CN103425889B - Living area, a kind of power station electrical energy consumption analysis method - Google Patents

Abstract

<b>The present invention relates to living area, a kind of power station electrical energy consumption analysis method, seek to estimate the power consumption in living area from generated energy, temperature, rainfall and other potential four aspects of influence factor. First consider the impact of every influence factor on power consumption. By analyzing, the historical data of each influence factor changes and the variation of power consumption, determines the relation of each factor and generated energy; And then set up living area electric model; Then utilize improved least square method to model solution coefficient matrix and constant term in different temperature ranges; Utilize the model of having set up to calculate living area power consumption. Not the invention solves not in living area the problem that installation table meter separately just cannot obtain living area power consumption.</b>

Description

Living area, a kind of power station electrical energy consumption analysis method

Technical field

The present invention relates to living area, power station electrical energy consumption analysis method.

Background technology

Living area, power station is mainly used in power station staff live lodging, amusement and recreation. To waterThe power supply in living area, power station is mainly used in the purposes such as apartment, dining room, living area outdoor lighting. OneAs the living area electricity consumption in power station accounted for 18% to 27% of direct station-service electric weight. At present, send outPower station power saving more and more receives people's concern, uses as the large electricity consumer's of plant area living areaElectricity has huge potentiality aspect energy-conservation. But often running into this aspect calculating living area electricity consumptionThe problem of sample: be first some morning construction of hydropower stations times, table is installed separately in living areaMeter metering living area electricity consumption, but counted in direct station service. Due to electric power station systemMoulding, is difficult in original system more extra increase table meter and measures separately living area electricity consumption,Consider from economic benefit, also do not advocate and install separately table meter additional simultaneously; Secondly because living area is usedElectricity equipment is mainly to carry out start and stop according to people's demand, so that the electricity consumption in living area has is very largeRandomness, does not have strict specification, Supervision and examination system, makes people's awareness of saving energy notOften cause very large electricity consumption waste by force. In order to understand living area power consumption situation, necessaryFrom direct station service record, separate living area electricity consumption. Use in order effectively to strengthen living areaElectricity administration of energy conservation, must set up a set of living area electricity consumption performance assessment criteria.

Conventional living area electricity consumption computational methods are least square methods, analyze all living areas that affectThe influence degree of electricity consumption factor to living area electricity consumption. But find by analyzing a large amount of historical datas,Traditional least square method is calculated and is had asking of following two aspects the power consumption in living areaTopic:

1) living area electricity consumption be mainly subject to generally three of generated energy, temperature, precipitation dominant because ofThe impact of element, but in real life, various potential impact factors as turn(a)round flow of personnel,The recessive factor such as wind direction also can the electricity consumption of deep effect living area, and these recessive factors cannot be withData are weighed;

2) power consumption in living area and temperature are not single dependency relation, higher than a certainIn the scope of temperature, they are positive correlation, and are negative correlation in the scope lower than a certain temperature.

The examination of living area electricity consumption is not at present also having systematic research and a large amount of practical application,Current living area electricity consumption examination is often just limited to power station and concentrates inspection, and inspection personnel also oftenOnly understand the ruuning situation of living area consumer, and ignored the energy-saving potential in living area.

Summary of the invention

The object of the invention is to propose living area, a kind of power station electrical energy consumption analysis method, it can be separatedCertainly not in living area, installation table meter just cannot obtain the problem of living area power consumption separately.

In order to achieve the above object, the technical solution adopted in the present invention is as follows:

Living area, a kind of power station electrical energy consumption analysis method, from generated energy, temperature, rainfall and otherThe power consumption in living area is sought to estimate in potential four aspects of influence factor. First considerThe impact of every influence factor on power consumption. Change by the historical data of analyzing each influence factorWith the variation of power consumption, determine the relation of each factor and generated energy; And then set up living area and useElectric model; Then utilize improved least square method to the model solution in different temperature rangesCoefficient matrix and constant term; Utilize the model of having set up to calculate living area power consumption.

