CN106447134A - Classified short-term power forecast method for small hydropower stations - Google Patents

Classified short-term power forecast method for small hydropower stations Download PDF

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CN106447134A
CN106447134A CN201610962307.9A CN201610962307A CN106447134A CN 106447134 A CN106447134 A CN 106447134A CN 201610962307 A CN201610962307 A CN 201610962307A CN 106447134 A CN106447134 A CN 106447134A
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CN106447134B (en
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范强
文贤馗
林呈辉
肖永
徐梅梅
顾威
徐玉韬
龙秋风
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Electric Power Research Institute of Guizhou Power Grid Co Ltd
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Abstract

The invention discloses a classified short-term power forecast method for small hydropower stations. The classified short-term power forecast method includes collecting data information of regional runoff-type small hydropower stations and regional non-runoff-type small hydropower stations; forecasting the short-term power of the regional runoff-type small hydropower stations through runoff-type small hydropower station direction forecast and BP neural network forecast, calculating weight coefficients of forecast values of the two methods according to the entropy weight method, and calculating short-term power forecast values of the regional runoff-type small hydropower stations at the j moment; calculating short-term power forecast values of the regional non-runoff-type small hydropower stations at the j moment; acquiring short-term power forecast values of regional small hydropower stations according to the short-term power forecast values of the regional runoff-type small hydropower stations at the j moment and the short-term power forecast values of the regional non-runoff-type small hydropower stations at the j moment. By the classified short-term power forecast method, the technical problems, such as low forecast accuracy and large forecast error in short-term power forecast of the small hydropower stations in the prior art, caused due to the fact that the influence factors of the small hydropower stations are different and data sources and data sizes are highly different when the short-term power forecast of the small hydropower stations is performed according to the prior art are solved.

Description

A kind of small power station's classification short term power Forecasting Methodology
Technical field
The short term power prediction side the invention belongs to small power station's short term power Predicting Technique, more particularly to a kind of small power station are classified Method.
Background technology
Increasingly serious with environmental pollution and energy shortage problem, small power station is used as the renewable replacement energy of cleaning green Source, different from big-and-middle-sized water power, is mainly distributed on the mountain area of remote geographic location, is the important power supply composition portion in rural area Point, with sporadic development, become on the spot net, on the spot power supply, build and that cost of electricity-generating is low, the construction period is short, return income is fast etc. is excellent Point, therefore quickly grows under the support of country and local policy with irreplaceable advantage, solves well partly The short of electricity situation in area.
At the same time, the development of small power station also brings some problems, and small power station is divided into radial-flow type small power station and non-radial-flow type Small power station.At present great majority are run-of-river small hydropower station, its no storage capacity regulating power substantially, with strong seasonality, flood season water More, in full hair-like state more than small power station, withered phase water is less, and small power station's generated energy is substantially reduced, and its generation load performance is very strong Uncertainty.Small hydropower station changes of operating modes is larger, and (non-radial-flow type water power includes non-footpath by the non-radial-flow type water power in upstream The big water power of streaming and non-radial-flow type small power station) affect larger, Unit Commitment is frequent, causes the fluctuation of major network trend big, increases circuit The out-of-limit probability of trend.Rather than run-of-river small hydropower station, although its proportion in systems is less, and reservoir has necessarily Regulation performance, can participate in regional load peak regulation work, but its adjust poor-performing, the wet season small power station City Regions with The increase of discharge, is easily clashed with big-and-middle-sized water power, electrical network major network is impacted, if sending out for small power station is not grasped in scheduling Electrical information, the harmful effect brought by power network safety operation and dispatching of power netwoks etc. will be notable all the more.Accordingly, it would be desirable to right Small power station carries out short term power prediction.But as radial-flow type small power station and non-radial-flow type small power station are in the method for operation by being affected Factor difference, and as the difference of the aspects such as way to manage causes the difference of the source data amount of data big, existing little water Electric Forecasting Methodology does not all consider the problem of the above, if single enters according to radial-flow type small power station or non-radial-flow type small power station Row prediction, will all cause prior art small power station short term power precision of prediction is low, and forecast error is big etc..
Content of the invention：
The technical problem to be solved in the present invention：A kind of small power station classification short term power Forecasting Methodology is provided, small power station is divided It is that radial-flow type small power station and the classification of non-radial-flow type small power station carry out short term power prediction, to solve existing small power station's Forecasting Methodology All do not consider that radial-flow type small power station is different with the influence factor that non-radial-flow type small power station is subject in the method for operation, and due to manager The difference such as formula causes the difference of the source data amount of data greatly, and single little according to radial-flow type small power station or non-radial-flow type Water power is predicted causing that prior art small power station short term power precision of prediction is low, the technical problem such as forecast error is big.
