CN111275267A - Power consumption prediction method for production type enterprise - Google Patents
Power consumption prediction method for production type enterprise Download PDFInfo
- Publication number
- CN111275267A CN111275267A CN202010119386.3A CN202010119386A CN111275267A CN 111275267 A CN111275267 A CN 111275267A CN 202010119386 A CN202010119386 A CN 202010119386A CN 111275267 A CN111275267 A CN 111275267A
- Authority
- CN
- China
- Prior art keywords
- enterprise
- power consumption
- order
- production type
- model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 96
- 238000000034 method Methods 0.000 claims description 34
- 230000000737 periodic effect Effects 0.000 claims description 29
- 230000005611 electricity Effects 0.000 claims description 25
- 238000009825 accumulation Methods 0.000 claims description 18
- 239000011159 matrix material Substances 0.000 claims description 8
- 230000009286 beneficial effect Effects 0.000 description 2
- 229910000831 Steel Inorganic materials 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 206010022000 influenza Diseases 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 239000010959 steel Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/067—Enterprise or organisation modelling
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Game Theory and Decision Science (AREA)
- Development Economics (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Educational Administration (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a power consumption prediction method for production type enterprises, which utilizes industry macroscopic factors and the historical power consumption data rule of the enterprises to classify the enterprises, establishes a universal power consumption prediction model suitable for each enterprise, helps the enterprises to accurately predict the future power consumption, accurately purchase the electric quantity in a real-time market, prevents deviation and avoids loss.
Description
Technical Field
The invention belongs to an enterprise power consumption prediction method, and particularly relates to a power consumption prediction method for a production type enterprise.
Background
The existing electric quantity prediction model mainly aims at the short-time electricity consumption prediction of a flexor or the whole industry, and the electric power related prediction of a single enterprise only aims at a load prediction system under an energy efficiency management system of each enterprise. With the second electricity change, the electricity market gradually develops towards the real-time market, and enterprises need to accurately estimate the electricity utilization condition of the enterprises, so that the enterprises can accurately purchase enough electricity in the real-time market, and the deviation assessment loss caused by excessive or insufficient electricity purchase is prevented.
The existing power utilization prediction model is only an integral prediction analysis aiming at areas or industries, or is predicted aiming at specific enterprises, and a universal prediction model is not available for providing respective power utilization prediction for each enterprise, so that the future power utilization of each enterprise cannot be predicted accurately, accurate power can not be purchased accurately, and deviation assessment cost is generated.
Disclosure of Invention
Aiming at the defects in the prior art, the power consumption prediction method for the production type enterprises, provided by the invention, solves the problem that the existing power consumption prediction method cannot accurately predict the power consumption according to the actual production conditions of the enterprises, so that deviation assessment cost is generated.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that: a power consumption prediction method for production type enterprises comprises the following steps:
s1, determining the type of the enterprise according to the production condition of the enterprise;
if the order is the order production type enterprise, go to step S2;
if the enterprise is a periodic production type enterprise, the process goes to step S5;
s2, determining the relation between the power consumption and the order quantity of the order production type enterprise according to the power consumption during production according to the order of the historical enterprise;
s3, predicting the order quantity according to the historical order quantity of the order production type enterprise;
s4, according to the relation between the power consumption and the order quantity of the order production type enterprise, based on the predicted order quantity, primarily predicting the power consumption;
s5, preliminarily predicting the power consumption of the periodic production type enterprise according to the historical power consumption rule of the periodic production type enterprise;
and S6, fine-tuning the preliminarily predicted enterprise electricity consumption based on the macroscopic industry factor, and realizing the prediction of the enterprise electricity consumption.
Further, in step S2, the auto-regressive integral moving average model is used to determine that the relationship between the power consumption and the order quantity of the order production type enterprise is:
y＝β0+β1*x1+β2*x2+…+βt*xt+e
in the formula, y is the power consumption of an order production type enterprise;
β0the power consumption constant of the order production type enterprise is the basic power consumption of the enterprise;
βiby a factor between the order for a product and the unit electricity consumption in the order-producing enterprise, i.e. the electricity consumed per unit productThe subscript i is 1,2,3, …, and t is the total number of each product order of the order producing enterprise;
e is the random error.