The present invention adopts following algorithm structure to realize above-mentioned purpose:

1) find optimum relevant turning point by the difference of coefficient correlation;

2) utilize least square method to obtain each relevant journey that affects factor and living area power consumptionDegree;

3) by the constant term in PSO Algorithm system of linear equations.

The present invention compared with prior art has the following advantages:

1) the non-single dependency relation of clear and definite living area power consumption and temperature, with front and back phaseClosing coefficient difference is index, determines optimum relevant turning point;

2), by introducing constant term, representing potential affects factor to living area power consumptionImpact, make calculate more accurate;

3) propose the improvement least square method in conjunction with particle cluster algorithm formation, passed through particleCycle calculations, constantly revise particle position, seeking fitting expression to be Ax+c=bCurve.

4) power consumption this performance assessment criteria of proportion in direct station service in introducing living area is oldExamination living area electricity consumption, Energy-saving Situation. Strengthen the living area management of power use with crossing Check,Effectively realize living area energy-saving electricity and reduce discharging target.

Brief description of the drawings

Fig. 1 is the flow process of living area, the power station electrical energy consumption analysis method of preferred embodiment of the present inventionFigure.

Detailed description of the invention

Below, with detailed description of the invention, the present invention is described further.

As shown in Figure 1, living area, a kind of power station electrical energy consumption analysis method, it comprises the following steps:

Step 1: obtain the historical measured data in living area, historical data and current data. To thisA little data carry out obtaining statistics after pretreatment. The historical measured data bag in wherein said living areaDraw together voltage, electric current and power factor (PF), the historical measured data in living area is by putting HIOKI3196 electricity dayCan quality/Electric Energy Analytic Instrument measure, due to put day the HIOKI3196 quality of power supply/Electric Energy Analytic Instrument is professional measurement device, costly, can not all install for a long timeIn power station. By historical living area measured data by calculating historical living area power consumption.In the database of described historical data by power station side, derive, comprise historical rainfall, temperature,Generated energy and directly station-service electric weight. Current data comprise current rainfall, temperature, generated energy andDirectly station-service electric weight, wherein rainfall, temperature record are provided by power station side's monitoring record, generatingAmount and directly station-service electric weight are from the generating daily record of power station side.

Step 2: determine the Optimal Temperature turning point of being correlated with.

Recognize living area power consumption by analyzing the historical measured data in living area and historical dataY and the linear dependency relation of temperature T emp. If there is some temperature spot Temp=T, whenWhen Temp > T, along with the rising of temperature, power consumption Y increases, living area power consumption Y and temperatureDegree Temp becomes positive correlation, has corr[Y (Temp > T)] > 0; In the time of Temp < T, along with temperatureDecline, living area power consumption Y increase, living area power consumption Y becomes negative with temperature T empPass relation, has corr[Y (Temp > T)] < 0, this temperature spot Temp=T is its relevant turning point.

(1) determine relevant turning point territory.

Produce the curve map that historical living area power consumption and historical temperature form, find out curveIn figure, the curve of historical living area power consumption is corresponding with the curve intersection part institute of historical temperatureTemperature range, determine relevant turning point territory (T1, T2) by the concept in relevant turning point territory.

(2) determine optimum relevant turning point.

In relevant turning point territory, all have corr[P (Temp > is k)] > 0,Corr[P (Temp > is k)] < 0. Only at certain some place, the coefficient correlation amplitude of variation maximum before and after it,Also will there is maximum variation in corresponding living area power consumption and the correlation of temperature. This enforcementExample utilizes following formula to determine optimum relevant turning point:

First, on average get a m in relevant turning point territory, the temperature that each point is corresponding is:

$T_i=T1+\frac{T2-T1}{m}*i,(i=1,\mathrm{2...}m)---\left(1\right)$

Utilize least square method, obtain respectively front territory phase relation corr[Y (the Temp > T of m point_i)]With converse domain coefficient correlation corr[Y (Temp < T_i)]. Then before and after asking, territory coefficient correlation is poor, finds out a littleMeet following condition:

max(corr[Y(Temp＞T_i)]-corr[Y(Temp＜T_i)])(2)This point is optimum relevant turning point.