Technical solution of the present invention：
A kind of small power station's classification short term power Forecasting Methodology, it is characterised in that：It includes
The data message of step 1, the regional radial-flow type small power station of collection and non-radial-flow type water power；
Step 2, by radial-flow type small power station directly prediction and BP neural network prediction to carry out radial-flow type small power station respectively short Phase power prediction, and the weight coefficient of two methods predictive value is asked for using entropy assessment, calculate regional radial-flow type small power station jth Moment short term power predictive value Prj；
Step 3, the regional non-radial-flow type small power station jth moment short term power predictive value P of calculatingnrj；
Step 4, according to regional radial-flow type small power station jth moment short term power predictive value PrjWith regional non-radial-flow type small power station Jth moment short term power predictive value Pnrj, obtain regional small power station's short term power predictive value Pj=Prj+Pnrj.
Collection area radial-flow type small power station data message described in step 1 includes meteorological data, goes out force data, startup-shutdown number According to the meteorological data should be including the temperature of radial-flow type small power station region, air pressure, humidity, wind direction, wind speed, rainfall number According to；The data message of the non-radial-flow type water power includes meteorological data, goes out force data, the outbound water yield, startup-shutdown data, meteorological number Little according to temperature, air pressure, humidity, wind direction, wind speed, rainfall product data and the non-radial-flow type that should include non-radial-flow type water power region The time-of-use tariffs data of the Medium and long term generation scheduling of water power and area power grid.The regional radial-flow type small power station jth moment described in step 2 Short term power predictive value PrjComputational methods include：
Step 2.1, discharge Q in i-th radial-flow type small power station jth moment of calculatingrij=Qr1ij+Qr2ij, in formula：Qr1ijFor The rainfall of radial-flow type small power station region；Qr2ijThe outbound water yield for the non-radial-flow type water power in upstream；
Step 2.2, i-th radial-flow type small power station the first short term power of jth moment predictive value P of calculatingr1ij,
Pr1ij=ki·Qrij·HiIn formula：Pr1ijFor i-th radial-flow type small power station the first short term power of jth moment predictive value Pr1ij, unit is W；QrijFor the discharge in i-th radial-flow type small power station jth moment, unit is m3/s；HiFor i-th radial-flow type The average water head of small power station, unit is m；kiPower factor for i-th radial-flow type small power station；
Calculate the first short term power predictive value P in regional radial-flow type small power station jth momentr1j；
In formula：N is the total quantity of regional radial-flow type small power station；
Step 2.3, according to i-th radial-flow type small power station meteorological data, go out force data, carry out BP neural network prediction, obtain
The second short term power predictive value P to the regional radial-flow type small power station jth momentr2j；
Step 2.4, determine Pr1jAnd Pr2jWeight coefficient wr1、wr2, and prediction jth moment day is calculated, regional radial-flow type is little Water
Electric short term power predictive value Prj,
Step 2.5, calculating predict that the history of ξ r days a few days ago goes out the error ε of force data and prediction datarIf, continuous ξ r days Error εrRadial-flow type small power station short term power precision of prediction threshold value er for setting is all higher than, re-starts step 2.5；Less than then defeated Go out to predict jth moment day area radial-flow type small power station short term power predictive value Prj.
The method bag of the second short term power predictive value Pr2j in the regional radial-flow type small power station jth moment described in step 2.3 Include：
Step 2.3.1, three layers of BP neural network are used, the input vector of given BP neural network power prediction model goes forward side by side Row normalized；The input layer number of the BP neural network power prediction model is 7, according to institute of radial-flow type small power station Temperature Tr in the meteorological data in region, air pressure Pr, humidity Hr, wind direction Wdr, wind speed Wsr, rainfall Qr1, the non-runoff in upstream Formula water power outbound water yield Qr2, forms the input vector Y of BP neural network power prediction modelr=[Tr, Pr, Hr, Wdr, Wsr, Qr1、Qr2]；Described its formula is normalized to input vector Yr it is
Y' in formularijFor jth moment, the normalization of i-th radial-flow type small power station Input vector after process；YrijFor jth moment, the input vector before i-th radial-flow type small power station normalized；max(Yri) With min (Yri) represent maximum and the minima of i-th radial-flow type small power station input vector respectively；
Step 2.3.2, selection prediction δ r day data a few days ago are trained to BP neural network, obtain as training sample BP neural network power prediction model after must training；
Step 2.3.3, will prediction jth moment day, temperature Tr of i-th radial-flow type small power station regionij, air pressure Prij, humidity Hrij, wind direction Wdrij, wind speed Wsrij, rainfall Qr1ijOutbound water yield Q with the non-radial-flow type water power in upstreamr2ijAs Input layer, is input in the BP neural network power prediction model after training, and output layer data is prediction jth moment day, i-th Radial-flow type small power station the second short term power predictive value pr2ij；
Step 2.3.4, the second short term power predictive value P in calculating regional radial-flow type small power station jth momentr2j
P in formular2ijFor predicting jth moment day, i-th the second short-term of radial-flow type small power station Power prediction value, n is the total quantity of regional radial-flow type small power station.