Further, the step S3 is specifically:
a1, determining original data X of order production type enterprise(0)＝{X(0)(1),X(0)(2),X(0)(3),…,X(0)(N)}；
The original data is total load data acquired at the high-voltage side of an enterprise through data acquisition equipment;
in the formula, X(0)A total load data set of an enterprise in a certain time period;
X(0)(N) is the total load data group of the enterprise at time N, and the subscript N is 1,2,3, …, N, where N is the time series, N is the maximum time series, and the maximum can be set to 96;
a2, pair X(0)(n) are all subjected to accumulation treatment to obtain X(1)：
In the formula, X(1)Accumulating the processed total load data group of the enterprise in a certain time period;
X(1)(N) is total load accumulated data of the enterprise at 1-N moments, wherein N is 1,2,3, …, N;
X(0)(k) the total load data of the enterprise at the moment k is 1,2,3, …, N;
a3 based on X(1)Constructing a gray GM (1,1) model and carrying out discretization treatment on the model;
a4, solving the discretized gray GM (1,1) model;
a5, performing model reconstruction based on the solving result of the gray GM (1,1) model, and performing inverse accumulation processing;
and A6, predicting the order quantity of the order production type enterprise according to the inverse accumulation processing result.
Further, the GM (1,1) model in step a3 is:
in the formula, a is a development coefficient and is used as a coefficient to be solved;
b is the ash action amount as the coefficient to be solved;
the GM (1,1) model after discretization is:
X(0)(k)+aZ(1)(k)＝b
in the formula, Z(1)(k) The total load data group of the enterprise accumulated after discretization in a certain time period.
Further, when the discretized GM (1,1) model is solved in step a4, the solving parameter includes Z(1)(k) A and b;
wherein Z is(1)(k) Comprises the following steps:
Z(1)(k)＝0.5*X(1)(k)+0.5*X(1)(k-1)
in the formula, X(1)(k) The total load data group of the enterprise is accumulated for a certain time period;
when a and b are solved by the least square method, a matrix p formed by a and b is as follows:
in the formula, B is an accumulation matrix;
ynis a constant vector;
superscript T is the transpose operator;
wherein,
further, in the step a5, the method for performing inverse accumulation processing specifically includes:
b1, determinationThe initial condition of the reconstructed gray GM (1,1) model is X(1)(1)＝X(0)(1)；
In the formula, X(1)(1) Reversely accumulating the data set for the total load of the enterprise at the first moment;
X(0)(1) the total load data group of the enterprise at the first moment; b2, processing the reconstructed gray GM (1,1) model by an integral factor method according to the determined initial conditions to obtain
Comprises the following steps:
in the formula,
inversely accumulating the data set estimation value for the total load of the enterprise at a certain moment;
b3, pair
Performing inverse accumulation processing to obtain the accumulated processing result
Comprises the following steps:
further, in the step a6, the order forecast amount of the order producing enterprise
Comprises the following steps:
further, in step S5, the power consumption of the periodic enterprise is preliminarily predicted through an autoregressive integral moving average model, where the preliminary power consumption prediction formula of the periodic enterprise is as follows:
in the formula,
the predicted value of the power consumption of the periodic production type enterprise;
mu is the electricity consumption reference data of the periodic production type enterprise;
φmthe index m is 1,2,3, …, p, p is the total number of production time periods of the order production type enterprise;
et-kthe subscript k is 1,2,3, …, q, and q is the total production time duration of the order production type enterprise;
θkthe value of the power consumption at the time k. Further, when the preliminary predicted power consumption of the order type enterprise and the preliminary predicted power consumption of the periodic type enterprise are determined, both the preliminary predicted power consumption of the order type enterprise and the preliminary predicted power consumption of the periodic type enterprise are determined by a least square method, and the determining method specifically comprises the following steps:
and sequentially using each historical data fitting autoregressive integral moving average model to determine the power consumption.
