CN103870886A - Prediction method for monthly maximal power consumption of enterprise - Google Patents

Abstract

A prediction method for the monthly maximal power consumption of an enterprise relates to the technical field of power systems and solves the technical problem about reducing the prediction error. The method comprises the steps of dividing all power consumption outlet line loads in the enterprise into four types according to the load sensitivity features of the enterprise power consumption; respectively setting four feature sensitivity coefficients for each power consumption outlet line load according to the load feature of each power consumption outlet line load; then obtaining a month weather prediction average temperature value, a month weather prediction sunlight light intensity value and a month prediction yield value of the prediction month and the month average temperature value, the month sunlight light intensity value and the month yield value of a reference historical month; calculating the feature function of the power consumption outlet line load of the enterprise according to the feature sensitivity coefficients and the obtained data, and further calculating the monthly maximal power consumption of the enterprise. According to the method, the power consumption cost of the enterprise can be reduced and the energy waste can be reduced.

Description

The business electrical moon maximum demand Forecasting Methodology

Technical field

The present invention relates to power system technology, particularly relate to the technology of the Forecasting Methodology of a kind of business electrical moon maximum demand.

Background technology

Electricity consumption maximum demand refers to that power consumer is within the single charge calculation cycle, the maximal value of time per unit electricity consumption average load, and namely a certain moment of power consumer within the single charge calculation cycle used the maximum active power value of electric energy.The electricity charge of electricity consumption maximum demand and power consumer are directly related, control electricity consumption maximum demand and can make enterprise's using electricity wisely cost, and power-generating enterprise also can reduce power equipment investment.Enterprise customer declares after moon electricity consumption maximum demand to power-generating enterprise, and power-generating enterprise can coordinate electrical energy production and distribution preferably, also can reduce enterprise customer's electric cost simultaneously.

But, current enterprise customer is the electricity consumption moon maximum demand of estimating following middle of the month with self experience, it is this that to estimate the electricity consumption moon maximum demand error that mode draws conventionally larger, to enterprise save electric cost and reduce help aspect energy dissipation extremely limited.

Summary of the invention

For the defect existing in above-mentioned prior art, it is little that technical matters to be solved by this invention is to provide a kind of predicated error, can effectively save business electrical cost and reduce energy dissipation the business electrical moon maximum demand Forecasting Methodology.

In order to solve the problems of the technologies described above, a kind of business electrical moon provided by the present invention maximum demand Forecasting Methodology, it is characterized in that, concrete steps are as follows:

1), according to the load-sensitive characteristic of business electrical, all electricity consumptions in enterprise are gone out to specific electric load and be divided into A, B, C, D totally 4 classes;

Wherein, weather temperature sensitive kinds load is included into category-A, and sunshine, light intensity sensitive kinds load was included into category-B, and firms output sensitive kinds load is included into C class, and immobilize load and other loads are all included into D class;

2) go out the part throttle characteristics of specific electric load according to each electricity consumption, for each electricity consumption goes out specific electric load, 4 characteristic sensitivity coefficients are respectively set, be respectively category-A load-sensitive coefficient, category-B load-sensitive coefficient, C type load sensitivity coefficient, D type load sensitivity coefficient;

The value rule of characteristic sensitivity coefficient is as follows: establish K _{i, 1}be the category-A load-sensitive coefficient that i electricity consumption goes out specific electric load, K _{i, 2}be the category-B load-sensitive coefficient that i electricity consumption goes out specific electric load, K _{i, 3}be the C type load sensitivity coefficient that i electricity consumption goes out specific electric load, K _{i, 4}it is the D type load sensitivity coefficient that i electricity consumption goes out specific electric load;

If it is category-A load that i electricity consumption goes out specific electric load, get K _{i, 1}=1, K _{i, 2}=K _{i, 3}=K _{i, 4}=0;

If it is category-B load that i electricity consumption goes out specific electric load, get K _{i, 2}=1, K _{i, 1}=K _{i, 3}=K _{i, 4}=0;

If it is C type load that i electricity consumption goes out specific electric load, get K _{i, 1}=K _{i, 2}=0.1, K _{i, 3}=1, K _{i, 4}=0;

If it is D type load that i electricity consumption goes out specific electric load, get K _{i, 4}=1, K _{i, 1}=K _{i, 2}=K _{i, 3}=0;

3) fundamental function that electricity consumption goes out specific electric load being set is:

$F\left(i\right)=\sqrt{{\left(K_{i,1\×}\frac{T1}{T0}\right)}^2+{(K_{i,2}\×\frac{W1}{W0})}^2+{(K_{i,3}\×\frac{P1}{P0})}^2+K_{i,4}^2}$

