CN112598227A - Power economic index construction method and system based on power data - Google Patents
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Abstract
The invention provides a power economic index construction method and system based on power data, which comprises the following steps: processing the acquired power data and the industrial increment data according to a time sequence to obtain industrial increment data and a power selling time sequence, determining an index value after season adjustment based on a set index type and the industrial increment data and the power selling time sequence, and obtaining the power economy index based on the index value; the invention obtains the index value after season adjustment by arranging the data and the time difference correlation coefficient formula, and predicts the power economy index through the index value after season adjustment, thereby effectively enabling the calculated power economy index to be more accurate.
Description
Technical Field
The invention relates to an electric power economic index, in particular to an electric power economic index construction method and system based on electric power data.
Background
The electric power data has the characteristics of high frequency, accuracy, wide coverage range, fine granularity and the like, the electric power is closely related to economic development of China and the livelihood of society, the electric power data is a weather meter for economic operation and can assist macroscopic economic prediction, and meanwhile, the electric power data also has the characteristics of objective proofreading, consistent conduction, high frequency, timeliness, wide range and fineness, so that the electric power data and external data resources are necessary to be fully utilized, the deep association relationship between the electric power and the economy is deeply excavated, a data excavation analysis model is constructed, the economic operation condition is objectively reflected, the economic development trend is assisted to be predicted, and the result obtained by the current electric power data-based electric power economic landscape index determination method is often inaccurate enough, has certain deviation and cannot accurately evaluate the economy.
Disclosure of Invention
Aiming at the problem that the result obtained by the determination method of the power economic index of the power data in the prior art is not accurate enough, the invention provides a power economic index construction method based on the power data, which comprises the following steps:
processing the acquired power data and the industrial added data according to a time sequence to obtain industrial added value data and a time sequence of electricity selling quantity;
determining an index value after season adjustment based on the set index type, the industrial increase value data and the electricity selling quantity time sequence;
and obtaining the power economy index based on the index value.
Preferably, the processing the acquired power data and the industrial increment data according to a time sequence to obtain an industrial increment value data and a power selling amount time sequence includes:
obtaining stabilized industrial increment data and a stabilized electricity selling quantity time sequence according to the obtained power data and the industrial increment data;
and carrying out seasonal adjustment on the stabilized industrial increase value data and the stabilized electricity selling quantity time sequence by utilizing the time sequence to obtain the seasonally adjusted industrial increase value data and electricity selling quantity time sequence.
Preferably, the seasonal adjustment of the smoothed industry increase value data and the smoothed electricity sales amount time series by using the time series includes:
separating the stabilized industrial added value data and the stabilized electricity selling quantity time sequence into a trend factor, a seasonal factor, a periodic factor and an irregular factor by using a time sequence seasonal adjustment program, and removing the seasonal factor;
and classifying the eliminated stabilized industrial added value data and the stabilized electricity selling quantity time sequence according to the chronological order of the indexes.
Preferably, the determining the index value after season adjustment based on the set index type, the industrial added value data and the electricity selling amount time series includes:
performing time difference correlation analysis on the industrial added value data and the electricity selling quantity time sequence after season adjustment to obtain a time difference correlation coefficient and lead time of the electricity selling time sequence;
determining the index type of the electricity selling amount time sequence data based on the lead time;
screening the time-difference correlation coefficient of the electricity selling time series and a preset correlation coefficient interval to obtain an index value after season adjustment;
the index types include: leading indicators, consistent indicators, and lagging indicators.
Preferably, the obtaining the electricity economy index based on the index value includes:
based on the selected index value after season adjustment, calculating the symmetrical change rate of the difference value between a certain moment and the last moment of the index value relative to the average value of the certain moment and the last moment by adopting a symmetrical change rate increasing sequence formula;
based on the symmetrical change rate and the index number, calculating a standardized symmetrical growth rate by adopting a standardized symmetrical growth rate formula;
based on the symmetrical growth rate, calculating the average change rate of the standardized symmetrical growth rate by adopting an average calculation formula;
and calculating the power economy index by adopting a power economy index formula based on the average change rate.