Further, the macroscopic industry factors in the step S6 include a market factor, an enterprise check factor, and an industry adjustment factor.
The invention has the beneficial effects that:
the invention provides a power consumption prediction method for production type enterprises, which utilizes industry macroscopic factors and the historical power consumption data rule of the enterprises to classify the enterprises, establishes a universal power consumption prediction model suitable for each enterprise, helps the enterprises to accurately predict the future power consumption, accurately purchase the electric quantity in a real-time market, prevents deviation and avoids loss.
Drawings
Fig. 1 is a flowchart of a method for predicting power consumption of a production enterprise according to the present invention.
Fig. 2 is a graph of daily electricity consumption for 30 days for an order production enterprise in the embodiment of the present invention.
FIG. 3 is a comparison graph of power consumption prediction for an order-producing manufacturing enterprise according to an embodiment of the present invention.
Fig. 4 is a graph of daily electricity consumption for 30 days for a periodic production type enterprise in the embodiment of the present invention.
FIG. 5 is a comparison graph of power consumption prediction for a periodically produced enterprise in accordance with an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
As shown in fig. 1, a method for predicting power consumption of a production enterprise includes the following steps:
s1, determining the type of the enterprise according to the production condition of the enterprise;
if the order is the order production type enterprise, go to step S2;
if the enterprise is a periodic production type enterprise, the process goes to step S5;
specifically, if the enterprise determines the production capacity by the sales volume, the enterprise is an order type enterprise, and if the enterprise determines the sales volume by the production capacity, the enterprise is a periodic type enterprise;
s2, determining the relation between the power consumption and the order quantity of the order production type enterprise according to the power consumption during production according to the order of the historical enterprise;
s3, predicting the order quantity according to the historical order quantity of the order production type enterprise;
s4, according to the relation between the power consumption and the order quantity of the order production type enterprise, based on the predicted order quantity, primarily predicting the power consumption;
s5, preliminarily predicting the power consumption of the periodic production type enterprise according to the historical power consumption rule of the periodic production type enterprise;
and S6, fine-tuning the preliminarily predicted enterprise electricity consumption based on the macroscopic industry factor, and realizing the prediction of the enterprise electricity consumption.
In step S2, the auto-regressive integral moving average model is used to determine that the relation between the power consumption and the order quantity of the order-producing enterprise is:
y＝β0+β1*x1+β2*x2+…+βt*xt+e
in the formula, y is the power consumption of an order production type enterprise;
β0the power consumption constant of the order production type enterprise is the basic power consumption of the enterprise;
βithe index i is 1,2,3, …, t, and t is the total number of the product orders of the order-producing type enterprise;
e is the random error.
The step S3 is specifically:
a1, determining original data X of order production type enterprise(0)＝{X(0)(1),X(0)(2),X(0)(3),…,X(0)(N)}；
The original data is total load data acquired at the high-voltage side of an enterprise through data acquisition equipment;
in the formula, X(0)A total load data set of an enterprise in a certain time period;
X(0)(N) is the total load data group of the enterprise at time N, and the subscript N is 1,2,3, …, N, where N is the time series, N is the maximum time series, and the maximum can be set to 96;
a2, pair X(0)(n) are all subjected to accumulation treatment to obtain X(1)：
In the formula, X(1)Accumulating the processed total load data group of the enterprise in a certain time period;
X(1)(N) is total load accumulated data of the enterprise at 1-N moments, wherein N is 1,2,3, …, N;
X(0)(k) the total load data of the enterprise at the moment k is 1,2,3, …, N;
a3 based on X(1)Constructing a gray GM (1,1) model and carrying out discretization treatment on the model;
a4, solving the discretized gray GM (1,1) model;
a5, performing model reconstruction based on the solving result of the gray GM (1,1) model, and performing inverse accumulation processing;
and A6, predicting the order quantity of the order production type enterprise according to the inverse accumulation processing result.