Step 3: set up living area electric model.

From living area electricity consumption Analysis of key influential factors, living area power consumption y_{Living area power consumption}WithTime be subject to environment minimum temperature x₁, rainfall x₂, generated energy x₃Impact. Its mathematic(al) representation form asShown in following formula:

y_{Living area power consumption}＝f(x₁,x₂,x₃)(3)

Consider the impact of various potential impact factors on power consumption, in expression formula, introduce constantItem c, expression formula becomes following form:

y_{Living area power consumption}＝f(x₁,x₂,x₃,c_i)(4)

Suppose in step 2, the temperature of trying to achieve optimum turning point is T_p, its living area electricity consumptionAmount expression formula is as follows:

Step 4: use and improve least square method and solve living area power consumption and each influence factorRelation.

As temperature x₁＜T_pTime, taking week as unit calculate living area electricity consumption, model solution process asUnder:

4.1: initialize: generate at random the particle of some in the septuple space, and given grainInitial position, speed and the historical optimal location of son. In population, i particle is threeVector is respectively:

Current position: c_i＝(c_i1,c_i2,c_i3,c_i4,c_i5,c_i6,c_i7)(6)

Historical optimal location: p_i＝(p_i1,p_i2,p_i3,p_i4,p_i5,p_i6,p_i7)(7)

Speed: v_i＝(v_i1,v_i2,v_i3,v_i4,v_i5,v_i6,v_i7)(8)

In addition, the desired positions searching up to now in whole population is labeled as:

p_g＝(p_g1,p_g2,p_g3,p_g4,p_g5,p_g6,p_g7)(9)

4.2: set up coefficient matrix. To each particle, according to its locus, set up as followsEquation group:

θ₀c_i1+θ₁x₁₁₁+θ₂x₁₁₂+θ₃x₁₁₃＝y₁₁

θ₀c_i2+θ₁x₁₂₁+θ₂x₁₂₂+θ₃x₁₂₃＝y₁₂

θ₀c_i7+θ₁x₁₇₁+θ₂x₁₇₂+θ₃x₁₇₃＝y₁₇

θ₀c_i1+θ₁x₂₁₁+θ₂x₂₁₂+θ₃x₂₁₃＝y₂₁(10)

θ₀c_i7+θ₁x₂₇₁+θ₂x₂₇₂+θ₃x₂₇₃＝y₂₇

θ₀c_i1+θ₁x₃₁₁+θ₂x₃₁₂+θ₃x₃₁₃＝y₃₁

θ₀c_i7+θ₁x_n71+θ₂x_n72+θ₃x_n73＝y_n7

X in formula_1ijMeet x_1ij＜T_p，θ₀For the influence factor of latency to living area power consumptionCoefficient, θ₁For the influence factor coefficient of temperature to living area power consumption, θ₂For rainfall is to lifeThe influence factor coefficient of district's power consumption, θ₃For the influence factor system of generated energy to living area power consumptionNumber.

4.3: first, utilize least square method to solve coefficient matrix. Equation in formula (10)Group, for overdetermined equation, can be tried to achieve coefficient matrix θ least-squares estimation in above formula by least square methodFor:

θ'＝(Χ^TΧ)^-1Χ^TY(11)

Wherein $Y=\left(\begin{array}{c}{y}_{11}\\ y_{12}\\ .\\ .\\ .\\ y_{n7}\end{array}\right),X=\left(\begin{array}{cccc}{c}_{i1}& x_{111}& x_{112}& x_{113}\\ c_{i2}& x_{121}& x_{122}& x_{123}\\ .& & & .\\ .& & & .\\ .& & & .\\ c_{i7}& x_{n71}& x_{n72}& x_{n73}\end{array}\right),\mathrm{\&theta;}=\left(\begin{array}{c}{\mathrm{\&theta;}}_0\\ {\mathrm{\&theta;}}_1\\ {\mathrm{\&theta;}}_2\\ {\mathrm{\&theta;}}_3\end{array}\right)$

