step determines the change trend type to which the current power consumption data belongs based on the similarity matching mechanism between historical segment data and current data. Compared with the method directly based on model prediction, by matching known trend samples, the misjudgment rate can be effectively reduced, the recognition ability of the system for sudden trend changes and the adaptability of the prediction model can be improved, and the response ability of the intelligent park energy consumption management system to the dynamic environment can be enhanced.

[0052] Let the lengths of each segment of the current power consumption data be 、 until where is the total number of segments; starting from the current power consumption data, divide forward in the order from back to front according to the lengths of 、 until to obtain matched segments to be matched, and all segments and segments to be matched are corresponding based on length, that is, the first segment to be matched corresponds to the first segment, the th segment to be matched corresponds to the th segment.

[0053] Calculate the DTW (Dynamic Time Warping) distance between each segment to be matched and its corresponding segment, and take the reciprocal of the DTW distance as the similarity between each segment to be matched and its corresponding segment.

[0054] Then determine the change trend type of the current power consumption data based on the principle of maximum similarity matching: If the similarity between a certain segment to be matched and its corresponding segment is the largest, take the change trend type of this corresponding segment as the change trend type of the current power consumption data.

[0055] S5: Dynamically adjust the parameters of the prediction model according to the change trend type of the current power consumption data and the prediction error of the prediction model.

[0056] To improve the response ability and residual correction ability of the prediction model to different types of trend changes, this step dynamically adjusts the autoregressive coefficient and moving average coefficient of the model by jointly considering the trend change type of the current power consumption data and the prediction residual information, so as to enhance the adaptability and robustness of the model to trend errors. Compared with the traditional static model parameter setting, this method can significantly reduce the prediction lag and overshoot phenomena, and improve the accuracy and stability of energy consumption prediction.

[0057] The residual reflects the magnitude of the prediction error of the prediction model and is a key indicator to measure the prediction accuracy of the model. It is specifically equal to the actual value minus the predicted value. By first obtaining the residual of the current power consumption data and the residuals corresponding to all historical power consumption data of the current power consumption data, and dynamically adjusting the parameters in combination with the trend change type and residual of the current power consumption data, as well as the residuals of the historical power consumption data, the model can capture the data trend changes more accurately.

[0058] When the residual of the current power consumption data is 0, it indicates that the prediction result of the prediction model is accurate. Whether the trend of the current power consumption data is a sharp drop trend or a fluctuation, there is no need to adjust the autoregressive coefficient and the moving average coefficient, and the existing prediction mode of the prediction model is maintained.

[0059] When the residual of the current power consumption data is not 0, it means that the prediction result of the prediction model is not accurate enough. Then it is necessary to discuss in different cases and set different adjustment strategies, which are specifically as follows: If the trend change type of the current power consumption data is a sharp rise type and the residual of the current power consumption data is positive, it means that the upward trend of the predicted value of the power consumption data lags behind the upward trend of the actual value, and there is a lag effect. It is necessary to increase the autoregressive coefficient and strengthen the dependence on the current power consumption data (the dependence on the sharp rise trend) to more accurately capture the actual trend of the data and reduce the problem of too small prediction results.

[0060] If the trend of the current power consumption data is a sharp rise type and the residual of the current power consumption data is negative, it means that the upward trend of the predicted value of the power consumption data is ahead of the upward trend of the actual value. It is necessary to reduce the autoregressive coefficient and reduce the excessive dependence on the current power consumption data (reduce the excessive dependence on the sharp rise trend) to better conform to the actual upward trend of the power consumption data and reduce the problem of too large prediction results.

[0061] If the change trend of the current power consumption data is a sharp decline, and the residual of the current power consumption data is positive. For example, if the actual value is 10 and the predicted value is 6, it indicates that the decline trend of the predicted value of the power consumption data is faster than the actual decline trend. It is necessary to reduce the autoregressive coefficient, decrease the over-reliance on the current power consumption data (reduce the reliance on the sharp decline trend), slow down the decline trend of the predicted value, make it more in line with the actual decline trend of the power consumption data, and reduce the problem of the predicted result being too small.

[0062] If the change trend of the current power consumption data is a sharp decline, and the residual of the current power consumption data is negative. For example, if the actual value is 6 and the predicted value is 10, it indicates that the decline trend of the predicted value of the power consumption data is slower than the actual value's decline trend, and there is a lag effect in the decline trend of the prediction model. It is necessary to increase the autoregressive coefficient, strengthen the dependence on the current power consumption data (strengthen the dependence on the sharp decline trend) to be more in line with the actual decline trend of the power consumption data, and reduce the problem of the predicted result being too large.