The GM (1,1) model in step a3 above is:
in the formula, a is a development coefficient and is used as a coefficient to be solved;
b is the ash action amount as the coefficient to be solved;
the GM (1,1) model after discretization is:
X(0)(k)+aZ(1)(k)＝b
in the formula, Z(1)(k) The total load data group of the enterprise accumulated after discretization in a certain time period.
When the discretized GM (1,1) model is solved in step a4, the solving parameter includes Z(1)(k) A and b;
wherein Z is(1)(k) Comprises the following steps:
Z(1)(k)＝0.5*X(1)(k)+0.5*X(1)(k-1)
in the formula, X(1)(k) The total load data group of the enterprise is accumulated for a certain time period;
when a and b are solved by the least square method, a matrix p formed by a and b is as follows:
in the formula, B is an accumulation matrix;
ynis a constant vector;
superscript T is the transpose operator;
wherein,
The gray model established in step a5 is:
in step a5, the method for performing inverse accumulation processing specifically includes:
b1, determining the initial condition of the reconstructed gray GM (1,1) model as X(1)(1)＝X(0)(1)；
In the formula, X(1)(1) Reversely accumulating the data set for the total load of the enterprise at the first moment;
X(0)(1) the total load data group of the enterprise at the first moment; b2, processing the reconstructed gray GM (1,1) model by an integral factor method according to the determined initial conditions to obtain
Comprises the following steps:
in the formula,
is a certain oneReversely accumulating the data set estimation value of the total load of the enterprise at the moment;
b3, pair
Performing inverse accumulation processing to obtain the accumulated processing result
Comprises the following steps:
in the step A6, the order forecast amount of the order producing enterprise
Comprises the following steps:
in step S5, the power consumption of the periodic production type enterprise is preliminarily predicted by using the autoregressive integral moving average model, and the preliminary power consumption prediction formula of the periodic production type enterprise is as follows:
in the formula,
the predicted value of the power consumption of the periodic production type enterprise;
mu is the electricity consumption reference data of the periodic production type enterprise;
φmthe index m is 1,2,3, …, p, p is the total number of production time periods of the order production type enterprise;
et-kthe subscript k is 1,2,3, …, q, and q is the total production time duration of the order production type enterprise;
θkthe value of the power consumption at the time k. It should be noted that, in the embodiment of the present invention, when determining the preliminary predicted power consumption of the order production type enterprise and the preliminary predicted power consumption of the periodic production type enterprise, both the preliminary predicted power consumption of the order production type enterprise and the preliminary predicted power consumption of the periodic production type enterprise are determined by a least square method, where the determination method specifically includes:
determining the power consumption by sequentially using each historical data fitting autoregressive integral moving average model;
specifically, a simple linear formula y ═ b is used0+b1t is for example written as follows:
wherein A is a matrix of t;
y is a matrix of Y;
solving to obtain:
in the formula,
is yiIs calculated as the arithmetic mean of the average of the values,
is tiIs calculated as the arithmetic mean of (1).
The macroscopic industry factor in the step S6 includes a market factor, an enterprise inspection factor, and an industry adjustment factor; for example, the steel industry was totally unpopular 2015 and 2016 and thus had a small market factor, i.e., the monthly power usage by a business when normally produced could be 1000 kWh, but could eventually be 300 kWh due to the small market factor. Another example is 2 months in 2018, the power consumption of pharmaceutical enterprises is larger than the normal power consumption due to the influenza epidemic, and the industry adjustment parameter is based on the ratio of the average power consumption of the whole industry to the power consumption in normal production. The specific predicted power consumption fine adjustment method comprises the following steps: and multiplying the forecast value of the normal production of the enterprise by the macroscopic industry factor.