Preferably, the symmetric rate-of-change growth sequence formula is shown as follows:
wherein, Oi(t) is the symmetric rate of change at time t; a isiAnd (t) is the index after season adjustment at the time t.
Preferably, the symmetric rate-of-change growth sequence formula is shown as follows:
wherein R (t) is the average rate of change at time t; stdiAnd (t) is the normalized symmetrical growth rate at the time t, and k is the index number.
Preferably, the power economy index formula is shown as follows:
wherein index (t) is the economic power index; RConstant is the average rate of change of the consistent index; RLead is the average rate of change of the leading indicator; r (t) is the average rate of change at time t.
Based on the same invention concept, the invention also provides a power economy index construction system based on power data, which comprises the following steps: the device comprises a data acquisition module, an index value determination module and a calculation module;
the data acquisition module: the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring power data and industrial increment data according to a time sequence to obtain industrial increment value data and a power selling time sequence;
the index value determining module: the index value after season adjustment is determined by determining the set index type, the industrial increase value data and the electricity selling quantity time sequence;
the calculation module: and the index value is determined to obtain the power economy index.
Preferably, the module for determining the index value comprises a time difference correlation submodule, an index type determining submodule and an index value determining submodule;
the time difference correlation submodule: the time difference correlation analysis module is used for determining the time sequence of the electricity selling quantity and the industrial added value data after season adjustment to obtain the time difference correlation coefficient and the lead time of the time sequence of the electricity selling quantity;
the determine indicator type submodule: the index type used for determining the electricity selling amount time sequence data by the lead time;
the index value determining module: and the time difference correlation coefficient and the preset correlation coefficient interval for determining the electricity selling time sequence are used for screening the electricity selling time sequence data of each type to obtain an index value after season adjustment.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention provides a power economic index construction method based on power data, which comprises the following steps: processing the acquired power data and the industrial increment data according to a time sequence to obtain industrial increment data and a power selling time sequence, determining an index value after season adjustment based on a set index type and the industrial increment data and the power selling time sequence, and obtaining the power economy index based on the index value; the invention obtains the index value after season adjustment by arranging the data and the time difference correlation coefficient formula, predicts the power economy index through the index value after season adjustment, and can accurately calculate the obtained power economy index.
2. The time correlation coefficient and the lead time are obtained through a time difference correlation coefficient formula, the season adjusted index value can be obtained through the time correlation coefficient and the lead time, the power economy index is calculated through the index value, and the calculation result is more accurate through the integration of the time difference correlation coefficient.
Drawings
FIG. 1 is a schematic diagram of a power economy index construction method based on power data according to the present invention;
FIG. 2 is a schematic diagram of a construction process of an electric power economic index based on electric power data according to the present invention;
FIG. 3 is a comparative graphical illustration of industry augmentation values of the present invention after removing long term trends.
Detailed Description
Example 1
With reference to fig. 1, the present invention provides a power economy index construction method based on power data, including:
the method comprises the following steps: processing the acquired power data and the industrial added data according to a time sequence to obtain industrial added value data and a time sequence of electricity selling quantity;
step two: determining an index value after season adjustment based on the set index type, the industrial increase value data and the electricity selling quantity time sequence;
step three: and obtaining the power economy index based on the index value.
Wherein, the first step: the obtained power data and the industrial increment data are processed according to the time sequence to obtain industrial increment data and a power selling time sequence, and the method comprises the following steps:
obtaining stabilized industrial increment data and a stabilized electricity selling quantity time sequence according to the obtained power data and the industrial increment data;
and carrying out seasonal adjustment on the stabilized industrial increase value data and the stabilized electricity selling quantity time sequence by utilizing the time sequence to obtain the seasonally adjusted industrial increase value data and electricity selling quantity time sequence.
Seasonal adjustment is performed on the stabilized industrial added value data and the stabilized electricity selling amount time series by using the time series, and the seasonal adjustment comprises the following steps:
separating the stabilized industrial added value data and the stabilized electricity selling quantity time sequence into a trend factor, a seasonal factor, a periodic factor and an irregular factor by using a time sequence seasonal adjustment program, and removing the seasonal factor;
and classifying the eliminated stabilized industrial added value data and the stabilized electricity selling quantity time sequence according to the chronological order of the indexes.
