CN109299814A - A kind of meteorological effect quantity division prediction technique - Google Patents
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Abstract
The invention discloses a kind of meteorological effect quantity division prediction techniques, comprising the following steps: S1: basic month is arranged: basic month in default spring is May, and basis month in autumn is October；S2: it calculates meteorological correlation: being divided into two parts of winter and summer and calculate separately data, if place month belongs to 11-4 month, calculate winter correlation, if place month belongs to 6-9 month, summer correlation is then calculated, is then not involved in calculating basic month, correlation calculations formula isObtain correlation data；S3: it calculates monthly meteorological effect electricity growth rate: y is passed through to the correlation data in S2t=yi‑yjWith δ=(yt/yj) * 100% formula calculated, obtain monthly meteorological effect electricity growth rate and show related coefficient.According to the rationale of science, being inferred to meteorologic factor is to influence the significant consideration of load forecast, the accuracy rate of increasing productivity prediction, and error is smaller.
Description
Technical field
The present invention relates to connect welding technology field more particularly to a kind of meteorological effect quantity division prediction technique.
Background technique
Prediction is basis, premise and the foundation of correct decisions, and correctly predicted area electricity needs level is that electric power at different levels are public
Department instructs the important foundation of Power System Planning and operation.Electric load analysis prediction needs to combine past and currently known warp
Situation, social development and the electricity market situation of helping are explored by the analysis and research to historical data and grasp each correlative factor and electricity
The inner link of power load and development and change rule, to be pushed away according to the prediction to economic situation in project period and social development
Calculate following electricity needs situation.
Power system reform further deeply, electricity market today for gradually forming, Utilities Electric Co. is in power Transmission ring
The service function of section is more prominent, carries out the load forecast of lean, and increasing productivity predictablity rate is under market environment
Layout operation plan, power supply plan, trading program basis.Conventional prediction technique combines one by the practical experience of prognosticator
Relationship between a little simple variables, is often a lack of the theoretical foundation of science, causes to predict that error is larger.The variation of electric load
It is influenced by multiple factors such as economy, meteorologies, especially as the raising and economic rapid development of Living consumption, so that
Electricity needs rapidly increases, and in summer and winter, meteorologic factor is to influence the significant consideration of load forecast.
Summary of the invention
Technical problems based on background technology, the invention proposes a kind of meteorological effect quantity division prediction techniques.
A kind of meteorological effect quantity division prediction technique proposed by the present invention, comprising the following steps:
S1: basic month is arranged: basic month in default spring is May, and basis month in autumn is October；
S2: it calculates meteorological correlation: being divided into two parts of winter and summer and calculate separately data, if place month belongs to
11-4 month winter correlation is then calculated, if place month belongs to 6-9 month, calculates summer correlation, basic month is then
It is not involved in calculating, correlation calculations formula isObtain correlation data；
S3: it calculates monthly meteorological effect electricity growth rate: y is passed through to the correlation data in S2t=yi-yjWith δ=(yt/
yj) * 100% formula calculated, obtain monthly meteorological effect electricity growth rate and show related coefficient；
S4: display: the related coefficient in display S3 judges the related coefficient of meteorological effect electricity Yu all kinds of meteorologies respectively,
The related coefficient of meteorological effect electricity growth rate and all kinds of meteorologies, the two same type are compared, and show biggish one group of related coefficient；
S5: calculating electricity influences: calculating the relationship between leading meteorologic factor and meteorological effect electricity, establishes leading meteorology
Polynomial-fitting function between factor and meteorological effect electricity, is calculated using multiple linear regression equations:With
S6: reprocessing processing reprocessing: is carried out to the data of output.
Preferably, in the S2, the electricity of every month is yi, i=1,2, L, 12, remove basic month yj, j=5,10 is not
Consider outer, other, electricity should subtract the historical basis moon electricity y nearest apart from the month every monthsj, constitute monthly meteorological
Influence electricity yt, t=1, L, 4,6, L 9,11,12.
Preferably, in the S3, for winter correlation calculations in the presence of across year property.
Preferably, in the S5, x is main weather factor, ytFor meteorological effect electricity, δ is the growth of meteorological effect electricity
Rate.
Preferably, it in the S5, is calculated using meteorological sensitivity analysis algorithm, to yt, derivation of δ obtain meteorology
The amplification that influence electricity or meteorological effect electricity growth rate are changed with meteorologic factor.
Preferably, in the S5, if prediction month is in winter, the high index of winter correlation is calculated, otherwise is calculated
The high index of summer correlation, basic month do not calculate.
Preferably, in the S6, the meteorological index of output should be integer, should not be decimal.
Preferably, it in the S6, if input quantity is meteorological effect electricity growth rate, needs to convert, meteorological effect
Electricity=meteorological effect electricity growth rate * nearest historical basis moon electricity.
Beneficial effects of the present invention: according to the rationale of science, being inferred to meteorologic factor is to influence load forecast
Significant consideration, the accuracy rate of increasing productivity prediction, error is smaller.