In one embodiment of the invention, an example of predicting power usage by the method of the invention for order producing and periodic producing enterprises is provided:
FIG. 2 is a graph of daily electricity consumption for 30 days for a certain order type enterprise, the enterprise electricity consumption data is substituted into the algorithm model of the invention, the obtained short-term electricity consumption predicted value is as shown in FIG. 3, the curve in FIG. 3 shows that the predicted result basically conforms to the actual situation, the total short-term precision reaches 96.6%, and the requirement of electric power marketization transaction is met;
fig. 4 is a graph of daily electricity consumption of a production enterprise for 30 days in a certain period, electricity consumption data of the enterprise is substituted into the algorithm model, an obtained short-term electricity consumption predicted value is shown in fig. 5, the curve in fig. 5 shows that the predicted result basically conforms to the actual situation, the total accuracy in the short term reaches 94.6%, and the requirement of electric power marketing is met.
The invention has the beneficial effects that:
the invention provides a power consumption prediction method for production type enterprises, which utilizes industry macroscopic factors and the historical power consumption data rule of the enterprises to classify the enterprises, establishes a universal power consumption prediction model suitable for each enterprise, helps the enterprises to accurately predict the future power consumption, accurately purchase the electric quantity in a real-time market, prevents deviation and avoids loss.
Claims (10)
1. A power consumption prediction method for production type enterprises is characterized by comprising the following steps:
s1, determining the type of the enterprise according to the production condition of the enterprise;
if the order is the order production type enterprise, go to step S2;
if the enterprise is a periodic production type enterprise, the process goes to step S5;
s2, determining the relation between the power consumption and the order quantity of the order production type enterprise according to the power consumption during production according to the order of the historical enterprise;
s3, predicting the order quantity according to the historical order quantity of the order production type enterprise;
s4, according to the relation between the power consumption and the order quantity of the order production type enterprise, based on the predicted order quantity, primarily predicting the power consumption;
s5, preliminarily predicting the power consumption of the periodic production type enterprise according to the historical power consumption rule of the periodic production type enterprise;
and S6, fine-tuning the preliminarily predicted enterprise electricity consumption based on the macroscopic industry factor, and realizing the prediction of the enterprise electricity consumption.
2. The method for predicting power consumption of a manufacturing facility as claimed in claim 1, wherein in step S2, the auto-regressive integral moving average model is used to determine the relationship between the power consumption and the order quantity of the order manufacturing facility as follows:
y＝β0+β1*x1+β2*x2+…+βt*xt+e
in the formula, y is the power consumption of an order production type enterprise;
β0the power consumption constant of the order production type enterprise is the basic power consumption of the enterprise;
βia subscript i is 1,2,3,.. and t is the total number of the product orders of the order production type enterprises, wherein the subscript i is 1,2,3, and t is the total number of the product orders of the order production type enterprises;
e is the random error.
3. The method for predicting power consumption of a manufacturing enterprise as claimed in claim 2, wherein the step S3 is specifically as follows:
a1, determining original data X of order production type enterprise(0)＝{X(0)(1)，X(0)(2)，X(0)(3)，...，X(0)(N)}；
The original data is total load data acquired at the high-voltage side of an enterprise through data acquisition equipment;
in the formula, X(0)A total load data set of an enterprise in a certain time period;
X(0)(N) is a total load data group of an enterprise at time N, and a subscript N is 1,2, 3.
A2, pair X(0)(n) are all subjected to accumulation treatment to obtain X(1)：
In the formula, X(1)Accumulating the processed total load data group of the enterprise in a certain time period;
X(1)(N) is total load accumulated data of the enterprise at 1-N moments, wherein N is 1,2, 3.
X(0)(k) The total load data of the enterprise at the moment k is 1,2,3,. and N;
a3 based on X(1)Constructing a gray GM (1,1) model and carrying out discretization treatment on the model;
a4, solving the discretized gray GM (1,1) model;
a5, performing model reconstruction based on the solving result of the gray GM (1,1) model, and performing inverse accumulation processing;
and A6, predicting the order quantity of the order production type enterprise according to the inverse accumulation processing result.