Regional radial-flow type small power station short term power predictive value P described in step 2.4 and step 2.5rjComputational methods include
Step 2.4.1 is according to prediction θ a few days agorIt history meteorological data and history go out force data, calculate first respectively short Phase power prediction value Pr1(1)j...Pr1(θr)jWith the second short term power predictive value Pr2(1)j...Pr2j(θr)j, and count actual power value For Pr1...Prθr；
Step 2.4.2, respectively with the first short term power predictive value and the second short term power predictive value and actual power value Error Absolute Value is evaluation index, determines weight coefficient w with entropy assessmentr1And wr2；wr1For Pr1jWeight coefficient, wr2For Pr2j's Weight coefficient；wr1+wr2=1；
Step 2.4.3, pass through formula Prj=wr1×Pr1j+wr2×Pr2jCalculate prediction jth moment day, regional radial-flow type Small power station short term power predictive value Prj；
Step 2.4.4, calculating predict that the history of ξ r days a few days ago goes out the error ε r of force data and prediction data, if continuous ξ r Its error ε r is all higher than radial-flow type small power station short term power precision of prediction threshold value er for setting, and re-starts step 2.4.3；It is less than Then jth moment day is predicted in output, regional radial-flow type small power station short term power predictive value Prj.
The non-radial-flow type small power station jth moment short term power predictive value P in calculating area described in step 3nrjMethod include under State step：
When step 3.1, the historical data according to k-th non-radial-flow type small power station of jth moment and meteorological data calculate jth Carve, k-th non-the first short term power of radial-flow type small power station predictive value Pnr1kj；Pnr1kj=kk·Qnrkj·HkjIn formula：kkFor k-th The power factor of non-radial-flow type small power station, HkjHead for k-th non-radial-flow type small power station of jth moment；QnrkjFor the jth moment The discharge of k Ge Fei radial-flow type small power station；
Step 3.2, jth moment the first short term power predictive value P of the regional non-radial-flow type small power station of calculatingnr1j；In formula：M is the quantity of regional non-radial-flow type small power station；
Step 3.3, in the jth moment, historical data according to the non-radial-flow type small power station of k-th radial-flow type, meteorological data and The time-of-use tariffs of area power grid, are calculated the second short term power prediction of regional non-radial-flow type small power station using support vector machine Value Pnr2j；
Step 3.4, determine Pnr1jAnd Pnr2jWeight coefficient wnr1、wnr2, calculate the non-radial-flow type in prediction jth moment day area Small power station short term power predictive value Pnrj；
ξ a few days ago is predicted in step 3.5, calculatingnrIt history goes out the error ε of force data and prediction datanrIf, continuous ξnr Its error εnrIt is all higher than non-radial-flow type small power station short term power precision of prediction threshold value e for settingnr, then re-start step 3.4； It is less than or equal to and then exports non-radial-flow type small power station short term power predictive value Pnrj.
Second short term power predictive value P of the non-radial-flow type small power station in area described in step 3.3nr2jComputational methods, its Step includes：
Step 3.3.1, the input vector of given support vector machine power prediction model are simultaneously normalized；According to non- The meteorological data of radial-flow type small power station region：Temperature Tnr, air pressure Pnr, humidity Hnr, wind direction Wdnr, wind speed WnrsAnd rainfall Qnr1, area power grid time-of-use tariffs Cnr, formed support vector machine power prediction model input vector Ynr:
Ynr=[Tnr、Pnr、Hnr、Wdnr、Wsnr、Qnr1、Cnr]；
Input vector Ynr is normalized, and its formula is：
In formula：Y'nrkjFor jth moment, the normalization of k-th non-radial-flow type small power station Input vector after process；YnrkjFor jth moment, the input vector before k-th non-radial-flow type small power station normalized；max (Ynrk) and min (Ynrk) represent the maximum of k-th non-radial-flow type small power station variable and minima respectively；
δ a few days ago is predicted in step 3.3.2, selectionnrIts YnrkjData are instructed using support vector machine as training sample Practice, obtain the support vector machine power prediction model after training；
Step 3.3.3, will prediction jth moment day, temperature T of k-th non-radial-flow type small power station regionnrkj, air pressure Pnrkj, humidity Hnrkj, wind direction Wdnrkj, wind speed Wsnrkj, rainfall Qnr1kjTime-of-use tariffs C with area power gridnrkjAs input Layer, is input in the support vector machine power prediction model after training, and output layer data is prediction jth moment day, k-th non-footpath Streaming small power station short term power predictive value pnr2kj；
Step 3.3.4, regional non-radial-flow type small power station jth moment the second short term power predictive value P of calculatingnr2j, its formula For：
P described in step 3.4nr1jAnd Pnr2jWeight coefficient wnr1、wnr2Determination method include：
θ a few days ago is predicted in step 3.4.1, acquisitionnrIt history meteorological data and history go out force data；
Step 3.4.2, calculate respectively regional non-radial-flow type small power station first prediction performance number Pnr1(1)j...Pnr1(θnr)j With the second power prediction value Pnr2(1)j...Pnr2j(θnr)j；
Step 3.4.3, acquisition actual power value are Pnr1...Pnrθnr；
Step 3.4.4, with predict performance number and actual power value Error Absolute Value as evaluation index, determined with entropy assessment Weight coefficient wnr1、wnr2, wnr1+wnr2=1.
