CN103077297A - Short-time-interval atmosphere ambient temperature prediction method - Google Patents

Abstract

The invention discloses a short-time-interval atmosphere ambient temperature prediction method, which is characterized in that a spherical control body with the cross section of one square meter is defined in an atmospheric environment, parameters of the control body such as sun radiation energy and atmospheric temperature are measured every one set time step length, when the sampling recording duration is greater than 12 hours, an atmospheric ambient temperature prediction program is begun to actuate, and the atmospheric ambient temperature prediction program is actuated for one time every one prediction time interval; during the actuation of the atmospheric ambient temperature prediction program, a Cth value is calculated and determined according to the parameters such as the atmospheric temperature obtained at present for a kth prediction time period, and an air radiation heat absorption coefficient value f is updated if k is more than or equal to 2; finally the distribution of the sun radiation energy within future prediction duration is predicted by utilizing a sun radiation energy prediction module, the temperature distribution within the prediction duration is obtained, and the program waits until a next prediction time; and the method is applicable to the situation of any place and any weather type, high in precision, small in calculation and capable of being actuated in real time online in real application.

Description

A kind of short time interval atmospheric temperature Forecasting Methodology

Technical field

The invention belongs to heating ventilation and air conditioner system energy saving technical field, be specifically related to a kind of short time interval atmospheric temperature Forecasting Methodology.

Background technology

Heating ventilation and air-conditioning system (HVAC) are one of main sources of building energy consumption, are one of major domains of present energy-saving building technology exploitation.An at full capacity operating mode is only considered in the operation control of most building HVAC system, and when changing for the run duration thermal load, in most cases the control parameter of system is not to operate under the optimal conditions.The HVAC energy efficiency management is by calculating thermal load in advance and predicting, and the controlling value of HVAC system operational parameters (such as refrigeration (heat) amount, flow, temperature etc.) is optimized, satisfying under the workload demand prerequisite, guarantee that the HVAC system operates in the least energy consumption state, thereby the control parameter of HVAC system is remained under the optimum condition of work in operational process always, reach energy-conservation purpose.Atmospheric temperature is one of key factor that affects the HVAC thermal load, so needs the outside atmosphere environment temperature is predicted in the HVAC energy efficiency management process.The optimization frequency of HVAC energy efficiency management is generally 10-15 minute, and high precision short time interval (sub-hourly) atmospheric temperature forecasting techniques is one of core technology of exploitation HVAC energy efficiency management.

The neural network model [5-10] that mainly contains early stage Parametric Analysis model [1-4] and commonly used in recent years about the atmospheric temperature Forecasting Methodology at present, but these distinct methods all are aimed at application-specific, prediction is existing significant limitation aspect precision and the applicability for the short time interval environment temperature, the Parametric Analysis model that proposes such as document [1-3] is aimed at the agricultural greenhouse application, be used for prediction near surface air themperature, analyze heat exchange relationship and impact between air and the surface soil.The factor that affects the atmospheric temperature variation has [4] such as solar radiant energy, local landform, atmospheric humidity, cloud cover situation and wind speed, the variation of these factors has the randomness feature, so the Parametric Analysis model is difficult to be applied to the temperature prediction of short time interval.And artificial neural network (ANN) is suitable for the feature identification to stochastic variable, classification and prediction, and be subject to broad research [5-10] aspect the atmospheric temperature prediction, but the ANN method has certain limitation for the short time interval Forecasting of Atmospheric Environment, the ANN model is set up the training that is based on historical data, in forecasting process, need such as humidity, the weather conditions such as wind speed and solar radiant energy are as input parameter [7], the ANN forecast model of setting up is relevant with the locality, do not possess the versatility characteristics, and the training time of setting up the forecast model needs is long, need a large amount of historical datas, differ and obtain surely effective ANN forecast model in the place frequently for climate change.

List of references:

[1]J.W.Deardorff，Efficient?prediction?of?ground?surface?temperatureand?moisture，with?inclusion?of?a?layer?of?vegetation，Journal?of?GeophysicalResearch，83(1978)：1889-1903.

[2]C.M.Bhumralkar，Numerical?experiments?on?the?computation?ofground?surface?temperature?in?an?atmospheric?general?circulation?model，J.Appl.Meteorol.，14(1975)：1246-1258.