[0063] If the change trend of the current power consumption data is fluctuating, it indicates that the upward or downward trend of the power consumption data is not significant enough. At this time, regardless of whether the residual of the current power consumption data is positive or negative, due to the data fluctuations, corresponding trend adjustments cannot be accurately made. Then retain the initial autoregressive coefficient of the prediction model and perform the prediction. However, since the moving average coefficient of the prediction model is suitable for analyzing fluctuating data, at this time, choose to dynamically adjust the moving average coefficient through the fluctuation characteristics of the data to reduce the error fluctuation and smooth the prediction result.

[0064] Therefore, based on the above adjustment strategy, the adjustment process is set as Figure 2 shown below: First, judge whether the residual of the current power consumption data is 0. If it is 0, retain the initial autoregressive coefficient and the initial moving average coefficient of the ARIMA prediction model; if it is not 0, further judge the change trend type of the current power consumption data. If the change trend type is a sharp increase or a sharp decline, then execute step S51 to adjust the initial autoregressive coefficient. If the change trend type is fluctuating, then execute step S52 to adjust the initial moving average coefficient.

[0065] By this method of dynamically adjusting the parameters of the prediction model, it is possible to adjust the autoregressive coefficient or the moving average coefficient targeted according to the prediction error and the change trend type of the current power consumption data, thereby enhancing the adaptability of the model to different change patterns such as sharp increases, sharp declines, and fluctuations.

[0066] S51: Adjust the initial autoregressive coefficient.

[0067] Set the current moment as At this moment, the initial autoregressive coefficient of the prediction model is dynamically adjusted based on the following formula:

[0068] In the formula, is the autoregressive coefficient after adjustment of the prediction model at moment, is the initial autoregressive coefficient of the prediction model at moment, is the change trend type of the power consumption data at the moment, , sharp rise type; , sharp drop type; , fluctuating type; is the residual of the power consumption data at the moment, is the sign function. If , , if , , if , . is the normalization function, is the mean of the absolute values of the residuals of all historical power consumption data at the moment, is the standard deviation of the absolute values of the residuals of all historical power consumption data at the moment, is a parameter to prevent the denominator from being zero, and the value is , is the absolute value symbol. Among them, the magnitude of the absolute value of the residual is used to reflect the magnitude of the prediction error.

[0069] In this formula, is the trend influence factor at the moment, denoted by , reflects the relative magnitude of the prediction error of the current power consumption data and the prediction error (absolute value of the residual) of the historical power consumption data.

[0070] The specific adjustment logic of this formula is: When , , the current power consumption data shows a sharp rise trend, and the predicted value is lower than the actual value, indicating that the response of the prediction model lags. If is larger, it means that the prediction error of the current power consumption data is larger relative to the prediction error of its historical power consumption data. Then is closer to 1, then is closer to 1. At this time, for the autoregressive coefficient The greater the increase, if the smaller, the closer to 0, then the closer to 0, and at this time the increase amplitude of the regression coefficient is smaller. Through this adjustment, the autoregressive coefficient is increased, making the response of the prediction model faster, catching up with the upward trend faster, and reducing the lag error.

[0071] When , at this time, the current power consumption data shows a sharp upward trend, and the predicted value is greater than the actual value, indicating that the response of the prediction model is ahead. At this time , it has a reducing effect on the autoregressive coefficient , and the greater the prediction error of the current power consumption data, the greater the reduction amplitude. Through this adjustment, the autoregressive coefficient is reduced to avoid the prediction result being overly ahead and improve the tracking stability of the prediction model for the actual trend.

[0072] When , at this time, the current power consumption data shows a sharp downward trend, and the predicted value is lower than the actual value, and the downward trend of the prediction model is too fast. At this time, , it has a reducing effect on the autoregressive coefficient , and the greater the prediction error of the current power consumption data, the greater the reduction amplitude. Through this adjustment, the autoregressive coefficient is reduced to slow down the response speed of the model to the downward trend and prevent the predicted value from being much lower than the actual value, improving the prediction accuracy.

[0073] When , at this time, the current power consumption data shows a sharp downward trend, and the predicted value is higher than the actual value, and the downward trend of the prediction model is too slow and the response is lagging. At this time, , it has an increasing effect on the autoregressive coefficient, and the greater the prediction error of the current power consumption data, the greater the increase amplitude. Through this adjustment, the autoregressive coefficient is increased to make the prediction model catch up with the actual downward trend faster.

[0074] By combining the type of change trend of the power consumption data with the magnitude and direction of the prediction error, the autoregressive coefficient is dynamically adjusted to achieve intelligent control of the model response speed. When the prediction is lagging or ahead, the model can increase or decrease the autoregressive coefficient in a timely manner according to the actual trend, strengthening or weakening the dependence on the current data, so as to more accurately capture the sharp increase or decrease in energy consumption, improve the sensitivity and accuracy of the prediction, and enhance the adaptability of the model to energy consumption changes.