Calculate the non-radial-flow type small power station short term power predictive value P in prediction jth moment day areanrjFormula be：
Pnrj=wnr1×Pnr1j+wnr2×Pnr2j.
Beneficial effects of the present invention：
The present invention provides a kind of small power station and classifies short term power Forecasting Methodology, and small power station is divided into radial-flow type small power station and non- Radial-flow type small power station carries out classification prediction, and radial-flow type small power station and non-radial-flow type small power station are utilized respectively combination forecasting method and enter Row short term power is predicted, to make up the prediction defect of single method, while ask for the power of two methods predictive value using entropy assessment Weight coefficient, and track continuous operation forecast error in a few days and in time weight coefficient is modified, to efficiently reduce prediction by mistake Difference, raising precision of prediction.Solve existing small power station's Forecasting Methodology and all do not consider radial-flow type small power station and the little water of non-radial-flow type Influence factor's difference that electricity is subject in the method for operation, and as the difference such as way to manage causes the difference of the source data amount of data Different big, and single be predicted causing prior art small power station short-term according to radial-flow type small power station or non-radial-flow type small power station Power prediction precision is low, the technical problem such as forecast error is big.
Specific embodiment
A kind of small power station's classification short term power Forecasting Methodology, it includes：
The data message of step 1, the regional radial-flow type small power station of collection and non-radial-flow type water power；
Collection area radial-flow type small power station data message described in step 1 includes meteorological data, goes out force data, startup-shutdown number According to the meteorological data should be including the temperature of radial-flow type small power station region, air pressure, humidity, wind direction, wind speed, rainfall number According to；The data message of the non-radial-flow type water power includes meteorological data, goes out force data, the outbound water yield, startup-shutdown data, meteorological number Little according to temperature, air pressure, humidity, wind direction, wind speed, rainfall product data and the non-radial-flow type that should include non-radial-flow type water power region The time-of-use tariffs data of the Medium and long term generation scheduling of water power and area power grid.
Geographic position data should include affiliated basin situation and the geographical position of non-radial-flow type water power and radial-flow type small power station Put.
Step 2, by radial-flow type small power station directly prediction and BP neural network prediction to carry out radial-flow type small power station respectively short Phase power prediction, and the weight coefficient of two methods predictive value is asked for using entropy assessment, calculate regional radial-flow type small power station jth Moment short term power predictive value Prj；
Area radial-flow type small power station jth moment short term power predictive value P described in step 2rjComputational methods include：
Step 2.1, in the jth moment, by the startup-shutdown feelings of the non-radial-flow type water power of i-th radial-flow type small power station and its upstream The rainfall Q of radial-flow type small power station region in condition, meteorological datar1ijOutbound water yield Q with the non-radial-flow type water power in upstreamr2ij, Obtain discharge Q of i-th radial-flow type small power stationrij, calculate discharge Q in i-th radial-flow type small power station jth momentrij= Qr1ij+Qr2ij, in formula：Qr1ijRainfall for radial-flow type small power station region；Qr2ijOutbound for the non-radial-flow type water power in upstream The water yield；
Step 2.2, i-th radial-flow type small power station the first short term power of jth moment predictive value P of calculatingr1ij,
Pr1ij=ki·Qrij·HiIn formula：Pr1ijFor i-th radial-flow type small power station the first short term power of jth moment predictive value Pr1ij, unit is W；QrijFor the discharge in i-th radial-flow type small power station jth moment, unit is m3/s；HiFor i-th radial-flow type The average water head of small power station, unit is m；kiPower factor for i-th radial-flow type small power station；
Calculate the first short term power predictive value P in regional radial-flow type small power station jth momentr1j；
In formula：N is the total quantity of regional radial-flow type small power station；
Step 2.3, according to i-th radial-flow type small power station meteorological data, go out force data, carry out BP neural network prediction, obtain
The second short term power predictive value P to the regional radial-flow type small power station jth momentr2j；
Step 2.3.1, three layers of BP neural network are used, the input vector of given BP neural network power prediction model goes forward side by side Row normalized；The input layer number of BP neural network power prediction model is 7, according to radial-flow type small power station location Temperature Tr in the meteorological data in domain, air pressure Pr, humidity Hr, wind direction Wdr, wind speed Wsr, rainfall Qr1, the non-radial-flow type water in upstream Electric outbound water yield Qr2, forms the input vector Y of BP neural network power prediction modelr=[Tr, Pr, Hr, Wdr, Wsr, Qr1, Qr2]；Described its formula is normalized to input vector Yr it is
Y' in formularijFor jth moment, the normalization of i-th radial-flow type small power station Input vector after process；YrijFor jth moment, the input vector before i-th radial-flow type small power station normalized；max(Yri) With min (Yri) represent maximum and the minima of i-th radial-flow type small power station input vector respectively；
Method of the node in hidden layer using empirical equation with hands-on determines, output layer nodes are input layer data The radial-flow type small power station short term power predictive value in corresponding moment
Step 2.3.2, selection prediction δ r day data a few days ago are trained to BP neural network, obtain as training sample BP neural network power prediction model after must training；Wherein, δ r is bigger, represents the nerve net that historical data is more, after training The network precision that predicts the outcome is higher.