[3]A.K.Blackada，Modeling?the?nocturnal?boundary?layer，Proceedingsof?the?Third?Symposium?on?Atmospheric?Turbulence，Diffusion?and?AirQuality,pp.46-49，American?Meteorological?Society，Boston，Mass.，1976.

[4]H.Swaid，M.E.Hoffman，Prediction?of?urban?air?temperaturevariations?using?the?analytical?CTTC?model，Energy?and?Building，14(1990)：313-224.

[5]L.Bodri，V.Cermak，Prediction?of?surface?air?temperatures?by?neuralnetwork，example?based?on?three-year?temperature?monitoring?at?Sporilovstation，Stud.Geophys.Geod.，47(2003)：173-184.

[6]A.Jain，RW.McClendon，G.Hoogenboom，Freeze?prediction?forspecific?locations?using?artificial?neural?networks，Transactions?of?the?ASABE，49(6)：1955-1962.

[7]B.A.Smith，R.W.McClendon，G.Hoogenboom，Improving?airtemperature?prediction?with?artificial?neural?networks，Int.J.ComputationalIntelligence，3(2006)：179-186.

[8]R.F.Chevalier，G.Hoogenboom，R.W.McClendon，J.A.Paz，Supportvector?regression?with?reduced?training?sets?for?air?temperature?prediction：acomparison?with?artificial?neural?networks，Neural?Comput.&Applic.，20(2011)：151-159.

[9]A.L.Labajo，J.L.Labajo，Analysis?of?temporal?behavior?of?climatevariables?using?artificial?neural?networks：an?application?to?mean?monthlymaximum?temperatures?on?the?Spanish?Central?Plateau，Atmosfera，24(2011)：267-285.

[10]M.Afzali，A.Afzali，G.Zahedi，The?potential?of?artificial?neuralnetwork?technique?in?daily?and?monthly?ambient?air?temperature?prediction，Int.J.Environmental?Science?and?Development，3(2012)：33-38.

[11]G.A.F.Seber，C.J.Wild，Nonlinear?Regression，John?Wiley＆Sons，Inc.，1989，p.254

Summary of the invention

For solving above-mentioned problems of the prior art, the object of the present invention is to provide a kind of short time interval atmospheric temperature Forecasting Methodology, the inventive method is applicable to the situation of any place and any weather pattern, and precision is high, calculated amount is little, can carry out in real time online in the practical application.

The design philosophy of the inventive method is:

At first, in atmospheric environment sectional area of definition be 1 square metre spherical control volume as research object, the variation of this control volume temperature has namely represented the atmospheric temperature variation.The heat that the control volume temperature variation mainly depends on the air thermal capacitance and receives, the variation of its Air thermal capacitance is relevant with temperature with air humidity, exchange heat outside control volume and the control volume mainly realizes by radiation and convection current, consider that the predicted time interval is shorter, can suppose that control volume is identical with surrounding air temperature in prediction period, therefore can ignore the convection heat transfer in control volume and the external world.Radiation heat has a plurality of ingredients, comprises the direct radiation heat that comes from (1) sun; (2) cloud layer and ground return; (3) cloud layer and terrestrial radiation heat.Wherein (1) and (2) directly depends on local solar radiant energy, and (3) and cloud layer, surface temperature are relevant, with solar radiant energy indirect dependence are arranged, and it changes with solar radiant energy a lag-effect in time.Therefore the variation of atmospheric temperature can be summed up as only relevantly with local solar radiant energy, and key content of the present invention is to have proposed a kind of new method to determine dependence between atmospheric temperature and the solar radiant energy.

For achieving the above object, the technical solution adopted in the present invention is:

A kind of short time interval atmospheric temperature Forecasting Methodology comprises the steps:

Step 1: at first, in atmospheric environment sectional area of definition be 1 square metre spherical control volume as research object, the sampling time step-length of setup control body environmental variance is set the prediction duration;

Step 2: every the time step of a setting, measure solar radiant energy, atmospheric temperature, pressure and relative humidity, the time span that continues when sample record is during greater than 12 hours, then begin to carry out the atmospheric temperature predictor, and every atmospheric temperature prediction of time interval execution through a prediction duration;

Step 3: in atmospheric temperature prediction implementation, for k predicted time section, the atmospheric temperature, pressure and the relative humidity that at first obtain according to current sampling are by equation (1)～(5) calculative determination C _ThValue if k equals 1, then is that air radiation heat absorption coefficients f sets a value (such as 0.0025) in interval [0.001,0.01] scope, if predicted time segment index k more than or equal to 2, adopts equation (6) to upgrade the value of air radiation heat absorption coefficients f:

$\ln \left(p_s\right)=$

$54.842763-\frac{6763.22}{T}-4.21\ln \left(T\right)+0.000367T+\tanh \left[0.0415(T-218.8)\right][53.878-$

$\frac{1331.22}{T}-9.44523\ln \left(T\right)+0.014025T]---\left(2\right)$

$\mathrm{\ρ}=\frac{p(1-x)}{287.058T}+\frac{\mathrm{px}}{461.495T}---\left(3\right)$

$C_{\mathrm{th}}=\frac{3}{4\mathrm{\π\ρ}{c}_p{r}^3}---\left(5\right)$

$f=\frac{A(T_0^k-T_0^{k-1})}{C_{\mathrm{th}}^k[{\∫}_{t_0^k-12}^{t_0^k}{f}_{\mathrm{bl}}\left(t\right){\stackrel{\·}{q}}_{\mathrm{rad}}\left(t\right)\mathrm{dt}-{\∫}_{t_0^{k-1}-12}^{t_0^{k-1}}{f}_{\mathrm{bl}}\left(t\right){\stackrel{\·}{q}}_{\mathrm{rad}}\left(t\right)\mathrm{dt}]}---\left(6\right)$

In the formula:

---air absolute humidity,

X---relative air humidity,

P---atmospheric pressure,

p _s---water in air saturated with vapor pressure,

T---control volume temperature,

ρ---control volume atmospheric density,

c _p---control volume air specific heat,

R---control volume radius

F---the photothermal absorption coefficient of control volume,

T ₀---Current Temperatures,

t ₀---current time, unit: hour

f _Bi(t)---Box Lucas function, expression formula is: Parameter a in the formula ₁, a ₂Default value can be taken as 0.5 and 3.8, can do certain adjustment for these two parameter values of different regions.

A---Box Lucas function is at the integration of interval [0,12], and expression formula is: $A={\∫}_0^{12}{f}_{\mathrm{bl}}\left(t\right)\mathrm{dt}$

Interval [t ₀-12, t ₀] q _Rad---the solar radiant energy in past 12 hours;

Step 4: utilize solar radiant energy distribution in a prediction of the solar radiant energy prediction module predict future duration, thereby obtain Temperature Distribution in a prediction in the future duration according to equation (10), afterwards program wait is until next the prediction constantly is specific as follows:

The thermal balance equation of control volume is (7):

$\frac{4}{3}{\mathrm{\π\ρc}}_p{r}^3\frac{\mathrm{dT}}{\mathrm{dt}}=f\stackrel{\·}{q}---\left(7\right)$

Integration is carried out on these equation both sides gets equation (8):

（8）

In the formula: T is the control volume temperature, and ρ is atmospheric density, c _pBe air specific heat, r is the control volume radius, Be the built-up radiation hot strength, f is the photothermal absorption coefficient of control volume;

Compare with reflection with direct solar radiation, the heat radiation that cloud layer and earth surface itself produces has certain hysteresis quality in time, consider that the own heat radiation of cloud layer and earth surface is solar radiant energy lag-effect in time, the radiation heat in the equation (7)

Calculating can be regarded as the past period solar radiant energy

The accumulation of thermal effect, thermal effect duration time lag of establishing solar radiation is 12 hours, and feature time lag of solar radiant energy thermal effect follows Box Lucas model [11], and is defined as equation (9):

$f_{\mathrm{decay}}\left(t\right)=\frac{f_{\mathrm{bl}}\left(t\right)}{A}---\left(9\right)$

In the formula: $f_{\mathrm{bl}}\left(t\right)=\frac{a_1(e^{-a_{2}t}-e^{-a_{1}t})}{a_1-a_2},$ $A={\∫}_0^{12}{f}_{\mathrm{bl}}\left(t\right)\mathrm{dt}$

Obtain t by equation (5), (8) and (9) _pThe prediction expression formula (10) of moment atmospheric temperature.

$T_p=T_0+\frac{C_{\mathrm{th}}f}{A}[{\∫}_{t_p-12}^{t_p}{f}_{\mathrm{bl}}\left(t\right){\stackrel{\·}{q}}_{\mathrm{rad}}\left(t\right)\mathrm{dt}-{\∫}_{t_0-12}^{t_0}{f}_{\mathrm{bl}}\left(t\right){\stackrel{\·}{q}}_{\mathrm{rad}}\left(t\right)\mathrm{dt}]---\left(10\right)$

Described prediction duration was less than 1 hour.