[0075] S52: Adjust the initial moving average coefficient.

[0076] Set the current time as At this moment, dynamically adjust the initial moving average coefficient of the prediction model based on the following formula:

[0077] In the formula, is the adjusted initial moving average coefficient of the prediction model at moment, is the initial moving average coefficient of the prediction model at moment, is the standard deviation of the absolute value of the residual of the power consumption data of the first segment before the power consumption data at is the standard deviation of the absolute value of the residual of the power consumption data of the second segment before the power consumption data at

[0078] In this formula, is the variance of the prediction error of the power consumption data of the first segment before the power consumption data at is the variance of the prediction error of the power consumption data of the second segment before the power consumption data at , it indicates that the recent prediction error fluctuation of the current power consumption data increases relative to the historical prediction error fluctuation, indicating that the stability of the prediction model decreases. At this time , by reducing the moving average coefficient, is weakened, reducing the sensitivity of the prediction model to recent prediction errors, preventing the prediction model from overfitting recent error data, and enhancing stability. If , it indicates that the recent prediction error fluctuation of the current power consumption data tends to be stable, and the model stability increases. At this time , by increasing the moving average coefficient, enhancing the dependence on recent prediction errors, and improving the fitting ability of the prediction model for stable data.

[0079] By comparing the variance changes of recent and historical prediction error fluctuations, dynamically adjust the moving average coefficient to achieve adaptive adjustment of the model's sensitivity to error fluctuations. When the prediction error fluctuation increases, appropriately reduce the moving average coefficient to reduce the dependence on recent abnormal errors, prevent the model from overfitting noise, and enhance the stability of the prediction; while when the error fluctuation tends to be stable, increase the moving average coefficient to strengthen the response to stable errors, thereby achieving intelligent adaptation to the volatility of energy consumption data.

[0080] In summary, by combining the change trend type of the current power consumption data with the residual information, the autoregressive coefficient and the moving average coefficient are dynamically adjusted to achieve adaptive modeling for different data characteristics: when the residual is 0, the initial parameters of the model are maintained regardless of the change trend type; when the change trend type is a sharp rise / drop, the autoregressive coefficient is adjusted using the direction and magnitude of the residual to enhance the model's response ability to trend changes and reduce the lag or lead error in prediction; when the trend is fluctuating, the moving average coefficient is dynamically adjusted by analyzing the change in the residual standard deviation, thereby smoothing short-term error fluctuations and improving the stability and fitting ability of the model in a non-trend environment.

[0081] S6: Use the adjusted prediction model to accurately predict future power consumption data, and monitor energy consumption anomalies based on the difference between the predicted value and the actual value.

[0082] In one embodiment, the energy consumption anomaly monitoring method is as follows: Use the prediction model with adjusted parameters to obtain the predicted value of the next power consumption data (the power consumption data at the next moment) of the current power consumption data , and at the same time obtain the actual value of the next power consumption data as ; Calculate the relative error between the predicted value and the actual value of the next power consumption data:

[0083] In the formula, is the relative error between the predicted value and the actual value of the power consumption data at time , and is the absolute value symbol.

[0084] Generally speaking, a relative error exceeding 10% - 15% can be regarded as an anomaly. In the present invention, the relative error threshold is set to 10%. If the relative error between the predicted value and the actual value exceeds 10%, it is determined that the power consumption data is an abnormal situation. If the relative error between the predicted value and the actual value does not exceed 10%, it is determined that the power consumption data is a normal situation.

Claims

1. A real-time monitoring method for energy consumption data in a smart park, characterized in that, Including: Collecting the power consumption data of the smart park in real time, and based on the historical power consumption data of each power consumption data, obtaining the predicted value of each power consumption data by using the ARIMA prediction model; Taking the power consumption data at any moment as the current power consumption data, detecting the mutation points of the historical power consumption data of the current power consumption data, and dividing the historical power consumption data into multiple segments according to the positions of the mutation points; Calculating the cumulative change rate and coefficient of variation of the power consumption data for each segment respectively, determining the change trend index of each segment based on the cumulative change rate and coefficient of variation, and determining the change trend type of each segment according to the change trend index; Determining the change trend type of the current power consumption data based on the similarity between the current power consumption data and each segment; Dynamically adjusting the autoregressive coefficient and moving average coefficient of the ARIMA prediction model according to the change trend type and residual of the current power consumption data and the residual of the historical power consumption data; where the residual is the difference between the actual value and the predicted value; Predicting the predicted value of the next power consumption data of the current power consumption data based on the adjusted ARIMA model, and performing energy consumption anomaly monitoring according to the difference between the predicted value and the actual value.