Step 2.3.3, will prediction jth moment day, temperature Tr of i-th radial-flow type small power station regionij, air pressure Prij, humidity Hrij, wind direction Wdrij, wind speed Wsrij, rainfall Qr1ijOutbound water yield Q with the non-radial-flow type water power in upstreamr2ijAs Input layer, is input in the BP neural network power prediction model after training, and output layer data is prediction jth moment day, i-th Radial-flow type small power station the second short term power predictive value pr2ij；
Step 2.3.4, the second short term power predictive value P in calculating regional radial-flow type small power station jth momentr2j
P in formular2ijFor predicting jth moment day, i-th the second short-term of radial-flow type small power station Power prediction value, n is the total quantity of regional radial-flow type small power station.
Step 2.4, determine Pr1jAnd Pr2jWeight coefficient wr1、wr2, and prediction jth moment day is calculated, regional radial-flow type is little Water power short term power predictive value Prj,
Step 2.5, calculating predict that the history of ξ r days a few days ago goes out the error ε of force data and prediction datarIf, continuous ξ r days Error εrRadial-flow type small power station short term power precision of prediction threshold value er for setting is all higher than, re-starts step 2.5；Less than then defeated Go out to predict jth moment day area radial-flow type small power station short term power predictive value Prj.
Regional radial-flow type small power station short term power predictive value P described in step 2.4 and step 2.5rjComputational methods include
Step 2.4.1 is according to prediction θ a few days agorIt history meteorological data and history go out force data, calculate first respectively short Phase power prediction value Pr1(1)j...Pr1(θr)jWith the second short term power predictive value Pr2(1)j...Pr2j(θr)j, and count actual power value For Pr1...Prθr；
Step 2.4.2, respectively with the first short term power predictive value and the second short term power predictive value and actual power value Error Absolute Value is evaluation index, determines weight coefficient w with entropy assessmentr1And wr2；wr1For Pr1jWeight coefficient, wr2For Pr2j's Weight coefficient；wr1+wr2=1；
Step 2.4.3, pass through formula Prj=wr1×Pr1j+wr2×Pr2jCalculate prediction jth moment day, regional radial-flow type Small power station short term power predictive value Prj；
Step 2.4.4, calculating predict that the history of ξ r days a few days ago goes out the error ε r of force data and prediction data, if continuous ξ r Its error ε r is all higher than radial-flow type small power station short term power precision of prediction threshold value er for setting, and re-starts step
2.4.3；Less than then output prediction jth moment day, regional radial-flow type small power station short term power predictive value Prj.
Step 3, the regional non-radial-flow type small power station jth moment short term power predictive value P of calculatingnrj；
The non-radial-flow type small power station jth moment short term power predictive value P in calculating area described in step 3nrjMethod include under State step：
When step 3.1, the historical data according to k-th non-radial-flow type small power station of jth moment and meteorological data calculate jth Carve, k-th non-the first short term power of radial-flow type small power station predictive value Pnr1kj；Pnr1kj=kk·Qnrkj·HkjIn formula：kkFor k-th The power factor of non-radial-flow type small power station, HkjHead for k-th non-radial-flow type small power station of jth moment；QnrkjFor the jth moment The discharge of k Ge Fei radial-flow type small power station；
Step 3.2, jth moment the first short term power predictive value P of the regional non-radial-flow type small power station of calculatingnr1j；In formula：M is the quantity of regional non-radial-flow type small power station；
Step 3.3, in the jth moment, historical data according to the non-radial-flow type small power station of k-th radial-flow type, meteorological data and The time-of-use tariffs of area power grid, are calculated the second short term power prediction of regional non-radial-flow type small power station using support vector machine Value Pnr2j；
Second short term power predictive value P of the non-radial-flow type small power station in area described in step 3.3nr2jComputational methods, its Step includes：
Step 3.3.1, the input vector of given support vector machine power prediction model are simultaneously normalized；According to non- The meteorological data of radial-flow type small power station region：Temperature Tnr, air pressure Pnr, humidity Hnr, wind direction Wdnr, wind speed WnrsAnd rainfall Qnr1, area power grid time-of-use tariffs Cnr, formed support vector machine power prediction model input vector Ynr:
Ynr=[Tnr、Pnr、Hnr、Wdnr、Wsnr、Qnr1、Cnr]；
Input vector Ynr is normalized, and its formula is：
