CN105046593A - Intelligent power distribution and utilization evaluation method conforming to low-carbon energy policy - Google Patents

Abstract

The invention discloses an intelligent power distribution and utilization evaluation method conforming to the low-carbon energy policy. The method comprises the steps of selecting multiple evaluation indicators which embody low carbon benefits at the power distribution side and the power utilization side of a smart power grid, and utilizing the entropy weight method for determining indicator weight for indicator values; then adopting the support vector machine method for conducting dynamic prediction analysis. The method comprises the specific steps of establishing power distribution and utilization indicator models, conducting data pre-processing on the established indicators, adopting the entropy weight method for evaluating the indicators, and adopting the support vector machine method for conducting the dynamic prediction analysis on the indicators. By means of the method, on one hand, aiming at the benefits of the power distribution side and utilization side of the smart power grid which are obtained in energy saving and emission reduction, the dynamic evaluation method which conforms to the low-carbon energy policy can be raised for being adapted to the development process of intelligent power distribution and utilization systems, the development level of relevant indicators at the power distribution side and power utilization side of the smart power grid can be reflected, on the other hand, the development law of each indicator within a certain time frame in future can be researched, and it is facilitated to formulate relevant policies for promoting the intelligent power distribution and utilization systems to develop in the low carbon direction.

Description

A kind of intelligent adapted electricity evaluation method meeting low-carbon energy policy

Technical field

The invention belongs to intelligent grid assessment technique field, particularly relate to a kind of intelligent adapted electricity evaluation method meeting low-carbon energy policy.

Background technology

Power industry, as the rich and influential family of carbon emission, carries the pressure of huge emission reduction and low carbonization reform, is also the main force promoting low-carbon economy development simultaneously.In order to respond the call that national energy-saving reduces discharging, driving whole power industry to the development of the direction harmonious orderly of low carbonization, being necessary the intelligent adapted electricity evaluation method setting up a kind of low carbonization.In intelligent grid distribution side, along with the extensive access of blower fan, photovoltaic distributed power supply, regenerative resource raises gradually in electrical production and consumptive link ratio, has effectively promoted the process of the low carbonization of electrical network; In user side, intelligent electric meter can carry out the management of power use effectively, reduces unnecessary waste, and thus, universal along with intelligent electric meter, and the increasing of electric automobile CER proportion, the low carbonizing degree of user side also can improve thereupon.Therefore distributed power source access amount is chosen to intelligent grid distribution side, user chooses side electric automobile CER and these three indexs of intelligent electric meter popularity rate, effectively can embody the low carbonizing degree of intelligent grid adapted electricity side, the demand promoting intelligent grid to join a side and electricity consumption side to meet low carbonization policy.

Current numerous scholar has carried out the correlative study of evaluation index and evaluation method for intelligent grid.But the research and practice of current evaluation mainly concentrates on to set forth and how to design evaluation index, rarely has the time dependent characteristic of index embodying various design in evaluation method.

Summary of the invention

The object of the present invention is to provide a kind of intelligent adapted electricity evaluation method meeting low-carbon energy policy, be intended to propose a kind of dynamic evaluation method meeting low-carbon energy policy to adapt to the evolution of intelligent distribution system, the index of correlation development level of reflection intelligent grid adapted electricity side, and be embodied in the rule of development of each index within the scope of certain hour from now on, be conducive to formulating relevant policies to promote intelligent distribution system to low carbonization future development.

The present invention realizes like this, a kind of intelligent adapted electricity evaluation method meeting low-carbon energy policy, by this evaluation method and evaluation system, reflect intelligent adapted electricity side index of correlation development degree, each index rule of development after embodiment certain hour, can be infiltration and promote that the correlation technique of the low carbonization development of intelligent grid and policy provide foundation, the call that response national energy-saving reduces discharging.By choosing intelligent grid distribution side and electricity consumption side, several embody the evaluation index of low-carbon (LC) benefits to the described intelligent adapted electricity evaluation method meeting low-carbon energy policy, use entropy assessment to desired value agriculture products weight; Then support vector machine method is adopted to carry out performance prediction analysis; Specifically comprise:

Step one, sets up adapted electricity index model;

Step 2, carries out data prediction to the index set up;

Step 3, adopts entropy assessment to evaluate index;

Step 4, adopts support vector machine method to carry out performance prediction analysis to index.