Specific embodiment
Combined with specific embodiments below the present invention is made further to explain.
Embodiment 1
A kind of meteorological effect quantity division prediction technique is proposed in the present embodiment, comprising the following steps:
S1: basic month is arranged: basic month in default spring is May, and basis month in autumn is October；
S2: it calculates meteorological correlation: being divided into two parts of winter and summer and calculate separately data, if place month belongs to
11-4 month winter correlation is then calculated, if place month belongs to 6-9 month, calculates summer correlation, basic month is then
It is not involved in calculating, correlation calculations formula isObtain correlation data；
S3: it calculates monthly meteorological effect electricity growth rate: y is passed through to the correlation data in S2t=yi-yjWith δ=(yt/
yj) * 100% formula calculated, obtain monthly meteorological effect electricity growth rate and show related coefficient；
S4: display: the related coefficient in display S3 judges the related coefficient of meteorological effect electricity Yu all kinds of meteorologies respectively,
The related coefficient of meteorological effect electricity growth rate and all kinds of meteorologies, the two same type are compared, and show biggish one group of related coefficient；
S5: calculating electricity influences: calculating the relationship between leading meteorologic factor and meteorological effect electricity, establishes leading meteorology
Polynomial-fitting function between factor and meteorological effect electricity, is calculated using multiple linear regression equations:With
S6: reprocessing processing reprocessing: is carried out to the data of output.
In the present embodiment, in the S2, the electricity of every month is yi, i=1,2, L, 12, remove basic month yj, j=5,
10 do not consider outer, other, electricity should subtract the historical basis moon electricity y nearest apart from the month every monthsj, constitute monthly
Meteorological effect electricity yt, t=1, L, 4,6, L 9,11,12, it is the presence of across year property for winter correlation calculations in the S3
, in the S5, x is main weather factor, ytFor meteorological effect electricity, δ is meteorological effect electricity growth rate, in the S5,
It is calculated using meteorological sensitivity analysis algorithm, to yt, derivation of δ show that meteorological effect electricity or meteorological effect electricity increase
The amplification that long rate is changed with meteorologic factor in the S5, if prediction month is in winter, calculates the high finger of winter correlation
Mark, on the contrary the high index of summer correlation is calculated, it does not calculate in basic month, in the S6, the meteorological index of output should be whole
Number, should not be decimal, in the S6, if input quantity is meteorological effect electricity growth rate, need to convert, meteorological effect
Electricity=meteorological effect electricity growth rate * nearest historical basis moon electricity.
Embodiment 2
With the data instance of somewhere 2014-2017, table 1 is the moon electricity of somewhere business over the years, and monthly number of days is rolled over
Be counted as average daily electricity, be shown in Table 2, select month based on May, October, other every months electricity subtract it is nearest apart from the month
The historical basis moon, electricity obtained meteorological effect electricity, was shown in Table 3, and table 4 is that somewhere 2014-2017 commercial weather influences electricity and increases
Long rate calculates separately summer meteorology index of correlation and meteorological effect electricity, winter meteorology index of correlation and meteorological effect electricity
Correlativity, in order to which unified each year all takes standardization, analysis the results are shown in Table 5, table 6.
Table 1
| Average daily electricity | 2014 | 2015 | 2016 | 2017 |
| January | 16.34 | 20.33 | 21.09 | 24.52 |
| 2 months | 19.39 | 22.39 | 24.97 | 29.39 |
| March | 14.07 | 17.39 | 20.16 | 24.37 |
| April | 14.19 | 17.62 | 19.31 | 23.21 |
| May | 12.10 | 14.55 | 16.52 | 18.43 |
| June | 14.38 | 15.46 | 18.38 | 21.94 |
| July | 15.11 | 16.93 | 19.38 | 23.35 |
| August | 18.60 | 21.45 | 23.61 | 27.03 |
| September | 19.43 | 21.60 | 24.82 | 26.69 |
| October | 13.63 | 15.93 | 17.99 | 20.31 |
| November | 14.95 | 17.38 | 19.16 | 22.88 |
| December | 17.06 | 17.95 | 21.81 | 25.79 |
Table 2
Table 3
Table 4
Table 5
Table 6
Participating in the charge value that related coefficient calculates has two classes: one is monthly meteorological effect electricity related coefficient, and one is
Monthly meteorological effect electricity growth rate related coefficient；Participating in the meteorological data that related coefficient calculates has four classes: the monthly mean temperature (moon
The average value of interior each mean daily temperature), monthly mean of daily maximum temperature (average value of each max. daily temperature in the moon), monthly average lowest temperature
(average value of each Daily minimum temperature in the moon), monthly total precipitation (aggregate-value of each intra day ward in the moon) are spent, when calculating meteorological
Need calculate within wrong one month, i.e., meteorology in May corresponds to electricity in June, and the highest index of summer correlation is meteorological effect electricity
With summer average maximum, and winter several meteorological index are not very high with meteorological effect electricity correlation, therefore following
Summer correlation models are only established, fitting of a polynomial is established, it is optimal as follows to obtain fitting effect:
Y=-0.0181x2-0.7699x+8.3066
To above formula equation derivation, sensitivity equation is obtained:
Y '=0.0362x-0.77.