In formula：Y'nrkjFor jth moment, the normalization of k-th non-radial-flow type small power station Input vector after process；YnrkjFor jth moment, the input vector before k-th non-radial-flow type small power station normalized；max (Ynrk) and min (Ynrk) represent the maximum of k-th non-radial-flow type small power station variable and minima respectively；
δ a few days ago is predicted in step 3.3.2, selectionnrIts YnrkjData are instructed using support vector machine as training sample Practice, obtain the support vector machine power prediction model after training；
Step 3.3.3, will prediction jth moment day, temperature T of k-th non-radial-flow type small power station regionnrkj, air pressure Pnrkj, humidity Hnrkj, wind direction Wdnrkj, wind speed Wsnrkj, rainfall Qnr1kjTime-of-use tariffs C with area power gridnrkjAs input Layer, is input in the support vector machine power prediction model after training, and output layer data is prediction jth moment day, k-th non-footpath Streaming small power station short term power predictive value pnr2kj；
Step 3.3.4, regional non-radial-flow type small power station jth moment the second short term power predictive value P of calculatingnr2j, its formula For：
Step 3.4, determine Pnr1jAnd Pnr2jWeight coefficient wnr1、wnr2, calculate the non-radial-flow type in prediction jth moment day area Small power station short term power predictive value Pnrj；
P described in step 3.4nr1jAnd Pnr2jWeight coefficient wnr1、wnr2Determination method include：
θ a few days ago is predicted in step 3.4.1, acquisitionnrIt history meteorological data and history go out force data；
Step 3.4.2, calculate respectively regional non-radial-flow type small power station first prediction performance number Pnr1(1)j...Pnr1(θnr)j With the second power prediction value Pnr2(1)j...Pnr2j(θnr)j；
Step 3.4.3, acquisition actual power value are Pnr1...Pnrθnr；
Step 3.4.4, with predict performance number and actual power value Error Absolute Value as evaluation index, determined with entropy assessment Weight coefficient wnr1、wnr2, wnr1+wnr2=1.
Calculate the non-radial-flow type small power station short term power predictive value P in prediction jth moment day areanrjFormula be：
Pnrj=wnr1×Pnr1j+wnr2×Pnr2j.
ξ a few days ago is predicted in step 3.5, calculatingnrIt history goes out the error ε of force data and prediction datanrIf, continuous ξnr Its error εnrIt is all higher than non-radial-flow type small power station short term power precision of prediction threshold value e for settingnr, then re-start step 3.4； It is less than or equal to and then exports non-radial-flow type small power station short term power predictive value Pnrj.
Step 4, according to regional radial-flow type small power station jth moment short term power predictive value PrjWith regional non-radial-flow type small power station Jth moment short term power predictive value Pnrj, obtain regional small power station's short term power predictive value Pj=Prj+Pnrj.

Claims (9)

1. a kind of small power station classifies short term power Forecasting Methodology, it is characterised in that：It includes
The data message of step 1, the regional radial-flow type small power station of collection and non-radial-flow type water power；
Step 2, by radial-flow type small power station, directly prediction and BP neural network prediction carry out radial-flow type small power station short-term work(respectively Rate is predicted, and asks for the weight coefficient of two methods predictive value using entropy assessment, calculates the regional radial-flow type small power station jth moment Short term power predictive value Prj；
Step 3, the regional non-radial-flow type small power station jth moment short term power predictive value P of calculatingnrj；
Step 4, according to regional radial-flow type small power station jth moment short term power predictive value PrjWith regional non-radial-flow type small power station jth Moment short term power predictive value Pnrj, obtain regional small power station's short term power predictive value Pj=Prj+Pnrj.
2. a kind of small power station according to claim 1 classifies short term power Forecasting Methodology, it is characterised in that：Described in step 1 Collection area radial-flow type small power station data message include meteorological data, go out force data, startup-shutdown data, the meteorological data should Temperature, air pressure including radial-flow type small power station region, humidity, wind direction, wind speed, rainfall product data；The non-radial-flow type water The data message of electricity includes meteorological data, goes out force data, the outbound water yield, startup-shutdown data, and meteorological data should include non-radial-flow type The temperature of water power region, air pressure, humidity, wind direction, wind speed, the medium-term and long-term generating of rainfall product data and non-radial-flow type small power station Plan and the time-of-use tariffs data of area power grid.