Described sampling time step-length is 1～5 minute.

The inventive method is applicable to the situation of any place and any weather pattern, and precision is high, and calculated amount is little, can carry out in real time online in the practical application.In the situation that predict duration less than 1 hour, predicted value and actual observed value meet well, and wherein absolute error is less than 1K when the prediction duration is 30 minutes, and absolute error is less than 3K when the prediction duration is 1 hour.

Description of drawings

Fig. 1 is solar radiant energy thermal effect characteristic curve time lag based on Box Lucas model.

Fig. 2 is to the predicted value of the atmospheric temperature during-8 days on the 1st April in 2012 of Singapore and observed reading relatively.

Embodiment

Below in conjunction with the drawings and specific embodiments the present invention is described in further detail.

The present embodiment illustrates a kind of short time interval atmospheric temperature of the present invention Forecasting Methodology by the atmospheric temperature during-8 days on the 1st April in 2012 of Singapore is predicted, comprises the steps:

Step 1: at first, in atmospheric environment sectional area of definition be 1 square metre spherical control volume as research object, the sampling time step-length of setup control body environmental variance is 3 minutes;

Step 2: every the time step of a setting, measure solar radiant energy, atmospheric temperature, pressure and the relative humidity of control volume, the time span that continues when sample record is during greater than 12 hours, then begin to carry out the atmospheric temperature predictor, and every atmospheric temperature prediction of time interval execution through a prediction duration;

Step 3: in atmospheric temperature prediction implementation, for k predicted time section, the atmospheric temperature, pressure, relative humidity, atmospheric density and the air specific heat that at first obtain according to current sampling are by equation (1)～(5) calculative determination C _ThValue, when k equaled 1, establishing the f value was 0.0025, if predicted time segment index k more than or equal to 2, adopts equation (6) to upgrade the value of air radiation heat absorption coefficients f;

$\ln \left(p_s\right)=$

$54.842763-\frac{6763.22}{T}-4.21\ln \left(T\right)+0.000367T+\tanh \left[0.0415(T-218.8)\right][53.878-$

$\frac{1331.22}{T}-9.44523\ln \left(T\right)+0.014025T]---\left(2\right)$

$\mathrm{\ρ}=\frac{p(1-x)}{287.058T}+\frac{\mathrm{px}}{461.495T}---\left(3\right)$

$C_{\mathrm{th}}=\frac{3}{4\mathrm{\π\ρ}{c}_p{r}^3}---\left(5\right)$

$f=\frac{A(T_0^k-T_0^{k-1})}{C_{\mathrm{th}}^k[{\∫}_{t_0^k-12}^{t_0^k}{f}_{\mathrm{bl}}\left(t\right){\stackrel{\·}{q}}_{\mathrm{rad}}\left(t\right)\mathrm{dt}-{\∫}_{t_0^{k-1}-12}^{t_0^{k-1}}{f}_{\mathrm{bl}}\left(t\right){\stackrel{\·}{q}}_{\mathrm{rad}}\left(t\right)\mathrm{dt}]}---\left(6\right)$

In the formula:

---air absolute humidity,

X---relative air humidity,

P---atmospheric pressure,

p _s---water in air saturated with vapor pressure,

T---control volume temperature,

ρ---control volume atmospheric density,

c _p---control volume air specific heat,

R---control volume radius

F---the photothermal absorption coefficient of control volume,

T ₀---Current Temperatures,

t ₀---current time, unit: hour

f _Bi(t)---Box Lucas function, expression formula is:

Parameter a in the formula ₁, a ₂Default value can be taken as 0.5 and 3.8, can do certain adjustment for these two parameter values of different regions.