The foregoing is only a preferred embodiment of the present invention, but scope of protection of the present invention is not limited thereto,
Anyone skilled in the art in the technical scope disclosed by the present invention, according to the technique and scheme of the present invention and its
Inventive concept is subject to equivalent substitution or change, should be covered by the protection scope of the present invention.
Claims (8)
1. a kind of meteorological effect quantity division prediction technique, which comprises the following steps:
S1: basic month is arranged: basic month in default spring is May, and basis month in autumn is October；
S2: it calculates meteorological correlation: being divided into two parts of winter and summer and calculate separately data, if place month belongs to 11-4
Month winter correlation is then calculated, if place month belongs to 6-9 month, calculates summer correlation, do not join then in basic month
With calculating, correlation calculations formula isObtain correlation data；
S3: it calculates monthly meteorological effect electricity growth rate: y is passed through to the correlation data in S2t=yi-yjWith δ=(yt/yj)*
100% formula is calculated, and is obtained monthly meteorological effect electricity growth rate and is shown related coefficient；
S4: display: the related coefficient in display S3 judges the related coefficient of meteorological effect electricity Yu all kinds of meteorologies respectively, meteorological
The related coefficient of electricity growth rate and all kinds of meteorologies is influenced, the two same type is compared, and shows biggish one group of related coefficient；
S5: calculating electricity influences: calculating the relationship between leading meteorologic factor and meteorological effect electricity, establishes leading meteorologic factor
Polynomial-fitting function between meteorological effect electricity, is calculated using multiple linear regression equations:
With
S6: reprocessing processing reprocessing: is carried out to the data of output.
2. a kind of meteorological effect quantity division prediction technique according to claim 1, which is characterized in that in the S2, often
A month electricity is yi, i=1,2, L, 12, remove basic month yj, j=5,10 do not consider outer, other, electricity should every months
Subtract the historical basis moon electricity y nearest apart from the monthj, constitute monthly meteorological effect electricity yt, t=1, L, 4,6, L 9,11,
12。
3. a kind of meteorological effect quantity division prediction technique according to claim 1, which is characterized in that right in the S3
In winter correlation calculations in the presence of across year property.
4. a kind of meteorological effect quantity division prediction technique according to claim 1, which is characterized in that in the S5, x is
Main weather factor, ytFor meteorological effect electricity, δ is meteorological effect electricity growth rate.
5. a kind of meteorological effect quantity division prediction technique according to claim 1, which is characterized in that in the S5, benefit
It is calculated with meteorological sensitivity analysis algorithm, to yt, derivation of δ show that meteorological effect electricity or meteorological effect electricity increase
The amplification that rate is changed with meteorologic factor.
6. a kind of meteorological effect quantity division prediction technique according to claim 1, which is characterized in that in the S5, such as
Fruit predicts month in winter, then calculates the high index of winter correlation, otherwise calculates the high index of summer correlation, basic month
It does not calculate.
7. a kind of meteorological effect quantity division prediction technique according to claim 1, which is characterized in that defeated in the S6
Meteorological index out should be integer, should not be decimal.
8. a kind of meteorological effect quantity division prediction technique according to claim 1, which is characterized in that in the S6, such as
Fruit input quantity is meteorological effect electricity growth rate, is needed to convert, and meteorological effect electricity=meteorological effect electricity growth rate * is most
Close historical basis moon electricity.
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Cited By (4)
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| CN110795698A (en) * | 2019-10-30 | 2020-02-14 | 国网重庆市电力公司电力科学研究院 | Summer electric quantity analysis method and readable storage medium |
| CN111242805A (en) * | 2020-01-09 | 2020-06-05 | 南方电网科学研究院有限责任公司 | Method and device for calculating increase rate of power consumption |
| CN111563644A (en) * | 2020-02-24 | 2020-08-21 | 中国气象局公共气象服务中心 | Method for determining summer electricity utilization index, computer-readable storage medium and electronic device |
| CN111598349A (en) * | 2020-05-22 | 2020-08-28 | 国网重庆市电力公司电力科学研究院 | Short-term power consumption prediction method and device and readable storage medium |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110795698A (en) * | 2019-10-30 | 2020-02-14 | 国网重庆市电力公司电力科学研究院 | Summer electric quantity analysis method and readable storage medium |
| CN111242805A (en) * | 2020-01-09 | 2020-06-05 | 南方电网科学研究院有限责任公司 | Method and device for calculating increase rate of power consumption |
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| CN111563644A (en) * | 2020-02-24 | 2020-08-21 | 中国气象局公共气象服务中心 | Method for determining summer electricity utilization index, computer-readable storage medium and electronic device |
| CN111598349A (en) * | 2020-05-22 | 2020-08-28 | 国网重庆市电力公司电力科学研究院 | Short-term power consumption prediction method and device and readable storage medium |
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