3. a kind of small power station according to claim 1 classifies short term power Forecasting Methodology, it is characterised in that：Described in step 2 Regional radial-flow type small power station jth moment short term power predictive value PrjComputational methods include：
Step 2.1, discharge Q in i-th radial-flow type small power station jth moment of calculatingrij=Qr1ij+Qr2ij, in formula：
Qr1ijRainfall for radial-flow type small power station region；Qr2ijThe outbound water yield for the non-radial-flow type water power in upstream；
Step 2.2, i-th radial-flow type small power station the first short term power of jth moment predictive value P of calculatingr1ij, Pr1ij=ki·Qrij· HiIn formula：Pr1ijFor i-th radial-flow type small power station the first short term power of jth moment predictive value Pr1ij, unit is W；QrijFor i-th The discharge in radial-flow type small power station jth moment, unit is m3/s；HiFor the average water head of i-th radial-flow type small power station, unit is m；kiPower factor for i-th radial-flow type small power station；
Calculate the first short term power predictive value P in regional radial-flow type small power station jth momentr1j；
In formula：N is the total quantity of regional radial-flow type small power station；
Step 2.3, according to i-th radial-flow type small power station meteorological data, go out force data, carry out BP neural network prediction, obtain ground The second short term power predictive value P in radial-flow type small power station of area jth momentr2j；
Step 2.4, determine Pr1jAnd Pr2jWeight coefficient wr1、wr2, and calculate prediction jth moment day, regional radial-flow type small power station Short term power predictive value Prj,
Step 2.5, calculating predict that the history of ξ r days a few days ago goes out the error ε of force data and prediction datarIf, continuous ξ r days error εrRadial-flow type small power station short term power precision of prediction threshold value er for setting is all higher than, re-starts step 2.5；Pre- less than then exporting Survey jth moment day area radial-flow type small power station short term power predictive value Prj.
4. a kind of small power station according to claim 3 classifies short term power Forecasting Methodology, it is characterised in that：
The method of the second short term power predictive value Pr2j in the regional radial-flow type small power station jth moment described in step 2.3 includes：
Step 2.3.1, three layers of BP neural network are used, the input vector of given BP neural network power prediction model simultaneously returned One change is processed；The input layer number of the BP neural network power prediction model is 7, according to radial-flow type small power station location Temperature Tr in the meteorological data in domain, air pressure Pr, humidity Hr, wind direction Wdr, wind speed Wsr, rainfall Qr1, the non-radial-flow type water in upstream Electric outbound water yield Qr2, forms the input vector Y of BP neural network power prediction modelr=[Tr, Pr, Hr, Wdr, Wsr, Qr1, Qr2]；Described its formula is normalized to input vector Yr it is
Y' in formularijFor jth moment, i-th radial-flow type small power station normalized Input vector afterwards；YrijFor jth moment, the input vector before i-th radial-flow type small power station normalized；max(Yri) and min(Yri) represent maximum and the minima of i-th radial-flow type small power station input vector respectively；
Step 2.3.2, selection prediction δ r day data a few days ago are trained to BP neural network, obtain instruction as training sample BP neural network power prediction model after white silk；
Step 2.3.3, will prediction jth moment day, temperature Tr of i-th radial-flow type small power station regionij, air pressure Prij, humidity Hrij, wind direction Wdrij, wind speed Wsrij, rainfall Qr1ijOutbound water yield Q with the non-radial-flow type water power in upstreamr2ijAs input layer, defeated In BP neural network power prediction model after entering to training, output layer data is prediction jth moment day, and i-th radial-flow type is little Water power the second short term power predictive value pr2ij；
Step 2.3.4, the second short term power predictive value P in calculating regional radial-flow type small power station jth momentr2j
P in formular2ijFor predicting jth moment day, i-th the second short term power of radial-flow type small power station Predictive value, n is the total quantity of regional radial-flow type small power station.
5. a kind of small power station according to claim 3 classifies short term power Forecasting Methodology, it is characterised in that：Step 2.4 and Regional radial-flow type small power station short term power predictive value P described in step 2.5rjComputational methods include
Step 2.4.1 is according to prediction θ a few days agorIt history meteorological data and history go out force data, calculate the first short term power respectively Predictive value Pr1(1)j...Pr1(θr)jWith the second short term power predictive value Pr2(1)j...Pr2j(θr)j, and count actual power value and be Pr1...Prθr；
Step 2.4.2, respectively with the first short term power predictive value and the error of the second short term power predictive value and actual power value Absolute value is evaluation index, determines weight coefficient w with entropy assessmentr1And wr2；wr1For Pr1jWeight coefficient, wr2For Pr2jWeight Coefficient；wr1+wr2=1；
Step 2.4.3, pass through formula Prj=wr1×Pr1j+wr2×Pr2jPrediction jth moment day is calculated, the little water of regional radial-flow type Electric short term power predictive value Prj；
Step 2.4.4, calculating predict that the history of ξ r days a few days ago goes out the error ε r of force data and prediction data, if continuous ξ r days is by mistake Difference ε r is all higher than radial-flow type small power station short term power precision of prediction threshold value er for setting, and re-starts step 2.4.3；Less than then defeated Go out to predict jth moment day, regional radial-flow type small power station short term power predictive value Prj.