Then, evaluate the fitness of i particle, calculated by following formula:

${\mathrm{\&Phi;}}_i=\underset{k=1}{\overset{n}{\&Sigma;}}\underset{j=1}{\overset{7}{\&Sigma;}}{(y_{kj}-{\mathrm{\&theta;}}_0{c}_{ij}-{\mathrm{\&theta;}}_1{x}_{kj1}-{\mathrm{\&theta;}}_2{x}_{kj2}-{\mathrm{\&theta;}}_3{x}_{kj3})}^2---\left(12\right)$

4.4: upgrade optimal location: first, to i particle, relatively particle fitness Φ_iWithThe fitness Φ of its individual optimal value_pi, individual initial optimal value fitness is by given at firstHistorical optimal location determine, be calculated as follows:

${\mathrm{\&Phi;}}_{p_i}=\underset{k=1}{\overset{n}{\&Sigma;}}\underset{j=1}{\overset{7}{\&Sigma;}}{(y_{kj}-{\mathrm{\&theta;}}_0{p}_{ij}-{\mathrm{\&theta;}}_1{x}_{kj1}-{\mathrm{\&theta;}}_2{x}_{kj2}-{\mathrm{\&theta;}}_3{x}_{kj3})}^2---\left(13\right)$

If Φ_i＜Φ_pi, the historical optimal location using current point as this particle, otherwise,Historical optimal location remains unchanged. Then more overall particle fitness, if Φ_pi＜Φ_pg，Use p_iReplace current p_g。

4.5: more new particle: for each particle, its d dimension (1≤d≤7) according to asLower equation changes:

v_id＝v_id+c₁×rand(1)×(p_id-c_id)+c₂×rand(1)×(p_gd-c_id)(14)

c_id＝c_id+v_id(15)

Wherein v_idRepresent the value of i the d dimension in particle rapidity vector, p_idRepresent theThe value of the d dimension in i the historical optimal location vector of particle, c_idRepresent that i particle is currentThe value of the d dimension in position vector, aceleration pulse c1 and c2 are two nonnegative values, these twoAceleration pulse makes particle have that oneself sums up and to the ability of excellent individual study in colony, therebyTo oneself historical optimum point and colony in or global optimum's point in codomain close. C1Be generally equal to 2 with c2. Rand (1) is the random number in the interior value of scope [0,1]. UserConventionally can set a Vmax value, be used for the maximum of maximum speed limit. The speed of particle is limitIn a scope [Vmax, Vmax],, after more new formula is carried out, whether judge speedIn limited field, if in limited field, carry out formula 15, if transfinited,Upgrade position according to maximum constraints speed.

4.6: end condition: in the time that population converges to the neighborhood of certain diameter, and under meetingWhen row relational expression, finish to calculate, otherwise rebound step 2 repetitive cycling is until meet final conditionTill.

$max\left(\underset{k=1}{\overset{7}{\&Sigma;}}{\left(c_{ik}-c_{jk}\right)}^2\right)\&le;\mathrm{\&epsiv;},(i=1,2,\mathrm{3...})(j=1,2,\mathrm{3...})---\left(16\right)$

C in formula_ik，c_jkFor required particle position, ε is little positive number given in advance;

As temperature x₁＞T_pTime, repeating step 4.1-4.6, wherein in step 4.2, x_1ijExpireFoot x_1ij＞T_p。

Comprehensive step 1,2,3, can obtain complete living area electric model. Use this mouldType, can calculate living area power consumption according to the generated energy in power station, temperature and rainfall.

Step 5: according to current temperature, rainfall and generated energy data, utilize in step 3 and buildVertical model, can calculate the current living area power consumption in power station, reaches living area is usedThe object of electric quantity metering.