A---Box Lucas function is at the integration of interval [0,12], and expression formula is: $A={\∫}_0^{12}{f}_{\mathrm{bl}}\left(t\right)\mathrm{dt}$

Interval [t ₀-12, t ₀] q _Rad---the solar radiant energy in past 12 hours;

Step 4: utilize solar radiant energy distribution in a prediction of the solar radiant energy prediction module predict future duration, thereby obtain Temperature Distribution in a prediction in the future duration according to equation (10), afterwards program wait is until next the prediction constantly; Specific as follows:

The thermal balance equation of control volume is (7):

$\frac{4}{3}{\mathrm{\π\ρc}}_p{r}^3\frac{\mathrm{dT}}{\mathrm{dt}}=f\stackrel{\·}{q}---\left(7\right)$

Integration is carried out on these equation both sides gets equation (8):

$T={\∫C}_{\mathrm{th}}\left(t\right)f\stackrel{\·}{q}\left(t\right)\mathrm{dt}---\left(8\right)$

In the formula: T is the control volume temperature, and ρ is atmospheric density, c _pBe air specific heat, r is the control volume radius, Be the built-up radiation hot strength, f is the photothermal absorption coefficient of control volume;

Compare with reflection with direct solar radiation, the heat radiation that cloud layer and earth surface itself produces has certain hysteresis quality in time, consider that the own heat radiation of cloud layer and earth surface is solar radiant energy lag-effect in time, the radiation heat in the equation (7) Calculating can be regarded as the past period solar radiant energy

The accumulation of thermal effect, thermal effect duration time lag of establishing solar radiation is 12 hours, and feature time lag of solar radiant energy thermal effect follows Box Lucas model [11], and is defined as equation (9):

$f_{\mathrm{decay}}\left(t\right)=\frac{f_{\mathrm{bl}}\left(t\right)}{A}---\left(9\right)$

In the formula: $f_{\mathrm{bl}}\left(t\right)=\frac{a_1(e^{-a_{2}t}-e^{-a_{1}t})}{a_1-a_2},$ $A={\∫}_0^{12}{f}_{\mathrm{bl}}\left(t\right)\mathrm{dt}$

Obtain t by equation (5), (8) and (9) _pThe prediction expression formula (10) of moment atmospheric temperature.

$T_p=T_0+\frac{C_{\mathrm{th}}f}{A}[{\∫}_{t_p-12}^{t_p}{f}_{\mathrm{bl}}\left(t\right){\stackrel{\·}{q}}_{\mathrm{rad}}\left(t\right)\mathrm{dt}-{\∫}_{t_0-12}^{t_0}{f}_{\mathrm{bl}}\left(t\right){\stackrel{\·}{q}}_{\mathrm{rad}}\left(t\right)\mathrm{dt}]---\left(10\right)$

As shown in Figure 1, be solar radiant energy thermal effect characteristic curve time lag based on Box Lucas model, as can be seen from the figure, the solar radiant energy thermal effect reaches peak value about 1 hour, thermal effect reduces gradually afterwards, its thermal effect almost can be ignored close to 0 after 12 hours.

As shown in Figure 2, for adopting the inventive method to predicted value and observed reading comparison to the atmospheric temperature during-8 days on the 1st April in 2012 of Singapore, can find out, in the situation that the prediction duration was less than 1 hour, predicted value and actual observed value meet well, wherein absolute error is less than 1K when the prediction duration is 30 minutes, and absolute error is less than 3K when the prediction duration is 1 hour.

Claims (3)

1. a short time interval atmospheric temperature Forecasting Methodology is characterized in that: comprise the steps:

Step 1: at first, in atmospheric environment sectional area of definition be 1 square metre spherical control volume as research object, the sampling time step-length of setup control body environmental variance is set the prediction duration;

Step 2: every the time step of a setting, measure solar radiant energy, atmospheric temperature, pressure and relative humidity, the time span that continues when sample record is during greater than 12 hours, then begin to carry out the atmospheric temperature predictor, and every atmospheric temperature prediction of time interval execution through a prediction duration;

Step 3: in atmospheric temperature prediction implementation, for k predicted time section, the atmospheric temperature, pressure and the relative humidity that at first obtain according to current sampling are by equation (1)～(5) calculative determination C _ThValue if k equals 1, [is that air radiation heat absorption coefficients f sets a value (such as 0.0025) in 0.001,0.01 scope, if predicted time segment index k more than or equal to 2, adopts equation (6) to upgrade the value of air radiation heat absorption coefficients f then in the interval;

$\ln \left(p_s\right)=$

$54.842763-\frac{6763.22}{T}-4.21\ln \left(T\right)+0.000367T+\tanh \left[0.0415(T-218.8)\right][53.878-$

$\frac{1331.22}{T}-9.44523\ln \left(T\right)+0.014025T]---\left(2\right)$

$\mathrm{\ρ}=\frac{p(1-x)}{287.058T}+\frac{\mathrm{px}}{461.495T}---\left(3\right)$