6. a kind of small power station according to claim 1 classifies short term power Forecasting Methodology, it is characterised in that：Described in step 3 The non-radial-flow type small power station jth moment short term power predictive value P in calculating areanrjMethod comprise the steps：
Step 3.1, the historical data according to k-th non-radial-flow type small power station of jth moment and meteorological data calculate the jth moment, K-th non-the first short term power of radial-flow type small power station predictive value Pnr1kj；Pnr1kj=kk·Qnrkj·HkjIn formula：
kkFor the power factor of k-th non-radial-flow type small power station, HkjHead for k-th non-radial-flow type small power station of jth moment；
QnrkjDischarge for k-th non-radial-flow type small power station of jth moment；
Step 3.2, jth moment the first short term power predictive value P of the regional non-radial-flow type small power station of calculatingnr1j；,
In formula：M is the quantity of regional non-radial-flow type small power station；
Step 3.3, in the jth moment, historical data according to the non-radial-flow type small power station of k-th radial-flow type, meteorological data and area The time-of-use tariffs of electrical network, are calculated the second short term power predictive value of regional non-radial-flow type small power station using support vector machine Pnr2j；
Step 3.4, determine Pnr1jAnd Pnr2jWeight coefficient wnr1、wnr2, calculate the little water of the non-radial-flow type in prediction jth moment day area Electric short term power predictive value Pnrj；
ξ a few days ago is predicted in step 3.5, calculatingnrIt history goes out the error ε of force data and prediction datanrIf, continuous ξnrIt is by mistake Difference εnrIt is all higher than non-radial-flow type small power station short term power precision of prediction threshold value e for settingnr, then re-start step 3.4；It is less than Or be equal to then export non-radial-flow type small power station short term power predictive value Pnrj.
7. a kind of small power station according to claim 6 classifies short term power Forecasting Methodology, it is characterised in that：Step 3.3 institute Second short term power predictive value P of the non-radial-flow type small power station in the area statednr2jComputational methods, its step includes：
Step 3.3.1, the input vector of given support vector machine power prediction model are simultaneously normalized；According to non-runoff The meteorological data of formula small power station region：Temperature Tnr, air pressure Pnr, humidity Hnr, wind direction Wdnr, wind speed WnrsWith rainfall Qnr1、 Time-of-use tariffs Cnr of area power grid, form the input vector Y of support vector machine power prediction modelnr:
Ynr=[Tnr、Pnr、Hnr、Wdnr、Wsnr、Qnr1、Cnr]；
Input vector Ynr is normalized, and its formula is：
In formula：Y'nrkjFor jth moment, k-th non-radial-flow type small power station normalized Input vector afterwards；YnrkjFor jth moment, the input vector before k-th non-radial-flow type small power station normalized；max(Ynrk) With min (Ynrk) represent the maximum of k-th non-radial-flow type small power station variable and minima respectively；
δ a few days ago is predicted in step 3.3.2, selectionnrIts YnrkjData are trained using support vector machine as training sample, are obtained Support vector machine power prediction model after must training；
Step 3.3.3, will prediction jth moment day, temperature T of k-th non-radial-flow type small power station regionnrkj, air pressure Pnrkj、 Humidity Hnrkj, wind direction Wdnrkj, wind speed Wsnrkj, rainfall Qnr1kjTime-of-use tariffs C with area power gridnrkjAs input layer, it is input into In support vector machine power prediction model to after training, output layer data is prediction jth moment day, and k-th non-radial-flow type is little Water power short term power predictive value pnr2kj；
Step 3.3.4, regional non-radial-flow type small power station jth moment the second short term power predictive value P of calculatingnr2j, its formula is：
P n r 2 j = Σ k = 1 m P n r 2 k j ;
8. a kind of small power station according to claim 6 classifies short term power Forecasting Methodology, it is characterised in that：Step 3.4 institute The P for statingnr1jAnd Pnr2jWeight coefficient wnr1、wnr2Determination method include：
θ a few days ago is predicted in step 3.4.1, acquisitionnrIt history meteorological data and history go out force data；
Step 3.4.2, calculate respectively regional non-radial-flow type small power station first prediction performance number Pnr1(1)j...Pnr1(θnr)jWith second Power prediction value Pnr2(1)j...Pnr2j(θnr)j；
Step 3.4.3, acquisition actual power value are Pnr1...Pnrθnr；
Step 3.4.4, with predict performance number and actual power value Error Absolute Value as evaluation index, determine weight with entropy assessment Coefficient wnr1、wnr2, wnr1+wnr2=1.
9. non-radial-flow type small power station short term power weight predicting method according to claim 6, it is characterised in that：Calculate pre- Survey the non-radial-flow type small power station short term power predictive value P in jth moment day areanrjFormula be：
Pnrj=wnr1×Pnr1j+wnr2×Pnr2j.
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