The present embodiment can also obtain historical temperature, rainfall, generated energy and direct station serviceAfter amount data, data are carried out to pretreatment and obtain statistics, utilize these data, according to stepIn rapid 4 various quarters of having set up, the living area power consumption in each week is calculated in living area with electric model. SoThe proportion of rear calculating living area power consumption in direct station service, by data scrubbing technology,To living area power consumption in the various quarters and direct station service ratio range (a%, b%).

The application of the present embodiment is as follows: utilize the direct station-service reporting in the certain hour section of power stationElectric weight, the living area power consumption of integrating step 5 gained, the living area of calculating in this time period is usedElectric weight is with direct station service than c%, and ratio is examined living area electricity consumption situation thus. If c < aIllustrate that real life district power consumption is greater than calculating power consumption, living area power consumption is relatively wasted,Need to strengthen energy-saving and emission-reduction work; If c > a illustrates that the electricity consumption of real life district is in normal range (NR),And c is larger, show that energy conservation does better; If c > b, may there be two kinds of situation one:There is new energy-saving potential point, living area electricity consumption was reduced more to some extent; Its two existence is usedElectricity equipment, makes some equipment fail normally to move or underrun, thereby makes power consumptionReduce. For the previous case, can be by the living area electricity consumption situation in this time period of combing,Find out new energy-saving potential point and developed and improve, will use living area for the second situationElectricity equipment is tested, and finds faulty equipment, gets rid of potential safety hazard.

Generally speaking, the core scheme of above-described embodiment is: from generated energy, temperature, rainfall andThe power consumption in living area is sought to estimate in other potential four aspects of influence factor. First comprehensiveConsider the impact of every influence factor on power consumption. By analyzing the historical data of each influence factorChange and the variation of power consumption, determine the relation of each factor and generated energy; And then set up and liveDistrict's electric model; Then utilize improved least square method to the model in different temperature rangesSolve coefficient matrix and constant term; Then obtain electricity consumption according to model and the historical data set upPerformance assessment criteria---the living area power consumption and direct station service ratio of amount; Utilize the model of having set upCalculate living area power consumption; Finally calculate performance assessment criteria, examination living area electricity consumption situation, according toCheck gives corresponding evaluation to living area electricity consumption, and takes corresponding administration of energy conservation measure.

For a person skilled in the art, can be according to technical scheme described above and structureThink, make other various corresponding changes and distortion, and all these changes and distortionWithin all should belonging to the protection domain of the claims in the present invention.

Claims (1)

1. living area, a power station electrical energy consumption analysis method, is characterized in that, comprises the following steps:

Step 1: obtain the historical measured data in living area, historical data and current data; InstituteState historical voltage, electric current and power that the historical measured data in living area comprises living area because ofElement, calculates historical living area power consumption by historical living area measured data; InstituteState historical data and comprise historical rainfall, temperature, generated energy and direct station-service electric weight; InstituteState current data and comprise current rainfall, temperature, generated energy and direct station-service electric weight;

Step 2: produce the curve map that historical living area power consumption and historical temperature form,Find out the curve of historical living area power consumption in curve map and the curve intersection of historical temperatureThe corresponding temperature range of part, determines relevant turning point by the concept in relevant turning point territoryTerritory (T1, T2);

In relevant turning point territory (T1, T2), on average get a m, the temperature that each point is correspondingFor:

$\begin{array}{cc}{T}_i=T1+\frac{T2-T1}{m}*i& (i=1,2...m)\end{array}---\left(1\right)$

Utilize least square method, obtain respectively the front territory coefficient correlation of m pointcorr[Y(Temp＞T_i)] and converse domain coefficient correlation corr[Y (Temp < T_i)], Y is that historical living area is usedElectric weight, Temp is historical temperature, before and after then asking, territory coefficient correlation is poor, finds out and meets public affairsThe point of formula (2) condition, this point is optimum relevant turning point, tries to achieve the temperature of optimum turning pointDegree is T_p：

max(corr[Y(Temp＞T_i)]-corr[Y(Temp＜T_i)])(2)