4. The method for forecasting power consumption of a manufacturing enterprise according to claim 3, wherein the GM (1,1) model in step A3 is:
in the formula, a is a development coefficient and is used as a coefficient to be solved;
b is the ash action amount as the coefficient to be solved;
the GM (1,1) model after discretization is:
X(0)(k)+aZ(1)(k)＝b
in the formula, Z(1)(k) The total load data group of the enterprise accumulated after discretization in a certain time period.
5. The method for forecasting power consumption of a manufacturing enterprise according to claim 4, wherein when the discretized GM (1,1) model is solved in step A4, the solving parameter comprises Z(1)(k) A and b;
wherein Z is(1)(k) Comprises the following steps:
Z(1)(k)＝0.5*X(1)(k)+0.5*X(1)(k-1)
in the formula, X(1)(k) The total load data group of the enterprise is accumulated for a certain time period;
when a and b are solved by the least square method, a matrix p formed by a and b is as follows:
in the formula, B is an accumulation matrix;
ynis a constant vector;
superscript T is the transpose operator;
wherein,
6. the method for predicting power consumption of a manufacturing enterprise as claimed in claim 5, wherein the step A5 is performed by performing inverse accumulation processing specifically as follows:
b1, determining the initial condition of the reconstructed gray GM (1,1) model as X(1)(1)＝X(0)(1)；
In the formula, X(1)(1) Reversely accumulating the data set for the total load of the enterprise at the first moment;
X(0)(1) the total load data group of the enterprise at the first moment;
b2, processing the reconstructed gray GM (1,1) model by an integral factor method according to the determined initial conditions to obtain
Comprises the following steps:
in the formula,
inversely accumulating the data set estimation value for the total load of the enterprise at a certain moment;
b3, pair
Performing inverse accumulation processing to obtain the accumulated processing result
Comprises the following steps:
7. the method as claimed in claim 6, wherein in step A6, the order forecast amount for the order producer is determined
Comprises the following steps:
8. the method according to claim 2, wherein in step S5, the power consumption of the periodic enterprise is preliminarily predicted by an autoregressive integral moving average model, and the preliminary power consumption prediction formula of the periodic enterprise is:
in the formula,
the predicted value of the power consumption of the periodic production type enterprise;
mu is the electricity consumption reference data of the periodic production type enterprise;
φma subscript m is 1,2,3, and p is the total number of the production time periods of the order production type enterprises;
et-kthe subscript k is 1,2,3, and q is the total production time of the order production type enterprise;
θkthe value of the power consumption at the time k.
9. The method for predicting power consumption of a production type enterprise according to claim 8, wherein the preliminary predicted power consumption of the order production type enterprise and the preliminary predicted power consumption of the periodic production type enterprise are determined by a least square method, and the determining method specifically comprises:
and sequentially using each historical data fitting autoregressive integral moving average model to determine the power consumption.