Wherein, F (i) goes out the fundamental function of specific electric load for i electricity consumption in enterprise, T1 is the moon weather prognosis average temperature value of predicted month, T0 is the monthly mean temperature value with reference to historical month, W1 is the moon weather prognosis light intensity value at sunshine of predicted month, W0 is the light intensity value at the sunshine moon with reference to historical month, the moon prediction output value that P1 is predicted month, and P0 is the moon output value with reference to historical month;

4) choose predicted month and reference historical month, obtain moon weather prognosis average temperature value, month weather prognosis light intensity value at sunshine, month prediction output value (this value is estimated by production division of enterprise) of predicted month, and with reference to the monthly mean temperature value of historical month, month sunshine light intensity value, month output value;

5) calculate all electricity consumptions in enterprise and go out the fundamental function of specific electric load, specific formula for calculation is:

$\mathrm{FA}=\underset{i=1}{\overset{m}{\mathrm{\Σ}}}F\left(i\right)$

Wherein, FA is the fundamental function that in enterprise, all electricity consumptions go out specific electric load, and m is that the electricity consumption in enterprise goes out specific electric load sum;

6) calculate the business electrical moon maximum demand of predicted month, specific formula for calculation is:

MD1=MD0×FA

Wherein, the business electrical moon maximum demand that MD1 is predicted month, MD0 is the business electrical moon maximum demand with reference to historical month.

The business electrical moon provided by the present invention maximum demand Forecasting Methodology, according to the load-sensitive characteristic of business electrical, business electrical is gone out to specific electric load and be divided into 4 classes, and for inhomogeneous load, different characteristic sensitivity coefficients is set, calculate electricity consumption and go out again the fundamental function of specific electric load in conjunction with historical month data, and then calculate the business electrical moon maximum demand of predicted month, the method combines environmental factor, output factor and the historical data of predicted month, can effectively reduce predicated error, can save business electrical cost, reduce energy dissipation.

Accompanying drawing explanation

Fig. 1 be the embodiment of the present invention the business electrical moon maximum demand the process flow diagram of Forecasting Methodology.

Embodiment

Below in conjunction with accompanying drawing explanation, embodiments of the invention are described in further detail, but the present embodiment is not limited to the present invention, every employing analog structure of the present invention and similar variation thereof, all should list protection scope of the present invention in.

As shown in Figure 1, the embodiment of the present invention provides a kind of business electrical moon maximum demand Forecasting Methodology, it is characterized in that, concrete steps are as follows:

1), according to the load-sensitive characteristic of business electrical, all electricity consumptions in enterprise are gone out to specific electric load and be divided into A, B, C, D totally 4 classes;

Wherein, weather temperature sensitive kinds load (such as air conditioner load) is included into category-A, light intensity sensitive kinds load at sunshine (such as lighting load) is included into category-B, and firms output sensitive kinds load (such as living load) is included into C class, and immobilize load and other loads are all included into D class;

2) go out the part throttle characteristics of specific electric load according to each electricity consumption, for each electricity consumption goes out specific electric load, 4 characteristic sensitivity coefficients are respectively set, be respectively category-A load-sensitive coefficient, category-B load-sensitive coefficient, C type load sensitivity coefficient, D type load sensitivity coefficient;

The value rule of characteristic sensitivity coefficient is as follows: establish K _{i, 1}be the category-A load-sensitive coefficient that i electricity consumption goes out specific electric load, K _{i, 2}be the category-B load-sensitive coefficient that i electricity consumption goes out specific electric load, K _{i, 3}be the C type load sensitivity coefficient that i electricity consumption goes out specific electric load, K _{i, 4}it is the D type load sensitivity coefficient that i electricity consumption goes out specific electric load;

If it is category-A load that i electricity consumption goes out specific electric load, get K _{i, 1}=1, K _{i, 2}=K _{i, 3}=K _{i, 4}=0;

If it is category-B load that i electricity consumption goes out specific electric load, get K _{i, 2}=1, K _{i, 1}=K _{i, 3}=K _{i, 4}=0;

If it is C type load that i electricity consumption goes out specific electric load, get K _{i, 1}=K _{i, 2}=0.1, K _{i, 3}=1, K _{i, 4}=0;

If it is D type load that i electricity consumption goes out specific electric load, get K _{i, 4}=1, K _{i, 1}=K _{i, 2}=K _{i, 3}=0;

3) fundamental function that electricity consumption goes out specific electric load being set is:

$F\left(i\right)=\sqrt{{\left(K_{i,1\×}\frac{T1}{T0}\right)}^2+{(K_{i,2}\×\frac{W1}{W0})}^2+{(K_{i,3}\×\frac{P1}{P0})}^2+K_{i,4}^2}$

Wherein, F (i) goes out the fundamental function of specific electric load for i electricity consumption in enterprise, T1 is the moon weather prognosis average temperature value of predicted month, T0 is the monthly mean temperature value with reference to historical month, W1 is the moon weather prognosis light intensity value at sunshine of predicted month, W0 is the light intensity value at the sunshine moon with reference to historical month, the moon prediction output value that P1 is predicted month, and P0 is the moon output value with reference to historical month;