Step 3: set up living area electric model,

Living area power consumption y_{Living area power consumption}Expression formula as follows:

Wherein,

x₁For minimum temperature, x₂For rainfall, x₃For generated energy, c_iAnd c_jBe constant;

Step 4: solve the relation of living area power consumption and each influence factor,

As temperature x₁＜T_pTime, calculate living area power consumption, model solution process taking week as unitAs follows:

4.1: initialize: generate at random the particle of some in the septuple space, and given grainSon initial position, speed and historical optimal location, in population three of i particle toAmount is respectively:

Current position: c_i＝(c_i1,c_i2,c_i3,c_i4,c_i5,c_i6,c_i7)(6)

Historical optimal location: p_i＝(p_i1,p_i2,p_i3,p_i4,p_i5,p_i6,p_i7)(7)

Speed: v_i＝(v_i1,v_i2,v_i3,v_i4,v_i5,v_i6,v_i7)(8)

The desired positions searching up to now in whole population is labeled as:

p_g＝(p_g1,p_g2,p_g3,p_g4,p_g5,p_g6,p_g7)(9)

4.2: set up coefficient matrix, to each particle, according to its locus, set up as followsEquation group:

$\begin{array}{c}{\mathrm{\&theta;}}_0{c}_{i1}+{\mathrm{\&theta;}}_1{x}_{111}+{\mathrm{\&theta;}}_2{x}_{112}+{\mathrm{\&theta;}}_3{x}_{113}=y_{11}\\ {\mathrm{\&theta;}}_0{c}_{i2}+{\mathrm{\&theta;}}_1{x}_{121}+{\mathrm{\&theta;}}_2{x}_{122}+{\mathrm{\&theta;}}_3{x}_{123}=y_{12}\\ .\\ .\\ .\\ {\mathrm{\&theta;}}_0{c}_{i7}+{\mathrm{\&theta;}}_1{x}_{171}+{\mathrm{\&theta;}}_2{x}_{172}+{\mathrm{\&theta;}}_3{x}_{173}=y_{17}\\ {\mathrm{\&theta;}}_0{c}_{i1}+{\mathrm{\&theta;}}_1{x}_{211}+{\mathrm{\&theta;}}_2{x}_{212}+{\mathrm{\&theta;}}_3{x}_{213}=y_{21}\\ .\\ .\\ .\\ {\mathrm{\&theta;}}_0{c}_{i7}+{\mathrm{\&theta;}}_1{x}_{271}+{\mathrm{\&theta;}}_2{x}_{272}+{\mathrm{\&theta;}}_3{x}_{273}=y_{27}\\ {\mathrm{\&theta;}}_0{c}_{i1}+{\mathrm{\&theta;}}_1{x}_{311}+{\mathrm{\&theta;}}_2{x}_{312}+{\mathrm{\&theta;}}_3{x}_{313}=y_{31}\\ .\\ .\\ .\\ {\mathrm{\&theta;}}_0{c}_{i7}+{\mathrm{\&theta;}}_1{x}_{n71}+{\mathrm{\&theta;}}_2{x}_{n72}+{\mathrm{\&theta;}}_3{x}_{n73}=y_{n7}\end{array}---\left(10\right)$

X in formula_1ijMeet x_1ij＜T_p，θ₀For the influence factor of latency to living area power consumptionCoefficient, θ₁For the influence factor coefficient of temperature to living area power consumption, θ₂For rainfall is to living areaThe influence factor coefficient of power consumption, θ₃For the influence factor coefficient of generated energy to living area power consumption;

4.3: first, utilize least square method to solve coefficient matrix, the side in formula (10)Journey group is overdetermined equation, can try to achieve coefficient matrix θ least square in above formula by least square method and estimateCount:

θ'＝(Χ^TΧ)^-1Χ^TY(11)