Wherein, the step two: the method for determining the index value after season adjustment based on the set index type, the industrial increase value data and the electricity selling quantity time sequence comprises the following steps:
performing time difference correlation analysis on the industrial added value data and the electricity selling quantity time sequence after season adjustment to obtain a time difference correlation coefficient and lead time of the electricity selling time sequence;
determining the index type of the electricity selling amount time sequence data based on the lead time;
screening various types of electricity selling quantity time series data based on the time difference correlation coefficient of the electricity selling time series and a preset correlation coefficient interval to obtain an index value after season adjustment;
the index types include: leading indicators, consistent indicators, and lagging indicators.
Wherein, the third step: obtaining an electricity economy index based on the index value, comprising:
based on the selected index value after season adjustment, calculating the symmetrical change rate of the difference value between a certain moment and the previous moment of the index value relative to the average value of the certain moment and the previous moment by adopting a symmetrical change rate increasing sequence formula;
based on the symmetrical change rate and the index number, calculating a standardized symmetrical growth rate by adopting a standardized symmetrical growth rate formula;
based on the symmetrical growth rate, calculating the average change rate of the standardized symmetrical growth rate by adopting an average calculation formula;
and calculating the power economy index by adopting a power economy index formula based on the average change rate.
The symmetric rate of change growth sequence formula is shown as follows:
wherein, Oi(t) is the symmetric rate of change at time t; a isiAnd (t) is the index after season adjustment at the time t.
The symmetric rate of change growth sequence formula is shown as follows:
wherein R (t) is the average rate of change at time t; stdiAnd (t) is the normalized symmetrical growth rate at the time t, and k is the index number.
The power economy index formula is shown as the following formula:
wherein index (t) is the power economy index; RConstant is the average rate of change of the consistent index; RLead is the average rate of change of the leading indicator; r (t) is the average rate of change at time t.
Example 2
With reference to fig. 2, the construction of the power economy index mainly comprises the steps of 101 data acquisition, 102 data preprocessing, 103 index screening and 104 index synthesis. The method comprises the steps of 101, acquiring data, 102, preprocessing the data, 103, screening indexes, 104 and evaluating results, wherein the data acquisition comprises power data and industrial increment value data, the preprocessing of the data comprises data cleaning, sequence stabilization calculation and season adjustment calculation, the index screening comprises time difference correlation analysis and index screening, and the index synthesis comprises consistent index synthesis, leading index synthesis and lagging index synthesis. The following describes the specific technical scheme of the invention in detail by combining specific embodiments:
1. step 101 data acquisition
In one embodiment of the present invention, the field descriptions required for obtaining power data are shown in table 1,
TABLE 1
The field descriptions required to obtain the industry added value data are shown in table 2:
| RPT_MONTH | IVA_RATE |
| year and month | Monthly unity rate of growth of industrial added value |
TABLE 2
2. Step 102 data preprocessing
And preprocessing the acquired power data, wherein the data preprocessing comprises data cleaning, numerical calculation, sequence stabilization and season adjustment. The electricity economic index can utilize the electricity selling quantity data and the attachment capacity data of the electricity data, the data processing procedures of the electricity selling quantity data and the attachment capacity data are the same, and in the embodiment of the invention, the electricity data is the electricity selling quantity data.
(1) Data cleansing
The method is characterized in that the power data is subjected to data cleaning (the data cleaning mainly aims at data with the power selling amount of 0, abnormal value data and missing value data) and comprises the following steps:
1): and (3) no related industry data processing is performed, data perspective operation is performed on the original electricity sales data, so that the data are in a two-dimensional table data perspective table form with rows and columns respectively being industry and year and month, if all the year and month electricity sales data values of one industry are 0, the fact that the industry does not exist locally is indicated, and deletion processing is performed on the data.
2): and processing abnormal values. And carrying out data perspective operation on the original electricity sales data to enable the data to be in a two-dimensional table data perspective table form with rows and columns respectively in industry and year and month, identifying abnormal values (such as negative values and single zero values) in the data, and converting the abnormal values into missing values for processing.
3): and (5) processing missing values. And carrying out data perspective operation on the original electricity sales data to enable the data to be in a two-dimensional table data perspective table form with rows and columns respectively in industry and year and month, and identifying missing values in the data. And filling missing value data through the same-period field in the last year, and filling the residual missing values by adopting a linear interpolation method.
Monthly electricity sales data corresponding to the electricity data in the embodiment of the invention are obtained through data cleaning, the data comprise electricity sales of all industries, and have no abnormal value or missing value, so that the basic requirements of data analysis are met.
(2) Numerical calculation
And calculating the monthly commensuration growth rate of the industry electricity sales amount corresponding to the industry increment value data according to the industry increment value data, inputting monthly electricity sales amount data which is subjected to data cleaning, and calculating monthly commensuration growth rate data of electricity sales amount in 7 months in 2013 to 8 months in 2019.
(3) Sequence smoothing
The monthly same-proportion growth rate data (power data for short) and the monthly industrial increment value same-proportion growth rate data (industrial increment value data for short) of the electricity sales are respectively input, long-term trend items of the power data and the industrial increment value data are removed by utilizing a time sequence high-pass filtering technology, and the method is realized by adopting an hpfilter method in a statmodel package of python language. The power data and the industrial added value data sequence are stabilized by removing the long-term trend item, namely the stabilized industrial added value data and the stabilized electricity selling quantity time sequence are generated, and the stability check of the ARIMA model is met. Taking the data of the industrial added value as an example, the data of the part before the industrial added value is not processed and after the industrial added value is stabilized are shown in table 3:
TABLE 3
After the industrial added value is trended to be removed, as shown in fig. 3, the left side is the original data, and the right side is the data after trend removal, it can be seen that the variation amplitude of the industrial added value on the right side is obviously reduced and tends to be stable.
(4) Season adjustment
Inputting a smoothed industrial added value time sequence and performing seasonal adjustment processing on the time sequence by using a time sequence x13_ ARIMA technology, wherein the seasonal adjustment is realized by using an x13_ ariam packet in a python language total statmodels packet in the embodiment. And acquiring a trend cycle item in the time series through seasonal adjustment, and embodying the cycle periodicity of the time series with the trend cycle item. And finishing data preprocessing to generate the preprocessed first data.
3. Step 103 index screening
In order to screen out specific industry electricity selling quantity data capable of supporting and calculating relevant indexes of the electricity economic index, time difference correlation analysis needs to be carried out on the electricity selling quantity time series data and the industry added value time series data, and industry data screening needs to be carried out.
(1) Calculating time difference correlation coefficient
Time difference correlation analysis is carried out on the time series data of the electricity selling quantity and the time series data of the industrial added value, so that a time difference correlation coefficient and lead time of the electricity selling time series are obtained, and a calculation formula of the time difference correlation coefficient is as follows:
where L is 0, ± 1 … … ± L, which indicates the number of lead or lag periods, negative values indicate lag, positive values indicate lead, L indicates the maximum number of lag, and nl is the number of data after data alignment.
And obtaining the correlation coefficient of each order of the monthly comparable growth rate of the electricity sales of different industries and the industry increment value through time difference correlation analysis. The value range of the correlation coefficient is-1- +1 (the correlation coefficient is 0 to represent no correlation, the coefficient is more than 0 to represent linear positive correlation, and the coefficient is less than 0 to represent linear negative correlation). Table 4 shows an example of the time difference correlation analysis between the electricity sales amount and the industry added value of the metal processing machine, wherein the 0 th order represents the calculation correlation coefficient between the electricity sales amount and the industry added value data at the same period, the 1 st order represents the calculation correlation coefficient between the electricity sales amount data which leads by 1 month and the industry added value data, and the +1 st order represents the calculation correlation coefficient between the electricity sales amount data which lags by 1 month and the industry added value data, and the order ranges from-12 to + 12.
TABLE 4
(2) Data screening
And aiming at time difference correlation calculation results of different industries, screening out specific electric quantity selling time sequences of which industries can be used for synthesizing a consistent index, a leading index and a lagging index according to a preset correlation coefficient interval value. The screening criteria in this example are shown in table 5:
| order of the scale | Correlation coefficient screening conditions | Use of |
| 0 | Greater than 0.6 | For calculating a conformity index |
| [-11,-2] | Greater than 0.75 | For calculating leading indicators |
| [1,11] | Greater than 0.75 | For measuring hysteresis index |
TABLE 5
4. Step 104 exponential synthesis
Inputting the screened leading index, the screened consistent index and the screened lagging index into a prospect index calculation model for calculation to obtain the power economic index; industry electricity sales data used for calculating the consistent index, the leading index and the lagging index are respectively input, and the consistent index, the leading index and the lagging index can be correspondingly calculated by using a scene gas synthesis method. The specific calculation steps are as follows:
(1): the formula for calculating the symmetric rate-of-change growth sequence of the corresponding index is as follows:
wherein, O _ i (t) and a _ i (t) are respectively the symmetrical change rate at the time t and the seasonal adjustment index.
(2): the symmetrical change rate increase sequence calculates a standardized symmetrical increase rate, and the formula for calculating the standardized symmetrical increase rate is as follows:
wherein, Averi is the ith index normalization calculation intermediate value, stdi (t) is the normalization symmetric growth rate corresponding to the t moment, N is the time period length, and N is an integer.
(3): averaging the normalized symmetrical growth rate of each index to obtain the average change rate of the index, wherein the calculation formula is as follows:
wherein R (t) and Stdi (t) are the average change rate and the normalized symmetric growth rate corresponding to the time t, respectively, and k is the index number.
(4): calculating the power economy index according to the average change rate, wherein a formula for calculating the power economy index is as follows:
where Index (t) is the above-mentioned economic power Index, rconstent and RLead respectively indicate the average change rates of the match and the preceding indices, r (t) and stdi (t) respectively indicate the average change rates corresponding to time t, when t is 0, the reference time is set, and Index (0) is 100.
After the power economy index is obtained through the embodiment of the invention, the reliability of the power economy index obtained through the method is evaluated through an index evaluation mode, and a time difference correlation index evaluation method and a trend method index evaluation method are specifically adopted.
(1) Time difference correlation index evaluation method
And (3) respectively carrying out time difference correlation analysis on the synthesized two time sequences of the consistent index and the advanced index and the time sequence of the industrial added value to obtain 2 lines of 25 rows to 12 values of 25 orders of time difference correlation coefficient, wherein the result is shown in a table 6:
TABLE 6
And respectively taking the maximum value of each row so as to obtain the consistent and advanced optimal orders, and evaluating whether the optimal orders accord with the consistent and advanced index definitions. And reasonably evaluating the construction method of the power economic index through the value of the correlation coefficient. From the results, the maximum accuracy of the consistent index is 0.684, and the final accuracy of the antecedent index is 0.726, which is in line with the trend of the industrial added value index.
(2) Trend consistency index evaluation method
And evaluating whether the trend of the synthesized three time series of the consistent index, the leading index and the lagging index is consistent with the trend of the time series of the industrial added value respectively by calculating the proportion that the section trends between every two same time points in the two equal-length time series are the same. From the results, the accuracy of the consistent index trend evaluation method is 0.66 (full score of 1), the accuracy of the leading index trend evaluation method is 0.67 (full score of 1), and the consistent index and the leading index accord with the industry increment trend standard.
The results of the evaluation method of the correlation index of the time difference and the evaluation method of the trend conformity index are shown in table 7. The power economic index obtained based on the power data in the embodiment of the invention conforms to the development trend of the industrial added value and can provide reference for economic monitoring.
| Evaluation method | Consistent evaluation | Preliminary evaluation |
| Time difference correlation index evaluation method | 0.68 | 0.73 |
| Trend consistency index evaluation method | 0.66 | 0.67 |
TABLE 7
The embodiment of the invention can solve the following problems: the method has the advantages that the updating frequency of the electric power data is high, the national and provincial (municipal) economic development situation can be monitored and predicted in advance in real time, and the problem of traditional data lag is solved; secondly, the power data is objective and accurate, the economic development condition can be objectively and accurately reflected by actual facts, and the problem of data summarization distortion is solved; and thirdly, three dimensions of the region, the industry and the industry are matched, the economic development condition is monitored in a three-dimensional comprehensive mode, and the one-sidedness problem of single-dimension observation economy is solved. And application verification is carried out in the following three scenes:
1) and (4) prejudging the macroscopic economic situation of the whole country. On the national level, the macro economy can be reflected in real time through the consistent synthesis index, the effective prediction of the macro economy is realized through the prior synthesis index, and the service government makes macro economy policies and industry development plans.
2) And (3) pre-judging the macroscopic economic situation of province (city). On the province (city) level, the real-time reflection and trend prejudgment are carried out on the economic landscape conditions of various provinces and cities, and accurate electric power data support is provided for regional economic development departments and operators to master regional economic trends in time.
3) And (5) analyzing industrial prosperity. The industrial landscape analysis comprehensively analyzes key industrial electric power data and economic data, effectively reflects the relationship between industrial development and power consumption, predicts the industrial development trend and supports relevant departments to implement industrial structure optimization and adjustment.
In the process, a macro-economic prediction monitoring index system is used as a support, a seasonal adjustment method is utilized, original data are separated into trend factors, seasonal factors, periodic factors and irregular factors, a reference index, a reference cycle and a reference date are determined, the seasonal factors are eliminated, an image method, a time sequence correlation method and a K-L information quantity method are used, an index which is well matched with the reference cycle is selected from a plurality of alternative indexes, and classification is carried out according to the time sequence of the fluctuation change of the index and the reference cycle.
Example 3
Based on the same invention concept, the invention also provides a power economy index construction system based on power data, which comprises the following steps: the device comprises a data acquisition module, an index value determination module and a calculation module;
a data acquisition module: the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring power data and industrial increment data according to a time sequence to obtain industrial increment value data and a power selling time sequence;
an index value determining module: the index value after season adjustment is determined by determining the set index type, the industrial increase value data and the electricity selling quantity time sequence;
a calculation module: and determining the index value to obtain the power economy index.
The index value determining module comprises a time difference correlation submodule, an index type determining submodule and an index value determining submodule;
time difference correlation submodule: the time difference correlation analysis module is used for determining the time sequence of the electricity selling quantity and the industrial added value data after season adjustment to obtain the time difference correlation coefficient and the lead time of the time sequence of the electricity selling quantity;
and an index type determining submodule: the index type used for determining the electricity selling amount time sequence data by the lead time;
an index value determining module: and the time difference correlation coefficient and the preset correlation coefficient interval for determining the electricity selling time sequence are used for screening the electricity selling time sequence data of each type to obtain an index value after season adjustment.
The calculation module comprises a symmetrical change rate submodule, a standardized symmetrical growth rate submodule, an average change rate submodule and a power economy index calculation submodule;
symmetric rate of change submodule: calculating a symmetrical change rate by adopting a symmetrical change rate increase sequence formula based on the leading index value, the consistent index value and the lagging index value after season adjustment;
normalized symmetric growth rate submodule: based on the symmetrical change rate, calculating a standardized symmetrical growth rate by adopting a standardized symmetrical growth rate formula;
average rate of change submodule: based on the symmetrical growth rate, calculating the average change rate by adopting an average calculation formula;
and (3) calculating a power economy index submodule: and calculating the power economy index by adopting a power economy index formula based on the average change rate.
Wherein, the formula of the power economy index is shown as the following formula:
where Index (t) is the above-mentioned economic power Index, rconstent and RLead respectively indicate the average change rates of the match and the preceding indices, r (t) and stdi (t) respectively indicate the average change rates corresponding to time t, when t is 0, the reference time is set, and Index (0) is 100.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The present invention is not limited to the above embodiments, and any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention are included in the scope of the claims of the present invention which are filed as the application.
Claims (10)
1. An economic prediction method based on an electricity economic index is characterized by comprising the following steps:
processing the acquired power data and the industrial added data according to a time sequence to obtain industrial added value data and a time sequence of electricity selling quantity;
determining an index value after season adjustment based on the set index type, the industrial increase value data and the electricity selling quantity time sequence;
and obtaining the power economy index based on the index value.
2. The method of claim 1, wherein the processing the acquired power data and industry augmentation data in a time series to obtain industry augmentation value data and electricity sales time series comprises:
obtaining stabilized industrial increment data and a stabilized electricity selling quantity time sequence according to the obtained power data and the industrial increment data;
and carrying out seasonal adjustment on the stabilized industrial increase value data and the stabilized electricity selling quantity time sequence by utilizing the time sequence to obtain the seasonally adjusted industrial increase value data and electricity selling quantity time sequence.
3. The method of claim 2, wherein the utilizing the time series for seasonal adjustment of the smoothed industry growth value data and the smoothed time series of electricity sales comprises:
separating the stabilized industrial added value data and the stabilized electricity selling quantity time sequence into a trend factor, a seasonal factor, a periodic factor and an irregular factor by using a time sequence seasonal adjustment program, and removing the seasonal factor;
and classifying the eliminated stabilized industrial added value data and the stabilized electricity selling quantity time sequence according to the chronological order of the indexes.
4. The method of claim 3, wherein determining a seasonally adjusted indicator value based on the set indicator type and the industry increase value data and electricity sales time series comprises:
performing time difference correlation analysis on the industrial added value data and the electricity selling quantity time sequence after season adjustment to obtain a time difference correlation coefficient and lead time of the electricity selling time sequence;
determining the index type of the electricity selling amount time sequence data based on the lead time;
screening the time-difference correlation coefficient of the electricity selling time series and a preset correlation coefficient interval to obtain an index value after season adjustment;
the index types include: leading indicators, consistent indicators, and lagging indicators.
5. The method of claim 4, wherein said deriving the power economy index based on the index value comprises:
based on the selected index value after season adjustment, calculating the symmetrical change rate of the difference value between a certain moment and the last moment of the index value relative to the average value of the certain moment and the last moment by adopting a symmetrical change rate increasing sequence formula;
based on the symmetrical change rate and the index number, calculating a standardized symmetrical growth rate by adopting a standardized symmetrical growth rate formula;
based on the symmetrical growth rate, calculating the average change rate of the standardized symmetrical growth rate by adopting an average calculation formula;
and calculating the power economy index by adopting a power economy index formula based on the average change rate.
6. The method of claim 5, wherein the symmetric rate-of-change growth sequence formula is represented by:
wherein, Oi(t) is the symmetric rate of change at time t; a isiSeason regulation rear finger with time tAnd (4) marking.
7. The method of claim 6, wherein the symmetric rate of change growth sequence formula is represented by the following equation:
wherein R (t) is the average rate of change at time t; stdiAnd (t) is the normalized symmetrical growth rate at the time t, and k is the index number.
8. The method of claim 7, wherein the power economy index formula is represented by the following equation:
wherein index (t) is the economic power index; RConstant is the average rate of change of the consistent index; RLead is the average rate of change of the leading indicator; r (t) is the average rate of change at time t.
9. A power economy index construction system based on power data, comprising: the device comprises a data acquisition module, an index value determination module and a calculation module;
the data acquisition module: the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring power data and industrial increment data according to a time sequence to obtain industrial increment value data and a power selling time sequence;
the index value determining module: the index value after season adjustment is determined by determining the set index type, the industrial increase value data and the electricity selling quantity time sequence;
the calculation module: and the index value is determined to obtain the power economy index.
10. The system of claim 9, wherein the determine metric value module comprises a time difference correlation submodule, a determine metric type submodule, and a determine metric value submodule;
the time difference correlation submodule: the time difference correlation analysis module is used for determining the time sequence of the electricity selling quantity and the industrial added value data after season adjustment to obtain the time difference correlation coefficient and the lead time of the time sequence of the electricity selling quantity;
the determine indicator type submodule: the index type used for determining the electricity selling amount time sequence data by the lead time;
the index value determining module: and the time difference correlation coefficient and the preset correlation coefficient interval for determining the electricity selling time sequence are used for screening the electricity selling time sequence data of each type to obtain an index value after season adjustment.
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| CN113313533A (en) * | 2021-06-21 | 2021-08-27 | 国网信通亿力科技有限责任公司 | Method for performing macroscopic economy prediction and monitoring by using electric power data |
| CN113344737A (en) * | 2021-06-04 | 2021-09-03 | 北京国电通网络技术有限公司 | Device control method, device, electronic device and computer readable medium |
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