2. The real-time monitoring method for energy consumption data of an intelligent park according to claim 1, characterized in that, The process of dynamically adjusting the autoregressive coefficient and moving average coefficient of the ARIMA prediction model is as follows: Judging whether the prediction error of the current power consumption data is 0. If it is 0, retaining the initial autoregressive coefficient and initial moving average coefficient of the ARIMA prediction model; if not, further judging the change trend type of the current power consumption data. If the change trend type is a sharp rise type or a sharp drop type, adjusting the initial autoregressive coefficient. If the change trend type is a fluctuating type, adjusting the initial moving average coefficient.

3. The real-time monitoring method for energy consumption data of an intelligent park according to claim 1, characterized in that The similarity between the current power consumption data and each segment is determined based on the following method: Let the lengths of each segment of the current power consumption data be in sequence as , until , where is the total number of segments; Starting from the current power consumption data, divide it forward in sequence from the back to the front according to the lengths of , until to obtain segments to be matched, and all segments and segments to be matched correspond based on length; Calculating the DTW distance between each segment to be matched and its corresponding segment, and taking the reciprocal of the DTW distance as the similarity between each segment to be matched and its corresponding segment.

4. The real-time monitoring method for energy consumption data of an intelligent park according to claim 1, wherein, The change trend type of each segment is determined by the quartile method based on the change trend indexes of all segments, including: When the change trend index of a certain segment is less than the first quartile of the change trend indexes of all segments, this segment is determined to be a sharp drop type; When the change trend index of a certain segment is greater than the third quartile of the change trend indexes of all segments, this segment is determined to be a sharp rise type; When the change trend index of a certain segment is greater than or equal to the first quartile and less than or equal to the third quartile, this segment is determined to be a fluctuating type.

5. The real-time monitoring method for energy consumption data of an intelligent park according to claim 1, characterized in that, The method for detecting mutation points of the historical power consumption data of the current power consumption data is: Calculating the absolute value of the first-order difference value between every two adjacent power consumption data in the historical power consumption data to construct a first-order difference sequence; Set the dynamic threshold range to the mean of the first-order difference sequence 1.5 times the standard deviation, perform extreme point detection on the first-order difference sequence, identify the extreme points outside the dynamic threshold range as mutation points, and identify the extreme points within the dynamic threshold range as non-mutation points and eliminate them.

6. The real-time monitoring method for energy consumption data of an intelligent park according to claim 1, characterized in that, The method for performing energy consumption anomaly monitoring according to the difference between the predicted value and the actual value is: Calculating the relative error between the predicted value and the actual value of the next power consumption data of the current power consumption data; if the relative error exceeds the preset relative error range, it is determined that the next power consumption data is abnormal; When the relative error does not exceed the preset relative error range, it is determined that the next power consumption data does not have an abnormality.

7. The real-time monitoring method for energy consumption data of an intelligent park according to claim 1, characterized in that The change trend index of each segment is determined by the ratio of the cumulative change rate of the power consumption data of the segment to the coefficient of variation, where the coefficient of variation is the ratio of the standard deviation to the mean of the power consumption data of the segment.

8. The real-time monitoring method for energy consumption data of an intelligent park according to claim 2, wherein, The adjustment of the initial autoregressive coefficient is carried out based on the following formula: ； In the formula, For the prediction model The time-adjusted autoregressive coefficient, For the prediction model The initial autoregressive coefficient at time , for The changing trend type of power consumption data at each moment, for The residual of the power consumption data at the time, is the symbolic function, is the normalization function, for The mean absolute value of the residuals of all historical power consumption data at time , for The standard deviation of the absolute value of the residuals of all historical power consumption data at the time, To prevent the denominator from being zero, is the absolute value symbol.

9. The real-time monitoring method for energy consumption data of an intelligent park according to claim 2, wherein, The adjustment of the initial moving average coefficient is carried out based on the following formula: ； In the formula, is the adjusted initial moving average coefficient of the prediction model at time, is the initial moving average coefficient of the prediction model at time, is the standard deviation of the absolute value of the residual of the power consumption data of the first segment before the power consumption data at is the standard deviation of the absolute value of the residual of the power consumption data of the second segment before the power consumption data at 10. The real-time monitoring method for energy consumption data of an intelligent park according to claim 3, characterized in that, The method for determining the change trend type of the current power consumption data is as follows: If the similarity between a segment to be matched and its corresponding segment is the largest, the change trend type of the corresponding segment is used as the change trend type of the current power consumption data.

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