Wherein $Y=\left(\begin{array}{c}{y}_{11}\\ y_{12}\\ .\\ .\\ .\\ y_{n7}\end{array}\right),$ $X=\left(\begin{array}{cccc}{c}_{i1}& x_{111}& x_{112}& x_{113}\\ c_{i2}& x_{121}& x_{122}& x_{123}\\ .& & & .\\ .& & & .\\ .& & & .\\ c_{i7}& x_{n71}& x_{n72}& x_{n73}\end{array}\right),$ $\mathrm{\&theta;}=\left(\begin{array}{c}{\mathrm{\&theta;}}_0\\ {\mathrm{\&theta;}}_1\\ {\mathrm{\&theta;}}_2\\ {\mathrm{\&theta;}}_3\end{array}\right)$

Then, evaluate the fitness of i particle, calculated by following formula:

${\mathrm{\&Phi;}}_i=\underset{k=1}{\overset{n}{\&Sigma;}}\underset{j=1}{\overset{7}{\&Sigma;}}{(y_{kj}-{\mathrm{\&theta;}}_0{c}_{ij}-{\mathrm{\&theta;}}_1{x}_{kj1}-{\mathrm{\&theta;}}_2{x}_{kj2}-{\mathrm{\&theta;}}_3{x}_{kj3})}^2---\left(12\right)$

4.4: upgrade optimal location: first, to i particle, relatively particle fitness Φ_iWithThe fitness of its individual optimal valueIndividual initial optimal value fitness is by given at firstHistorical optimal location determine, be calculated as follows:

${\mathrm{\&Phi;}}_{p_i}=\underset{k=1}{\overset{n}{\&Sigma;}}\underset{j=1}{\overset{7}{\&Sigma;}}{(y_{kj}-{\mathrm{\&theta;}}_0{p}_{ij}-{\mathrm{\&theta;}}_1{x}_{kj1}-{\mathrm{\&theta;}}_2{x}_{kj2}-{\mathrm{\&theta;}}_3{x}_{kj3})}^3---\left(13\right)$

IfThe historical optimal location using current point as this particle, otherwise,Historical optimal location remains unchanged, then more overall particle fitness, ifUse p_iReplace current p_g；

4.5: more new particle: for each particle, its d dimension changes according to following equation,Wherein, 1≤d≤7:

v_id＝v_id+c₁×rand(1)×(p_id-c_id)+c₂×rand(1)×(p_gd-c_id)(14)

c_id＝c_id+v_id(15)

Wherein v_idRepresent the value of i the d dimension in particle rapidity vector, p_idRepresent theThe value of the d dimension in i the historical optimal location vector of particle, c_idRepresent that i particle is currentThe value of the d dimension in position vector, aceleration pulse c1 and c2 are two nonnegative values, rand(1) be the random number in the interior value of scope [0,1], the speed of particle is limited in a scopeIn [Vmax, Vmax], Vmax is the maximum for maximum speed limit, judges the speed of particleWhether, in limited field, if in limited field, carry out formula (15), if superLimit, upgrades position according to maximum constraints speed;

4.6: end condition: in the time that population converges to the neighborhood of certain diameter, and meet publicWhen formula (16), finish to calculate, otherwise rebound step 2 repetitive cycling is until meet formula (16)Till,

$\begin{array}{ccc}max\left(\underset{k=1}{\overset{7}{\&Sigma;}}{\left(c_{ik}-c_{jk}\right)}^2\right)\&le;\mathrm{\&epsiv;}& (i=1,2,3...)& (j=1,2,3...)\end{array}---\left(16\right)$ C in formula_ik，c_jkFor required particle position, ε is little positive number given in advance;

As temperature x₁＞T_pTime, repetitive process 4.1-4.6, wherein in step 4.2, x_1ijExpireFoot x_1ij＞T_p；

Step 5: utilize the living area electric model that step 3 is set up to calculate according to current dataCurrent living area power consumption.