$C_{\mathrm{th}}=\frac{3}{4\mathrm{\π\ρ}{c}_p{r}^3}---\left(5\right)$

$f=\frac{A(T_0^k-T_0^{k-1})}{C_{\mathrm{th}}^k[{\∫}_{t_0^k-12}^{t_0^k}{f}_{\mathrm{bl}}\left(t\right){\stackrel{\·}{q}}_{\mathrm{rad}}\left(t\right)\mathrm{dt}-{\∫}_{t_0^{k-1}-12}^{t_0^{k-1}}{f}_{\mathrm{bl}}\left(t\right){\stackrel{\·}{q}}_{\mathrm{rad}}\left(t\right)\mathrm{dt}]}---\left(6\right)$

In the formula:

---air absolute humidity,

X---relative air humidity,

P---atmospheric pressure,

p _s---water in air saturated with vapor pressure,

T---control volume temperature,

ρ---control volume atmospheric density,

c _p---control volume air specific heat,

R---control volume radius

F---the photothermal absorption coefficient of control volume,

T ₀---Current Temperatures,

t ₀---current time, unit: hour

f _Bi(t)---Box Lucas function, expression formula is:

Parameter a in the formula ₁, a ₂Default value can be taken as 0.5 and 3.8, can do certain adjustment for these two parameter values of different regions;

A---Box Lucas function is at the integration of interval [0,12], and expression formula is: $A={\∫}_0^{12}{f}_{\mathrm{bl}}\left(t\right)\mathrm{dt};$

Interval [t ₀-12, t ₀] q _Rad---the solar radiant energy in past 12 hours;

Step 4: utilize solar radiant energy distribution in a prediction of the solar radiant energy prediction module predict future duration, thereby obtain Temperature Distribution in a prediction in the future duration according to equation (10), afterwards program wait is until next the prediction constantly; Specific as follows:

The thermal balance equation of control volume is (7):

$\frac{4}{3}{\mathrm{\π\ρc}}_p{r}^3\frac{\mathrm{dT}}{\mathrm{dt}}=f\stackrel{\·}{q}---\left(7\right)$

Integration is carried out on these equation both sides gets equation (8):

$T={\∫C}_{\mathrm{th}}\left(t\right)f\stackrel{\·}{q}\left(t\right)\mathrm{dt}---\left(8\right)$

In the formula: T is the control volume temperature, and ρ is atmospheric density, c _pBe air specific heat, r is the control volume radius,

Be the built-up radiation hot strength, f is the photothermal absorption coefficient of control volume;

Compare with reflection with direct solar radiation, the heat radiation that cloud layer and earth surface itself produces has certain hysteresis quality in time, consider that the own heat radiation of cloud layer and earth surface is solar radiant energy lag-effect in time, the radiation heat in the equation (7)

Calculating can be regarded as the past period solar radiant energy

The accumulation of thermal effect, thermal effect duration time lag of establishing solar radiation is 12 hours, and feature time lag of solar radiant energy thermal effect follows Box Lucas model [11], and is defined as equation (9):

$f_{\mathrm{decay}}\left(t\right)=\frac{f_{\mathrm{bl}}\left(t\right)}{A}---\left(9\right)$

In the formula: $f_{\mathrm{bl}}\left(t\right)=\frac{a_1(e^{-a_{2}t}-e^{-a_{1}t})}{a_1-a_2},$ $A={\∫}_0^{12}{f}_{\mathrm{bl}}\left(t\right)\mathrm{dt}$

Obtain t by equation (5), (8) and (9) _pThe prediction expression formula (10) of moment atmospheric temperature.

$T_p=T_0+\frac{C_{\mathrm{th}}f}{A}[{\∫}_{t_p-12}^{t_p}{f}_{\mathrm{bl}}\left(t\right){\stackrel{\·}{q}}_{\mathrm{rad}}\left(t\right)\mathrm{dt}-{\∫}_{t_0-12}^{t_0}{f}_{\mathrm{bl}}\left(t\right){\stackrel{\·}{q}}_{\mathrm{rad}}\left(t\right)\mathrm{dt}]---\left(10\right)$

2. Forecasting Methodology according to claim 1, it is characterized in that: described prediction duration was less than 1 hour.

3. Forecasting Methodology according to claim 1, it is characterized in that: described sampling time step-length is 1～5 minute.