10. The method for forecasting power consumption of a manufacturing enterprise of claim 1, wherein the macroscopic industry factors in the step S6 include a market factor, an enterprise check factor and an industry adjustment factor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010119386.3A CN111275267B (en) | 2020-02-26 | 2020-02-26 | Method for predicting electricity consumption of production type enterprises |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010119386.3A CN111275267B (en) | 2020-02-26 | 2020-02-26 | Method for predicting electricity consumption of production type enterprises |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111275267A true CN111275267A (en) | 2020-06-12 |
| CN111275267B CN111275267B (en) | 2024-04-16 |
Family
ID=71003693
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010119386.3A Active CN111275267B (en) | 2020-02-26 | 2020-02-26 | Method for predicting electricity consumption of production type enterprises |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111275267B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100292856A1 (en) * | 2009-05-15 | 2010-11-18 | Lincoln Mamoru Fujita | Method and system for managing a load demand on an electrical grid |
| CN103729689A (en) * | 2013-12-20 | 2014-04-16 | 华南理工大学 | Power grid electric quantity prediction method based on industry classifications and leading industry data |
| CN104809525A (en) * | 2015-05-08 | 2015-07-29 | 广东电网有限责任公司阳江供电局 | Electricity load prediction method and device |
| CN107292455A (en) * | 2017-07-31 | 2017-10-24 | 华自科技股份有限公司 | Electricity demand forecasting method, device, readable storage medium storing program for executing and computer equipment |
| CN107292420A (en) * | 2017-05-24 | 2017-10-24 | 国网北京市电力公司 | Data processing method and device |
-
2020
- 2020-02-26 CN CN202010119386.3A patent/CN111275267B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100292856A1 (en) * | 2009-05-15 | 2010-11-18 | Lincoln Mamoru Fujita | Method and system for managing a load demand on an electrical grid |
| CN103729689A (en) * | 2013-12-20 | 2014-04-16 | 华南理工大学 | Power grid electric quantity prediction method based on industry classifications and leading industry data |
| CN104809525A (en) * | 2015-05-08 | 2015-07-29 | 广东电网有限责任公司阳江供电局 | Electricity load prediction method and device |
| CN107292420A (en) * | 2017-05-24 | 2017-10-24 | 国网北京市电力公司 | Data processing method and device |
| CN107292455A (en) * | 2017-07-31 | 2017-10-24 | 华自科技股份有限公司 | Electricity demand forecasting method, device, readable storage medium storing program for executing and computer equipment |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111275267B (en) | 2024-04-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109687447B (en) | Electric power energy consumption prediction method and device | |
| CN105243609A (en) | User ordered power utilization decision method based on fuzzy hierarchical analysis | |
| CN107194618A (en) | Entropy weight method based weight calculation method for customer selection evaluation indexes of power selling company | |
| CN104573877A (en) | Power distribution network equipment demand prediction and quantitative method and system | |
| CN114140176B (en) | Adjustable capacity prediction method and device for load aggregation platform | |
| CN109447332A (en) | A kind of Middle-long Electric Power Load Forecast method suitable for S type load curve | |
| CN107730076A (en) | A kind of regional distribution network equipment complex utilization ratio analysis method | |
| CN115882456B (en) | Power control method and system based on large-scale power grid tide | |
| CN115496627A (en) | Method and system for evaluating response potential of adjustable resource | |
| CN109636033B (en) | Spontaneous self-use comprehensive electricity price prediction method for distributed photovoltaic project | |
| CN112330089B (en) | Comprehensive energy efficiency monitoring method and monitoring system for equipment manufacturing enterprises | |
| CN111275267A (en) | Power consumption prediction method for production type enterprise | |
| CN116703491A (en) | Power market deviation electricity pricing method considering load rate level | |
| Rochman et al. | Comparison between the backpropagation and single exponential smoothing method in sugar production forecasting case | |
| Wang et al. | Short-term electricity sales forecasting model based on wavelet decomposition and LSTM | |
| CN111047108B (en) | Electric energy duty ratio prediction method in terminal energy consumption based on optimal combination model | |
| CN114463059A (en) | thermal load-green energy transaction electricity price prediction method of t-distribution GARCH model | |
| Hansun | WEMA versus B-WEMA methods in forex forecasting | |
| CN106886851A (en) | Electric energy meter standby redundancy Forecasting Methodology based on electric energy meter repair based on condition of component | |
| CN113095687A (en) | Electric interruptible evaluation method and device for sewage treatment enterprise | |
| CN105844342A (en) | Short-term electricity market forecasting system and method | |
| CN112651651A (en) | Investment benefit evaluation method based on feature extraction and lasso regression | |
| CN115178590B (en) | Seamless steel tube production method | |
| CN115936754A (en) | Method, system, device and medium for predicting short-term power load of power market | |
| CN112037082A (en) | Method and system for calculating interruptible load electricity price in power grid peak shaving |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |