CN110795698A - Summer electric quantity analysis method and readable storage medium - Google Patents
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Abstract
The invention discloses a summer electric quantity analysis method and a readable storage medium, wherein the method comprises the following steps: selecting an electric quantity analysis index, and acquiring daily electric quantity of a corresponding area in summer; performing correlation analysis on the electric quantity analysis indexes and the daily electric quantity of the corresponding area; fitting the daily electric quantity of the corresponding area according to the analysis index; and performing comparative analysis according to the correlation analysis results of different years and the fitting result of the daily electricity consumption to predict the summer electricity consumption. The method performs correlation analysis on the electric quantity analysis index and the daily electric quantity of the corresponding area; fitting the daily electric quantity of the corresponding area according to the analysis index; and performing comparative analysis according to the correlation analysis results of different years and the fitting result of the daily electricity consumption to predict the summer electricity consumption. Various electric quantity analysis indexes are considered, and the prediction of the electric quantity in summer is realized.
Description
Technical Field
The invention relates to the technical field of power grid load management, in particular to a summer electric quantity analysis method and a readable storage medium.
Background
With the development of social economy and the improvement of the living standard of people, the holding quantity and the utilization rate of air conditioning equipment in the whole society are continuously improved. Local areas such as Chongqing are one of the four fire furnaces in China due to terrain reasons and sultry in summer. The temperature in summer rises faster and lasts longer, and the rapid increase of the power consumption in summer in the area is directly influenced. Particularly, 6 and 7 months in summer each year are the highest temperature rising speed and the highest temperature time including summer day.
The Chongqing station of the China weather service discloses the climate profiles of summer part months of 2018 and 2019:
(1) the average temperature of the whole market in 6 months in 2018 is 25 ℃, which is 0.5 ℃ higher than the temperature of the whole market in the same period of the year (24.5 ℃). Fluctuation in the month is obvious, slightly higher by 0.2 ℃ in the first ten days, lower by 0.9 ℃ in the middle ten days and obviously higher by 2.2 ℃ in the last ten days.
(2) The average temperature of the whole market in 7 months in 2018 is 29.2 ℃, and is obviously higher than that of the same year (27.4 ℃) by 1.8 ℃. The air temperature in each ten days is higher by 0.4 ℃ in the last ten days, and is obviously higher by 2.9 ℃ and 2.1 ℃ in the middle and last ten days respectively.
(3) The average air temperature of the whole market in 6 months in 2019 is 23.9 ℃, and is respectively lower by 0.6 ℃ and 1.1 ℃ than the same year (24.5 ℃) and the same year (25 ℃) of the same year (figure 1). The temperature fluctuation in each ten days in the month is obvious, the temperature is obviously higher by 1.4 ℃ in the last ten days, and is obviously lower by 1.6 ℃ in both the middle and the last ten days.
According to collected meteorological data, the average temperature of the whole market in 7 months in 2019 is 28.1 ℃, 0.7 ℃ higher than that of the same year (27.4 ℃) and 1.1 ℃ lower than that of the same year (29.2 ℃). 2, according to meteorological data and actual body feeling, the sweltering degree in summer of 2018 is far higher than that in summer of 2019, summer of 2018 is summer heat, and summer of 2019 belongs to cool summer.
From the analysis data of the weather bureau, it can be seen that the relationship between the temperature and the electric quantity in summer needs to be accurately analyzed, but the relationship is far from the curve of the temperature change.
Disclosure of Invention
In view of the above-mentioned defects of the prior art, an object of the present invention is to provide a summer power analysis method and a readable storage medium, which consider various power analysis indexes and realize prediction of power consumption in summer.
One of the purposes of the invention is realized by the technical scheme that the method for analyzing the electric quantity in summer comprises the following steps:
selecting an electric quantity analysis index, and acquiring daily electric quantity of a corresponding area in summer;
performing correlation analysis on the electric quantity analysis indexes and the daily electric quantity of the corresponding area;
fitting the daily electric quantity of the corresponding area according to the analysis index;
and performing comparative analysis according to the correlation analysis results of different years and the fitting result of the daily electricity consumption to predict the summer electricity consumption.
Optionally, the analysis index includes a maximum daily temperature, a temperature and humidity index, a weighted temperature and humidity index, and a heat index.
Optionally, when the analysis indicator is the highest daily temperature, performing correlation analysis on the electricity analysis indicator and the daily electricity consumption of the corresponding area, including:
the highest temperature in summer and the corresponding daily electric quantity are selected, and the correlation degree is calculated through the Pearson correlation coefficient, so that the following requirements are met:
wherein p is a correlation coefficient, n is the number of selected dates, and xiIndicates daily electricity quantity, yiIndicating the daily maximum air temperature.
Optionally, under the condition that the analysis index is a temperature and humidity index, performing correlation analysis on the daily electricity consumption of the corresponding area through the electricity consumption analysis index, including:
constructing a temperature-humidity index which meets the following requirements:
wherein THI represents the temperature-humidity index, TempcIndicating the temperature in degrees celsius for the day, Hmd indicating the relative humidity for the day;
weighting the temperature-humidity index based on the current date, satisfying:
WTHIC＝(10THIC+4THIC-1+THIC-2)/15
wherein, WCHIcIndicating the weighted temperature-humidity index, THIc、THIc-1、THIc-2Respectively represents the temperature and humidity indexes of the day C, the yesterday C-1 and the day before C-2.
Optionally, the fitting the daily electricity consumption of the corresponding area according to the analysis index includes:
performing regression through a primary curve according to the maximum daily air temperature in summer and the corresponding daily electricity quantity to obtain a fitting relation of the maximum daily air temperature; and
and performing regression through a primary curve according to the weighted temperature and humidity index and the daily electricity consumption to obtain a weighted temperature and humidity index fitting relation.
Optionally, in a case that the analysis index is a heat index, performing correlation analysis on the electricity analysis index and the daily electricity consumption of the corresponding area, including:
and (3) constructing a heat index which meets the following requirements:
I＝1.8TempC-0.55(1.8TempC-26)*(1-Hmd)+32
wherein I represents the heat index.
Optionally, performing comparative analysis according to correlation analysis results of different years and fitting results of daily power consumption to predict summer power consumption includes:
and (3) comparing the correlation coefficients and the fitting relations of the daily maximum temperature and daily electricity consumption, the weighted temperature and humidity index and the daily electricity consumption in different years, and combining the relation of the sweltering index and the daily electricity consumption to predict the summer electricity consumption.
The second object of the present invention is achieved by the technical solution, which is a computer-readable storage medium, wherein an implementation program for information transfer is stored on the computer-readable storage medium, and the implementation program implements the steps of the foregoing method when executed by a processor.
Due to the adoption of the technical scheme, the invention has the following advantages: the method performs correlation analysis on the electric quantity analysis index and the daily electric quantity of the corresponding area; fitting the daily electric quantity of the corresponding area according to the analysis index; and performing comparative analysis according to the correlation analysis results of different years and the fitting result of the daily electricity consumption to predict the summer electricity consumption. Various electric quantity analysis indexes are considered, and the prediction of the electric quantity in summer is realized.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention.
Drawings
The drawings of the invention are illustrated as follows:
FIG. 1 is a flow chart of a first embodiment of the present invention;
FIG. 2 is a curve of the maximum temperature in summer and the daily electricity consumption of the first embodiment 2018;
FIG. 3 shows summer THI and WTHI indices of 2018 according to a first embodiment of the present invention;
FIG. 4 shows a first embodiment 2018 of the present invention showing the trend of WHI in summer versus daily electricity usage;
FIG. 5 is a curve of the maximum temperature in summer and the daily electricity consumption of the second embodiment 2019;
FIG. 6 shows summer THI and WTHI indicators of a second embodiment 2019;
fig. 7 shows a summer WTHI and daily electricity consumption trend of the second embodiment 2019.
Detailed Description
The invention is further illustrated by the following figures and examples.
Example one
A first embodiment of the present invention provides a summer power analysis method, as shown in fig. 1, the method includes:
selecting an electric quantity analysis index, and acquiring daily electric quantity of a corresponding area in summer;
performing correlation analysis on the electric quantity analysis indexes and the daily electric quantity of the corresponding area;
fitting the daily electric quantity of the corresponding area according to the analysis index;
and performing comparative analysis according to the correlation analysis results of different years and the fitting result of the daily electricity consumption to predict the summer electricity consumption.
The method performs correlation analysis on the electric quantity analysis index and the daily electric quantity of the corresponding area; fitting the daily electric quantity of the corresponding area according to the analysis index; and performing comparative analysis according to the correlation analysis results of different years and the fitting result of the daily electricity consumption to predict the summer electricity consumption. Various electric quantity analysis indexes are considered, and the prediction of the electric quantity in summer is realized.
Optionally, the analysis index includes a maximum daily temperature, a temperature and humidity index, and a heat index.
Specifically, in the embodiment, three aspects are adopted to analyze and predict summer electric quantity, including analysis of influence of the highest daily temperature on the electric quantity, analysis of influence of temperature accumulation effect on the electric quantity, analysis of influence of hot weather process on the electric quantity, and corresponding to the highest daily temperature, temperature and humidity indexes and hot index respectively.
Optionally, when the analysis indicator is the highest daily temperature, performing correlation analysis on the electricity analysis indicator and the daily electricity consumption of the corresponding area, including:
the highest temperature in summer and the corresponding daily electric quantity are selected, and the correlation degree is calculated through the Pearson correlation coefficient, so that the following requirements are met:
wherein p is a correlation coefficient, n is the number of selected dates, and xiIndicates daily electricity quantity, yiIndicating the daily maximum air temperature.
Specifically, taking the correlation between the highest daily temperature in summer and the corresponding daily electricity consumption as an example, the main factor influencing the electricity consumption in the meteorological factors in summer is the highest daily temperature, the highest daily temperature is selected to analyze the influence of the climate on the daily electricity consumption, and the change trends of the two are shown in fig. 2. To investigate the phase between the maximum daily temperature and the daily powerAnd (4) introducing a Pearson correlation coefficient. For two variables X ═ X1，x2，…，xnY ═ Y1，y2，…，ynThe calculation formula of the pearson correlation coefficient p is as follows:
through calculation, the correlation coefficient of the highest temperature in summer days of 2018 and the daily electric quantity is 0.8864, which shows that the two have strong correlation.
Optionally, the fitting the daily electricity consumption of the corresponding area according to the analysis index includes:
performing regression through a primary curve according to the maximum daily air temperature in summer and the corresponding daily electricity quantity to obtain a fitting relation of the maximum daily air temperature; and
and performing regression through a primary curve according to the weighted temperature and humidity index and the daily electricity consumption to obtain a weighted temperature and humidity index fitting relation.
Specifically, as shown in fig. 2, in order to quantify the degree of influence of the highest daily temperature on the daily power consumption, regression analysis is performed using a primary curve to obtain a linear function relation:
P2018＝1112.1T-8861.7
in the formula: p2018Representing the daily electricity consumption; t represents the highest daily temperature. Sensitivity analysis can be performed according to the regression model, and the following can be obtained: the maximum temperature T rises by 1 ℃, and the daily electricity consumption is increased by 1112.1 ten thousand kWh.
Optionally, under the condition that the analysis index is a temperature and humidity index, performing correlation analysis on the daily electricity consumption of the corresponding area through the electricity consumption analysis index, including:
constructing a temperature-humidity index which meets the following requirements:
wherein THI represents the temperature-humidity index, TempcIndicating the temperature in degrees Celsius of the day, Hmd indicating the dayRelative humidity;
weighting the temperature-humidity index based on the current date, satisfying:
WTHIC＝(10THIC+4THIC-1+THIC-2)/15
wherein, WCHIcIndicating the weighted temperature-humidity index, THIc、THIc-1、THIc-2Respectively represents the temperature and humidity indexes of the day C, the yesterday C-1 and the day before C-2.
Specifically, in this embodiment, the temperature accumulation effect refers to a phenomenon that the daily electricity change lags behind the temperature change due to a process of adapting the human body sense to the temperature change. In the embodiment, the cumulative effect is more obvious in large and medium cities, for example, the air conditioning load accounts for a higher proportion in summer and the air temperature cumulative effect is more obvious in Chongqing cities due to special geographical positions and meteorological conditions of the Chongqing cities.
Therefore, the main factors influencing the comfort of the human body in the embodiment are the air temperature and the humidity, the temperature and humidity information is introduced and analyzed through the temperature-humidity index (THI), and the specific formula is as follows:
in the formula: tempcIs the temperature in degrees centigrade; hmd is relative humidity. On the basis, the THI indexes of the current day, yesterday and the previous day are weighted to obtain a weighted temperature and humidity index (WHI) index considering the temperature and humidity cumulative effect, and the formula is as follows:
WTHIC＝(10THIC+4THIC-1+THIC-2)/15
in the formula (THI)C，THIC-1，THIC-2The temperature and humidity indexes of the current day C, yesterday C-1 and the previous day C-2 are respectively.
The calculation results of summer THI and WTHI in Chongqing city in 2018 are shown in FIG. 3. As can be seen from fig. 3, the variation range of the WTHI curve is slightly smaller than the THI curve and has a certain hysteresis, which indicates that the cumulative effect of the air temperature is more obvious in the Chongqing area. WTHI maximum occurs at 22 days 7 months, lagging 20 days 7 months of maximum daily charge occurrence in summer, which is 37152 ten thousand kWh.
Through calculation, the Pearson correlation coefficient of the WTHI index and the daily electricity consumption is 0.9152, and is higher than the correlation coefficient 0.8864 of the highest air temperature and the daily electricity consumption, so that the influence of the accumulated effect of the air temperatures on the electricity consumption is higher than the influence of the highest air temperature on the electricity consumption.
Optionally, the fitting the daily electricity consumption of the corresponding area according to the analysis index includes:
performing regression through a primary curve according to the maximum daily air temperature in summer and the corresponding daily electricity quantity to obtain a fitting relation of the maximum daily air temperature; and
and performing regression through a primary curve according to the weighted temperature and humidity index and the daily electricity consumption to obtain a weighted temperature and humidity index fitting relation.
Specifically, in order to further quantify the influence of the cumulative effect of the air temperature on the daily power consumption, a scatter diagram of the variation trend of the two is shown in fig. 4. According to the variable increase of the daily electric quantity and the daily electric quantity, dividing the daily electric quantity and the sensitive interval of the WTHI into two sections by taking the WTHI as a critical value as 83:
(1) WHI is less than or equal to 83 is a weak sensitive area, and regression analysis is carried out by using a primary curve to obtain a primary function relation:
P＝560WTHI-21405
namely, the daily electricity consumption is increased by 560 thousands kWh every time the WTHI is increased by 1 unit.
(2) WHI >83 is a strong sensitive area, and regression analysis is carried out by using a primary curve to obtain a primary function relation formula:
P＝1634.7WTHI-111290
namely, the daily electricity quantity is increased by 1634.7 ten thousand kWh every time the WZHI is increased by 1 unit.
Optionally, in a case that the analysis index is a heat index, performing correlation analysis on the electricity analysis index and the daily electricity consumption of the corresponding area, including:
and (3) constructing a heat index which meets the following requirements:
I＝1.8TempC-0.55(1.8TempC-26)*(1-Hmd)+32
wherein I represents the heat index.
Aiming at the hot weather process frequently appearing in summer in the Chongqing area, a hot index I is introduced for quantification:
I＝1.8TempC-0.55(1.8TempC-26)*(1-Hmd)+32
in this embodiment, when I ≧ 80, TempCAt a temperature of more than 35 ℃, the body feels hot, the definition lasts for 3d and more than I is more than or equal to 80, and Temp is higherCMore than or equal to 35 ℃ is the hot weather process for 1 time.
Through calculation and analysis, the Chongqing city in summer of 2018 undergoes two hot processes, and the table 1 shows the specific changes of the occurrence time of the two hot processes and the process power consumption condition, wherein the maximum increment of daily power/peak load/valley load is the difference between the maximum value in the hot process and the first day value in the process.
As can be seen from table 1, compared with the second thermal process, the first thermal process, which is shorter in duration but occurs in the early summer stage, has a greater influence on the power consumption in the Chongqing city, and the power consumption increment, peak load increment, and valley load increment are all significantly higher than those of the second thermal process. And further analysis is carried out by combining a daily electricity consumption curve and other electricity consumption data, and the daily electricity consumption, the daily maximum load and the daily minimum load in the first hot process are monotonically increased. The second heat process is longer, the daily electricity consumption is in the trend of increasing first and then decreasing, the electricity consumption is monotonically increased 7 days before the heat process, the peak value is 38972 ten thousand kWh in 7.20 days, and the maximum temperature is maintained at a higher level, but the daily electricity consumption is fluctuated and decreased.
TABLE 12018 summer hot weather course and electricity consumption change
Optionally, performing comparative analysis according to correlation analysis results of different years and fitting results of daily power consumption to predict summer power consumption includes:
and (3) comparing the correlation coefficients and the fitting relations of the daily maximum temperature and daily electricity consumption, the weighted temperature and humidity index and the daily electricity consumption in different years, and combining the relation of the sweltering index and the daily electricity consumption to predict the summer electricity consumption.
The method performs correlation analysis on the electric quantity analysis index and the daily electric quantity of the corresponding area; fitting the daily electric quantity of the corresponding area according to the analysis index; and performing comparative analysis according to the correlation analysis results of different years and the fitting result of the daily electricity consumption to predict the summer electricity consumption. Various electric quantity analysis indexes are considered, and the prediction of the electric quantity in summer is realized.
The second embodiment of the invention provides a specific example of a summer electric quantity analysis method
Analysis of influence of highest daily temperature on electric quantity
2019 the curve of the maximum temperature in summer and the daily electricity consumption shows a trend as shown in fig. 5. The Pearson correlation coefficient of the highest temperature in summer days in 2019 and the daily electricity consumption is calculated to be 0.7102, the Pearson correlation coefficient and the daily electricity consumption have strong correlation, but the correlation is obviously lower than 0.8862 in 2018, and the fact that the influence degree of summer temperature in 2019 on electricity consumption is lower than 2018 is shown.
In order to quantify the influence degree of the highest daily temperature on the daily power consumption, a linear curve is used for carrying out regression analysis to obtain a linear function relation:
P2019＝794.28T+1177.7
in the formula: p2019Representing the daily electricity consumption; t represents the highest daily temperature. Sensitivity analysis can be performed according to the regression model, and the following can be obtained: the maximum temperature T rises by 1 ℃, and the daily electricity consumption is increased by 794.28 ten thousand kWh.
Analysis of influence of air temperature cumulative effect on electric quantity
The calculation results of summer THI and WTHI in Chongqing city in 2019 are shown in FIG. 6. As can be seen from fig. 6, the trend of the WTHI curve lags behind the THI curve, indicating that the cumulative effect of the air temperature in summer is also more significant in 2019. WTHI maximum occurs at 29 days 7 months, lagging 7 months 28 days in summer where the maximum daily charge occurs, which is 35995 kWh.
Through calculation, the Pearson correlation coefficient of the WTHI index and the daily electricity consumption is 0.8234, and is higher than the correlation coefficient 0.7102 of the highest air temperature and the daily electricity consumption, so that the influence of the accumulated effect of the air temperatures on the electricity consumption is higher than the influence of the highest air temperature on the electricity consumption.
In order to further quantify the influence of the cumulative effect of the air temperature on the daily electricity consumption, a scatter diagram of the change trend of the two is shown in fig. 7. According to the variable increase of the daily electric quantity and the daily electric quantity, dividing the daily electric quantity and the sensitive interval of the WTHI into two sections by taking the WTHI as a critical value:
(1) WHI is less than or equal to 84, which is a weak sensitive area, and regression analysis is carried out by using a primary curve to obtain a primary function relation:
P＝603.18WTHI-23820
namely, the daily electricity quantity is increased by 603.18 ten thousand kWh every time the WZHI is increased by 1 unit.
(2) WHI >84 is a strong sensitive area, and regression analysis is carried out by using a primary curve to obtain a primary function relation formula:
P＝2154.7WTHI-155420
namely, the daily electricity quantity is increased by 2154.7 ten thousand kWh every time the WZHI is increased by 1 unit.
Analysis of influence of hot weather process on electric quantity
The sweltering index of each day in 2019 is calculated and counted, the temperature of the Chongqing city in summer of 2019 is low, and only one short sweltering process is carried out, and the table 2 shows the specific changes of the occurrence time of the sweltering process and the process electricity utilization condition.
As can be seen from the daily power consumption curve and other power consumption data in fig. 2, the daily power consumption, the daily maximum load, and the daily minimum load all show a monotonically increasing trend in the hot process. Because the hot process occurs at the end of 7 months, the overall temperature is relatively high, and the duration of the hot process is short, the increment of each power utilization index is obviously smaller compared with the two hot processes in summer of 2018.
TABLE 22019 summer hot weather course and electricity consumption change
2018. Contrastive analysis and prediction of influences of temperature on electricity consumption in 2019
In summer of 2018, the temperature characteristics are opposite to those in summer of 2019, namely summer in 2018 is extremely hot, and summer in 2019 is relatively cool. The characteristics of electricity consumption in summer in two years are different due to different temperature characteristics, and the characteristics of the electricity consumption in summer in 2018 and 2019 are compared and analyzed based on the calculation results:
(1) daily electricity consumption
In consideration of natural increase of electricity consumption, the average daily electricity consumption in the 6-7 months in the 2017 of 2010-plus is used as historical data for regression analysis, a regression equation is established to predict the average daily electricity consumption in the 6-7 months in the 2018 and the 2019, and the predicted values are 25749.2 kWh and 27101 kWh respectively. Under the influence of high temperature, the average daily electricity consumption of 28317 ten thousands of kWh in 2018 is higher than the predicted value of 2567.8 ten thousands of kWh; under the influence of low air temperature, the average daily electricity consumption in 2019 is 25527 ten thousand kWh, which is lower than the predicted value 1574 ten thousand kWh.
(2) Influence of maximum daily temperature on daily electricity consumption
The Pearson coefficient of the highest temperature in the year 2018 and the daily electricity consumption is 0.8862, the Pearson coefficient of the highest temperature in the year 2019 and the daily electricity consumption is 0.7142, and the influence of the temperature in the year 2018 on the daily electricity consumption is more obvious.
Comparing the regression equation of the maximum air temperature and the daily electricity consumption in 2018 and 2019:
P2018＝1112.1T-8861.7
P2019＝794.28T+1177.7
it is known that the daily electricity consumption in 2018 has higher sensitivity to the daily maximum temperature.
(3) Cumulative effect on electricity usage
The Pearson coefficient of the WZHI index and the daily electricity consumption in 2018 is 0.9152, the Pearson coefficient of the WZHI index and the daily electricity consumption in 2019 is 0.8234, and the WZHI index and the daily electricity consumption are both higher than the correlation coefficient of the highest temperature of the year and the daily electricity consumption, and the temperature accumulation effect has larger influence on the daily electricity consumption.
Comparing the regression equation, the WHI indexes in 2018 and 2019 are close to the critical value of the sensitivity interval of the daily electricity consumption, and the WHI indexes fall in the interval of [83, 84], the sensitivity of the daily electricity consumption to the accumulated temperature and humidity index on the left side of the interval is low, and the sensitivity of the daily electricity consumption to the accumulated temperature and humidity index on the right side of the interval is high. Meanwhile, in the weak sensitive area and the strong sensitive area, the daily electricity consumption in 2019 shows that the sensitivity to the WHI index is higher than that in 2018, which shows that the sensitivity of the electricity consumption to the accumulated effect of the air temperature is higher in the cold summer period.
(4) Effect of hot weather Process on Power consumption
The summer of 2018 and 2019 undergoes three sweltering courses for 6 days, 17 days and 4 days respectively. Comparing the three hot processes, the daily electricity consumption is in a monotonous increasing trend in the hot process with short duration, and the daily electricity consumption is in a trend of increasing first and then decreasing in the long-time hot process. Meanwhile, the effect of the hot process appearing in 6 months in the early summer on the sudden increase of the power consumption is more obvious.
EXAMPLE III
A third embodiment of the present invention proposes a computer-readable storage medium, on which an implementation program for information transfer is stored, which when executed by a processor implements the steps of the aforementioned method.
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.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered thereby.
Claims (8)
1. A summer power analysis method is characterized by comprising the following steps:
selecting an electric quantity analysis index, and acquiring daily electric quantity of a corresponding area in summer;
performing correlation analysis on the electric quantity analysis indexes and the daily electric quantity of the corresponding area;
fitting the daily electric quantity of the corresponding area according to the analysis index;
and performing comparative analysis according to the correlation analysis results of different years and the fitting result of the daily electricity consumption to predict the summer electricity consumption.
2. The method of claim 1, wherein the analytical indicators include a maximum daily temperature, a temperature and humidity index, a weighted temperature and humidity index, and a heat index.
3. The method of claim 2,
and under the condition that the analysis index is the highest daily temperature, performing correlation analysis on the electric quantity analysis index and the daily electric quantity of the corresponding area, wherein the correlation analysis comprises the following steps:
the highest temperature in summer and the corresponding daily electric quantity are selected, and the correlation degree is calculated through the Pearson correlation coefficient, so that the following requirements are met:
wherein p is a correlation coefficient, n is the number of selected dates, and xiIndicates daily electricity quantity, yiIndicating the daily maximum air temperature.
4. The method of claim 2, wherein in a case that the analysis index is a temperature and humidity index, performing correlation analysis by using the electricity quantity analysis index and the daily electricity quantity of the corresponding area includes:
constructing a temperature-humidity index which meets the following requirements:
wherein THI represents the temperature-humidity index, TempcIndicating the temperature in degrees celsius for the day, Hmd indicating the relative humidity for the day;
weighting the temperature-humidity index based on the current date, satisfying:
WTHIC＝(10THIC+4THIC-1+THIC-2)/15
wherein, WCHIcIndicating the weighted temperature-humidity index, THIc、THIc-1、THIc-2Respectively represent the day C,The temperature and humidity indexes of yesterday C-1 and the previous day C-2.
5. The method of claim 4, wherein said fitting the daily charge of the corresponding region according to the analysis index comprises:
performing regression through a primary curve according to the maximum daily air temperature in summer and the corresponding daily electricity quantity to obtain a fitting relation of the maximum daily air temperature; and
and performing regression through a primary curve according to the weighted temperature and humidity index and the daily electricity consumption to obtain a weighted temperature and humidity index fitting relation.
6. The method of claim 5, wherein in the case that the analysis index is a heat index, performing correlation analysis by the power analysis index and the daily power of the corresponding area comprises:
and (3) constructing a heat index which meets the following requirements:
I＝1.8TempC-0.55(1.8TempC-26)*(1-Hmd)+32
wherein I represents the heat index.
7. The method of claim 6, wherein performing comparative analysis to predict summer power usage based on the correlation analysis results for different years and the fit of daily power usage comprises:
and (3) comparing the correlation coefficients and the fitting relations of the daily maximum temperature and daily electricity consumption, the weighted temperature and humidity index and the daily electricity consumption in different years, and combining the relation of the sweltering index and the daily electricity consumption to predict the summer electricity consumption.
8. A computer-readable storage medium, characterized in that it has stored thereon a program for implementing the transfer of information, which program, when being executed by a processor, implements the steps of the method according to any one of claims 1 to 7.
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