Further, described adapted electricity index model of setting up comprises three index models:

Distributed power source access amount: comprise blower fan access amount and photovoltaic access amount;

Wherein, blower fan access amount in this formula, ρ is atmospheric density, and S is swept area; C _pfor power factor, V is wind speed;

Photovoltaic access amount in formula, P _dCfor the DC power of the actual output of solar power generation unit; P _sTCfor the DC power that solar power generation unit exports under standard test condition; G _sTCfor under the test condition of standard, solar radiation degree, unit is W/m ²; G _afor the solar radiation degree under physical condition; T _sTCfor under the test condition of standard, the temperature of solar power generation unit, gets 25 DEG C usually; T _cfor the actual temperature of cell panel; C _tfor temperature power coefficient, by making, manufacturer provides; NOCT is solar power generation unit temperature under normal operating conditions, usually gets 20 DEG C; T _nfor ambient temperature; Thus distributed power source access amount S ₁=P _m+ P _dC;

Electric automobile CER: electric automobile CER S ₂=N _ce _cb _c; N in formula _crepresent electric automobile recoverable amount, E _crepresent average each power consumption, b in electric automobile year _crepresent the dusty gas CER of electric automobile unit used electricity amount;

Intelligent electric meter popularity rate: intelligent electric meter popularity rate in formula, I _mrepresent intelligent electric meter cost of investment, r _mrepresent intelligent electric meter unit cost, N _mrepresent power supply amount.

Further, the described index to setting up is carried out data prediction and is comprised:

Eliminated the difference of each index unit and the order of magnitude by nondimensionalization process, adopt standardized method, normalization method; S the most at last ₁, S ₂, S ₃the data mode of three indexs is unified, is designated as s respectively ₁, s ₂, s ₃.

Further, described employing entropy assessment is evaluated index and is specifically comprised:

Will to m data analysis, the annual packet analyzed is containing index s ₁, s ₂, s ₃;

Form matrix $S={\left(s_{ij}\right)}_{m\×3}=\left[\begin{array}{ccc}{s}_{11}& s_{12}& s_{13}\\ .& .& .\\ .& .& .\\ .& .& .\\ s_{m1}& s_{m2}& s_{m3}\end{array}\right];$

Ask the probability P that factor of influence occurs _ij, wherein i=1,2 ..., m; J=1,2,3;

Ask the entropy E that a jth factor of influence exports _j: j=1,2,3;

In formula, $K=\frac{1}{\ln m};$

Obtain the entropy power D of a jth factor of influence _j:

In formula, due to the columns of n representing matrix S, thus n=3.

Further, described employing support vector machine method is carried out performance prediction analysis to index and is specifically comprised:

Adopt support vector machine method to carry out performance prediction to index, treat that regression data integrates as Z={x _iy _i, i=1,2 ..., n, x _i∈ R ⁿfor n ties up input quantity, y _ifor output quantity;

First SVM estimation function is constructed: in formula, for the input space is to the Nonlinear Mapping of high-dimensional feature space, w and b is coefficient, by following formula determine, wherein represent empiric risk, weighed by ε insensitive loss function, represent regularization part;

Next solves parameter factors w: introduce Lagrange factor α _iand α _i ^*, structure Lagrangian function in formula, α _iand α _i ^*satisfy condition α _i× α ^* _i=0, and α _i>=0, α ^* _i>=0; Former problem turns in constraint condition with $\left\{\begin{array}{c}0\≤{\mathrm{\α}}_i\≤c\\ 0\≤{{\mathrm{\α}}^{*}}_i\≤c\end{array}\right.$ Solve maximum secondary type $R({\mathrm{\α}}_i,{{\mathrm{\α}}_i}^{*})=-\mathrm{\ϵ}\underset{i=1}{\overset{n}{\Σ}}({\mathrm{\α}}_i+{{\mathrm{\α}}_i}^{*})+\underset{i=1}{\overset{n}{\Σ}}{d}_i({{\mathrm{\α}}_i}^{*}-{\mathrm{\α}}_i)-\frac{1}{2}\underset{i,j=1}{\overset{n}{\Σ}}({{\mathrm{\α}}_i}^{*}-{\mathrm{\α}}_i)({{\mathrm{\α}}_i}^{*}-{\mathrm{\α}}_j)K(x_i,x_j),$ Its Kernel Function calculate Lagrange factor α thus _iand α _i ^*, passing through formula calculate parameter w;

Then estimated parameter factor b: adopt Kuhn-Tucker condition, selects to send as an envoy to predicated error δ _k=f (x _k)-y _kuniquely by the Lagrange factor α determined _iand α _i ^*, through type obtain b;

Last output-index predicts the outcome.