4) choose predicted month and reference historical month, obtain moon weather prognosis average temperature value, month weather prognosis light intensity value at sunshine, month prediction output value (this value is estimated by production division of enterprise) of predicted month, and with reference to the monthly mean temperature value of historical month, month sunshine light intensity value, month output value;

Wherein, conventionally get the moon identical with predicted month month in the previous year with reference to the historical moon;

5) calculate all electricity consumptions in enterprise and go out the fundamental function of specific electric load, specific formula for calculation is:

$\mathrm{FA}=\underset{i=1}{\overset{m}{\mathrm{\Σ}}}F\left(i\right)$

Wherein, FA is the fundamental function that in enterprise, all electricity consumptions go out specific electric load, and m is that the electricity consumption in enterprise goes out specific electric load sum;

6) calculate the business electrical moon maximum demand of predicted month, specific formula for calculation is:

MD1=MD0×FA

Wherein, the business electrical moon maximum demand that MD1 is predicted month, MD0 is the business electrical moon maximum demand with reference to historical month.

Claims (1)

1. The business electrical moon maximum demand a Forecasting Methodology, it is characterized in that, concrete steps are as follows:

   1), according to the load-sensitive characteristic of business electrical, all electricity consumptions in enterprise are gone out to specific electric load and be divided into A, B, C, D totally 4 classes;

   Wherein, weather temperature sensitive kinds load is included into category-A, and sunshine, light intensity sensitive kinds load was included into category-B, and firms output sensitive kinds load is included into C class, and immobilize load and other loads are all included into D class;

   2) go out the part throttle characteristics of specific electric load according to each electricity consumption, for each electricity consumption goes out specific electric load, 4 characteristic sensitivity coefficients are respectively set, be respectively category-A load-sensitive coefficient, category-B load-sensitive coefficient, C type load sensitivity coefficient, D type load sensitivity coefficient;

   The value rule of characteristic sensitivity coefficient is as follows: establish K _{i, 1}be the category-A load-sensitive coefficient that i electricity consumption goes out specific electric load, K _{i, 2}be the category-B load-sensitive coefficient that i electricity consumption goes out specific electric load, K _{i, 3}be the C type load sensitivity coefficient that i electricity consumption goes out specific electric load, K _{i, 4}it is the D type load sensitivity coefficient that i electricity consumption goes out specific electric load;

   If it is category-A load that i electricity consumption goes out specific electric load, get K _{i, 1}=1, K _{i, 2}=K _{i, 3}=K _{i, 4}=0;

   If it is category-B load that i electricity consumption goes out specific electric load, get K _{i, 2}=1, K _{i, 1}=K _{i, 3}=K _{i, 4}=0;

   If it is C type load that i electricity consumption goes out specific electric load, get K _{i, 1}=K _{i, 2}=0.1, K _{i, 3}=1, K _{i, 4}=0;

   If it is D type load that i electricity consumption goes out specific electric load, get K _{i, 4}=1, K _{i, 1}=K _{i, 2}=K _{i, 3}=0;

   3) fundamental function that electricity consumption goes out specific electric load being set is:

   $F\left(i\right)=\sqrt{{\left(K_{i,1\×}\frac{T1}{T0}\right)}^2+{(K_{i,2}\×\frac{W1}{W0})}^2+{(K_{i,3}\×\frac{P1}{P0})}^2+K_{i,4}^2}$

   Wherein, F (i) goes out the fundamental function of specific electric load for i electricity consumption in enterprise, T1 is the moon weather prognosis average temperature value of predicted month, T0 is the monthly mean temperature value with reference to historical month, W1 is the moon weather prognosis light intensity value at sunshine of predicted month, W0 is the light intensity value at the sunshine moon with reference to historical month, the moon prediction output value that P1 is predicted month, and P0 is the moon output value with reference to historical month;

   4) choose predicted month and reference historical month, obtain moon weather prognosis average temperature value, month weather prognosis light intensity value at sunshine, month prediction output value (this value is estimated by production division of enterprise) of predicted month, and with reference to the monthly mean temperature value of historical month, month sunshine light intensity value, month output value;

   5) calculate all electricity consumptions in enterprise and go out the fundamental function of specific electric load, specific formula for calculation is:

   $\mathrm{FA}=\underset{i=1}{\overset{m}{\mathrm{\Σ}}}F\left(i\right)$

   Wherein, FA is the fundamental function that in enterprise, all electricity consumptions go out specific electric load, and m is that the electricity consumption in enterprise goes out specific electric load sum;

   6) calculate the business electrical moon maximum demand of predicted month, specific formula for calculation is:

   MD1=MD0×FA

   Wherein, the business electrical moon maximum demand that MD1 is predicted month, MD0 is the business electrical moon maximum demand with reference to historical month.

Images