Another object of the present invention is to provide a kind of intelligent adapted electricity evaluation system meeting low-carbon energy policy, described in meet low-carbon energy policy intelligent adapted electricity evaluation system comprise:

Adapted electricity Index module, sets up adapted electricity index model;

Pretreatment module, carries out data prediction to the index set up;

Evaluation module, adopts entropy assessment to evaluate index;

Analysis module, adopts support vector machine method to carry out performance prediction analysis to index.

Further, described adapted electricity Index module comprises further:

Distributed power source access amount unit;

Electric automobile CER unit;

Intelligent electric meter popularity rate unit.

Further, described evaluation module comprises further:

Matrix unit, forms normalized matrix;

Probability unit, asks the probability that factor of influence occurs;

Factor of influence output unit, asks the entropy that each factor of influence exports;

Factor of influence entropy power unit, obtains the entropy power of a jth factor of influence.

Further, described analysis module comprises further:

Function unit, structure SVM estimation function;

Solve unit, solve parameter factors;

Output unit, output-index predicts the outcome.

The intelligent adapted electricity evaluation method meeting low-carbon energy policy provided by the invention, the evolution that a kind of dynamic evaluation method meeting low-carbon energy policy adapts to intelligent distribution system is proposed by the benefit obtained in energy-saving and emission-reduction intelligent grid adapted electricity side, the development level of the index of correlation of intelligent grid adapted electricity side can be reflected on the one hand, the rule of development of each index within the scope of certain hour from now on can be studied in another aspect, is conducive to formulating relevant policies to promote intelligent distribution system to low carbonization future development.

Accompanying drawing explanation

Fig. 1 is the intelligent adapted electricity evaluation method process flow diagram meeting low-carbon energy policy that the embodiment of the present invention provides.

Fig. 2 is the process flow diagram meeting the intelligent adapted electricity evaluation method embodiment of low-carbon energy policy that the embodiment of the present invention provides.

Embodiment

In order to make object of the present invention, technical scheme and advantage clearly understand, below in conjunction with embodiment, the present invention is further elaborated.Should be appreciated that specific embodiment described herein only in order to explain the present invention, be not intended to limit the present invention.

The present invention, by choosing the evaluation index of intelligent grid distribution side and several embodiment low-carbon (LC) benefits of electricity consumption side, uses entropy assessment to These parameters value agriculture products weight, avoids the impact of subjective factor; Then support vector machine (SVM) method is adopted to carry out performance prediction analysis.

Below in conjunction with accompanying drawing, application principle of the present invention is explained in detail.

As shown in Figure 1, the intelligent adapted electricity evaluation method meeting low-carbon energy policy of the embodiment of the present invention comprises the following steps:

S101: set up adapted electricity index model;

S102: data prediction is carried out to the index set up;

S103: adopt entropy assessment to evaluate index;

S104: adopt SVM method to carry out performance prediction analysis to index.

In step S101, adapted electricity metrics evaluation model comprises three index models:

(1) distributed power source access amount: comprise blower fan access amount and photovoltaic access amount.

(2) electric automobile CER.

(3) intelligent electric meter popularity rate.

In step s 102, data prediction is carried out to above three indexs, the data mode of unified each index: by the optimization value direction of the unified each index of unification process, can adopt and ask poor, ask the methods such as inverse; Eliminated the difference of each index unit and the order of magnitude by nondimensionalization process, standardized method, normalization method can be adopted.The data mode of three indexs is unified the most at last.

Entropy assessment is adopted to evaluate each index in step s 103:

(1) normalized matrix is formed.

(2) probability asking factor of influence to occur.

(3) entropy that each factor of influence exports is asked

(4) the entropy power of a jth factor of influence is obtained.

In step S104, support vector machine method (SVM) is adopted to carry out performance prediction to index:

(1) SVM estimation function is constructed.

(2) parameter factors is solved.

(3) output-index predicts the outcome.

Below in conjunction with specific embodiment, effect of the present invention is further described.

The present invention, by choosing the evaluation index of intelligent grid distribution side and several embodiment low-carbon (LC) benefits of electricity consumption side, uses entropy assessment to These parameters value agriculture products weight, avoids the impact of subjective factor; Then support vector machine (SVM) method is adopted to carry out performance prediction analysis.The present invention has following four steps:

Step one, sets up adapted electricity index model;

Step 2, carries out data prediction to the index set up;

Step 3, adopts entropy assessment to evaluate index;

Step 4, adopts SVM method to carry out performance prediction analysis to index.

The specific embodiment of the present invention as shown in Figure 2.

Step one, set up adapted electricity index model:

Adapted electricity metrics evaluation model comprises three index models:

(1) distributed power source access amount: comprise blower fan access amount and photovoltaic access amount.

Wherein, blower fan access amount in this formula, ρ is atmospheric density, and S is swept area; C _pfor power factor, V is wind speed;

Photovoltaic access amount in the formula, P _dCfor the DC power of the actual output of solar power generation unit; P _sTCfor the DC power that solar power generation unit exports under standard test condition; G _sTCfor under the test condition of standard, solar radiation degree, unit is W/m ²; G _afor the solar radiation degree under physical condition; T _sTCfor under the test condition of standard, the temperature of solar power generation unit, gets 25 DEG C usually; T _cfor the actual temperature of cell panel; C _tfor temperature power coefficient, by making, manufacturer provides; NOCT is solar power generation unit temperature under normal operating conditions, usually gets 20 DEG C; T _nfor ambient temperature.

Thus distributed power source access amount S ₁=P _m+ P _dC.

(2) electric automobile CER: electric automobile CER S ₂=N _ce _cb _c.N in the formula _crepresent electric automobile recoverable amount (ten thousand), E _crepresent each electric automobile year power consumption (kWh/) average, b _crepresent the dusty gas CER (kg/kWh) of electric automobile unit used electricity amount.

(3) intelligent electric meter popularity rate: intelligent electric meter popularity rate in the formula, I _mrepresent intelligent electric meter cost of investment (unit), r _mrepresent intelligent electric meter unit cost (unit/family), N _mrepresent power supply amount.

Step 2, data prediction is carried out to the index set up:

In this step, data prediction is carried out to above three indexs, the data mode of unified each index: by the optimization value direction of the unified each index of unification process, can adopt and ask poor, ask the methods such as inverse; Eliminated the difference of each index unit and the order of magnitude by nondimensionalization process, standardized method, normalization method can be adopted.S the most at last ₁, S ₂, S ₃the data mode of three indexs is unified, is designated as s respectively ₁, s ₂, s ₃.

Step 3, adopts entropy assessment to evaluate index:

If will to m data analysis, the annual packet analyzed is containing index s ₁, s ₂, s ₃.

(1) matrix is formed $S={\left(s_{ij}\right)}_{m\×3}=\left[\begin{array}{ccc}{s}_{11}& s_{12}& s_{13}\\ .& .& .\\ .& .& .\\ .& .& .\\ s_{m1}& s_{m2}& s_{m3}\end{array}\right].$

(2) probability P asking factor of influence to occur _ij, wherein i=1,2 ..., m; J=1,2,3.

(3) the entropy E that a jth factor of influence exports is asked _j:

In the formula,

(4) the entropy power D of a jth factor of influence is obtained _j:

In the formula, due to the columns of n representing matrix S, thus n=3.

Step 4, adopts SVM method to carry out performance prediction analysis to index:

In this step, support vector machine method (SVM) is adopted to carry out performance prediction to index, if treat that regression data integrates as Z={x _iy _i, i=1,2 ..., n.X _i∈ R ⁿfor n ties up input quantity, y _ifor output quantity.

(1) SVM estimation function is constructed: in the formula, for the input space is to the Nonlinear Mapping of high-dimensional feature space, w and b is coefficient, by following formula determine, wherein represent empiric risk, weighed by ε insensitive loss function, represent regularization part.

(2) parameter factors w is solved: introduce Lagrange factor α _iand α _i ^*, structure Lagrangian function in this formula, α _iand α _i ^*satisfy condition α _i× α ^* _i=0, and α _i>=0, α ^* _i>=0.Thus former problem turns in constraint condition with $\left\{\begin{array}{c}0\≤{\mathrm{\α}}_i\≤c\\ 0\≤{{\mathrm{\α}}^{*}}_i\≤c\end{array}\right.$ Solve maximum secondary type $R({\mathrm{\α}}_i,{{\mathrm{\α}}_i}^{*})=-\mathrm{\ϵ}\underset{i=1}{\overset{n}{\Σ}}({\mathrm{\α}}_i+{{\mathrm{\α}}_i}^{*})+\underset{i=1}{\overset{n}{\Σ}}{d}_i({{\mathrm{\α}}_i}^{*}-{\mathrm{\α}}_i)-\frac{1}{2}\underset{i,j=1}{\overset{n}{\Σ}}({{\mathrm{\α}}_i}^{*}-{\mathrm{\α}}_i)({{\mathrm{\α}}_i}^{*}-{\mathrm{\α}}_j)K(x_i,x_j),$ Its Kernel Function lagrange factor α can be calculated thus _iand α _i ^*, passing through formula calculate parameter w.

(3) estimated parameter factor b: adopt Kuhn-Tucker condition, selects to send as an envoy to predicated error δ _k=f (x _k)-y _kuniquely by the Lagrange factor α determined _iand α _i ^*, through type obtain b.

(4) output-index predicts the outcome.

The foregoing is only preferred embodiment of the present invention, not in order to limit the present invention, all any amendments done within the spirit and principles in the present invention, equivalent replacement and improvement etc., all should be included within protection scope of the present invention.

Claims (9)

1. one kind meets the intelligent adapted electricity evaluation method of low-carbon energy policy, it is characterized in that, by choosing intelligent grid distribution side and electricity consumption side, several embody the evaluation index of low-carbon (LC) benefits to the described intelligent adapted electricity evaluation method meeting low-carbon energy policy, use entropy assessment to desired value agriculture products weight; Then support vector machine method is adopted to carry out performance prediction analysis; Specifically comprise:

Step one, sets up adapted electricity index model;

Step 2, carries out data prediction to the index set up;

Step 3, adopts entropy assessment to evaluate index;

Step 4, adopts support vector machine method to carry out performance prediction analysis to index.

2. meet the intelligent adapted electricity evaluation method of low-carbon energy policy as claimed in claim 1, it is characterized in that, described adapted electricity index model of setting up comprises three index models:

Distributed power source access amount: comprise blower fan access amount and photovoltaic access amount;

Wherein, blower fan access amount in this formula, ρ is atmospheric density, and S is swept area; C _pfor power factor, V is wind speed;

Photovoltaic access amount in formula, P _dCfor the DC power of the actual output of solar power generation unit; P _sTCfor the DC power that solar power generation unit exports under standard test condition; G _sTCfor under the test condition of standard, solar radiation degree, unit is W/m ²; G _afor the solar radiation degree under physical condition; T _sTCfor under the test condition of standard, the temperature of solar power generation unit, gets 25 DEG C usually; T _cfor the actual temperature of cell panel; C _tfor temperature power coefficient, by making, manufacturer provides; NOCT is solar power generation unit temperature under normal operating conditions, usually gets 20 DEG C; T _nfor ambient temperature; Thus distributed power source access amount S ₁=P _m+ P _dC;

Electric automobile CER: electric automobile CER S ₂=N _ce _cb _c; N in formula _crepresent electric automobile recoverable amount, E _crepresent average each power consumption, b in electric automobile year _crepresent the dusty gas CER of electric automobile unit used electricity amount;

Intelligent electric meter popularity rate: intelligent electric meter popularity rate in formula, I _mrepresent intelligent electric meter cost of investment, r _mrepresent intelligent electric meter unit cost, N _mrepresent power supply amount.

3. meet the intelligent adapted electricity evaluation method of low-carbon energy policy as claimed in claim 1, it is characterized in that, the described index to setting up is carried out data prediction and is comprised:

Eliminated the difference of each index unit and the order of magnitude by nondimensionalization process, adopt standardized method, normalization method; S the most at last ₁, S ₂, S ₃the data mode of three indexs is unified, is designated as s respectively ₁, s ₂, s ₃.

4. meet the intelligent adapted electricity evaluation method of low-carbon energy policy as claimed in claim 1, it is characterized in that, described employing entropy assessment is evaluated index and is specifically comprised:

Will to m data analysis, the annual packet analyzed is containing index s ₁, s ₂, s ₃;

Form matrix $S={\left(s_{ij}\right)}_{m\×3}=\left[\begin{array}{ccc}{s}_{11}& s_{12}& s_{13}\\ .& .& .\\ .& .& .\\ .& .& .\\ s_{m1}& s_{m2}& s_{m3}\end{array}\right];$

Ask the probability P that factor of influence occurs _ij, wherein i=1,2 ..., m; J=1,2,3;

Ask the entropy E that a jth factor of influence exports _j: $E_j=-K\underset{i=1}{\overset{m}{\Σ}}{P}_{ij}\ln P_{ij},j=1,2,3;$

In formula, $K=\frac{1}{\ln m};$

Obtain the entropy power D of a jth factor of influence _j:

In formula, due to the columns of n representing matrix S, thus n=3.

5. meet the intelligent adapted electricity evaluation method of low-carbon energy policy as claimed in claim 1, it is characterized in that, described employing support vector machine method is carried out performance prediction analysis to index and is specifically comprised:

Adopt support vector machine method to carry out performance prediction to index, treat that regression data integrates as Z={x _iy _i, i=1,2 ..., n, x _i∈ R ⁿfor n ties up input quantity, y _ifor output quantity;

First SVM estimation function is constructed: in formula, for the input space is to the Nonlinear Mapping of high-dimensional feature space, w and b is coefficient, by following formula determine, wherein represent empiric risk, weighed by ε insensitive loss function, represent regularization part;

Next solves parameter factors w: introduce Lagrange factor α _iand α _i ^*, structure Lagrangian function in formula, α _iand α _i ^*satisfy condition α _i× α ^* _i=0, and α _i>=0, α ^* _i>=0; Former problem turns in constraint condition with solve maximum secondary type $R({\mathrm{\α}}_i,{{\mathrm{\α}}_i}^{*})=-\mathrm{\ϵ}\underset{i=1}{\overset{n}{\Σ}}({\mathrm{\α}}_i+{{\mathrm{\α}}_i}^{*})+\underset{i=1}{\overset{n}{\Σ}}{d}_i({{\mathrm{\α}}_i}^{*}-{\mathrm{\α}}_i)-\frac{1}{2}\underset{i,j=1}{\overset{n}{\Σ}}({{\mathrm{\α}}_i}^{*}-{\mathrm{\α}}_i)({{\mathrm{\α}}_i}^{*}-{\mathrm{\α}}_j)K(x_i,x_j),$ Its Kernel Function calculate Lagrange factor α thus _iand α _i ^*, passing through formula calculate parameter w;

Then estimated parameter factor b: adopt Kuhn-Tucker condition, selects to send as an envoy to predicated error δ _k=f (x _k)-y _kuniquely by the Lagrange factor α determined _iand α _i ^*, through type obtain b;

Last output-index predicts the outcome.

6. meet an intelligent adapted electricity evaluation system for the intelligent adapted electricity evaluation method of low-carbon energy policy as claimed in claim 1, it is characterized in that, described intelligent adapted electricity evaluation system comprises:

Adapted electricity Index module, sets up adapted electricity index model;

Pretreatment module, carries out data prediction to the index set up;

Evaluation module, adopts entropy assessment to evaluate index;

Analysis module, adopts support vector machine method to carry out performance prediction analysis to index.

7. intelligent adapted electricity evaluation system as claimed in claim 6, is characterized in that, described adapted electricity Index module comprises further:

Distributed power source access amount unit;

Electric automobile CER unit;

Intelligent electric meter popularity rate unit.

8. intelligent adapted electricity evaluation system as claimed in claim 6, it is characterized in that, described evaluation module comprises further:

Matrix unit, forms normalized matrix;

Probability unit, asks the probability that factor of influence occurs;

Factor of influence output unit, asks the entropy that each factor of influence exports;

Factor of influence entropy power unit, obtains the entropy power of a jth factor of influence.

9. intelligent adapted electricity evaluation system as claimed in claim 6, it is characterized in that, described analysis module comprises further:

Function unit, structure SVM estimation function;

Solve unit, solve parameter factors;

Output unit, output-index predicts the outcome.