CN103559655A - Microgrid novel feeder load prediction method based on data mining - Google Patents
Microgrid novel feeder load prediction method based on data mining Download PDFInfo
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Abstract
The invention provides a microgrid novel feeder load prediction method based on data mining, and relates to a microgrid feeder load prediction method. Microgrid novel feeder load serves as an object, and various types of feeder load features are analyzed on a microgrid by the adoption of a data mining technology; according to weather data, various feeder load modes are built by the adoption of clustering analysis; an optimization method is selected to determine various feeder load prediction modes for solution algorithm design. The microgrid novel feeder load can change the situation that the load receives system electricity passively, and can actively coordinate with a main network and plays an active role in demand response. Microgrid novel feeder load prediction can predict total load amount of a microgrid and can also predict power of microgrid distribution type power generation including wind power, photovoltaic generation and energy storage, a prediction model is preferred, prediction errors are corrected, and prediction precision of various feeder loads can be improved.
Description
Technical field
The present invention relates to a kind of Forecasting Methodology of microgrid feeder line load, be specifically related to the Forecasting Methodology of the novel feeder line load of a kind of microgrid based on data mining.
Background technology
The novel feeder line load of microgrid consists of traditional feeder line load (positive carry), photovoltaic and wind-power electricity generation distributed regenerative resource (negative load) and energy storing device.The load prediction of novel microgrid feeder line, should consider the tendency that traditional feeder line load curve peak valley rises and falls, and also will pay close attention to mutual relationship and operational mode between novel microgrid feeder line load.The current domestic report that not yet has correlation technique.
Summary of the invention
The Forecasting Methodology that the object of this invention is to provide the novel feeder line load of a kind of microgrid based on data mining, the novel feeder line load of the microgrid of take is object, application data digging technology is analyzed all kinds of feeder line load characteristics on microgrid; According to weather data, adopt cluster analysis to set up all kinds of feeder line load patterns; Choose optimization method, as methods such as robust regression, BP neural network, ARMA time prediction models, determine all kinds of feeder line load forecasting model, carry out derivation algorithm design.
The Forecasting Methodology of the novel microgrid feeder line load based on data mining of the present invention, comprises following content:
(1) to the raw data analysis of the undressed all kinds of feeder line loads from electrical network collection in worksite, design entity-contact figure;
(2) according to feeder line load and weather data structure, set up data warehouse, to giving birth to data, extract, change, be loaded in the feeder line load data warehouse of subject-oriented after cleaning, integrated processing;
(3) adopt fuzzy clustering, all kinds of feeder lines in data warehouse are loaded by setting up working day and festivals or holidays all kinds of novel feeder line load patterns;
(4) according to all kinds of novel feeder line load operation rules, in method base, choose optimization method and determine novel feeder line load forecasting model, by historical data sample, train and learn, calculate or estimation prediction model parameters;
(5) test that actual observation sample predicts the outcome, statistical study predicated error, revises forecast model.
Tool of the present invention has the following advantages:
1, the novel feeder line of microgrid load can change the electric energy passive situation of accepting merely system, can be on one's own initiative and major network cooperation, at demand response, play a positive role;
2, the magnanimity feeder line load data of electrical network is analyzed, discarded the dross and selected the essential, after data pre-service, store in the data warehouse of subject-oriented;
3, can obtain the relevant decision informations such as the novel all kinds of feeder line Load Characteristic Analysis of microgrid, all kinds of novel feeder line load prediction trend;
4, the novel feeder line load prediction of microgrid can not only be predicted the load total amount of microgrid, can also predict microgrid distributed power generation (wind-powered electricity generation, photovoltaic generation, energy storage) power;
5, preferred forecast model, revises predicated error, can improve all kinds of feeder line load prediction precision.
Accompanying drawing explanation
The E-R figure of Fig. 1 for being the theme based on novel feeder line load;
Fig. 2 is all kinds of novel feeder line load prediction process flow diagram based on data mining.
Embodiment
Below in conjunction with accompanying drawing, technical scheme of the present invention is further described; but be not limited to this; every technical solution of the present invention is modified or is equal to replacement, and not departing from the spirit and scope of technical solution of the present invention, all should be encompassed in protection scope of the present invention.
The Forecasting Methodology of the novel microgrid feeder line load based on data mining of the present invention, comprises following content:
(1) to the raw data analysis of the undressed all kinds of feeder line loads from electrical network collection in worksite, design entity-contact figure;
(2) according to feeder line load and weather data structure, set up data warehouse, to giving birth to data, extract, change, be loaded in the feeder line load data warehouse of subject-oriented after cleaning, integrated processing;
(3) adopt fuzzy clustering, all kinds of feeder lines in data warehouse are loaded by setting up working day and festivals or holidays all kinds of novel feeder line load patterns;
(4) according to all kinds of novel feeder line load operation rules, in method base, choose optimization method and determine novel feeder line load forecasting model, by historical data sample, train and learn, calculate or estimation prediction model parameters;
(5) test that actual observation sample predicts the outcome, statistical study predicated error, revises forecast model.
In foregoing, specific requirement is as follows:
1, database entity relation design
Data analysis is whole database design, sets up the first step in ground process, is also an of paramount importance step simultaneously.The data analysis of load database will be determined to the various data types of required use in novel feeder line load prediction process.
The historical data that in forecasting process, in the middle of the various data types of required use, most importantly all kinds of novel feeder lines of distribution are loaded.Can be divided into two classes: (1) positive carry: i.e. the sub-load identical with conventional energy flow direction; (2) load: with the sub-load of energy flow opposite direction in traditional distribution feeder, this sub-load is provided by near wind-force user's side, photovoltaic, the generating of energy storage device small distributed power supply.
In order to determine the corresponding relation between all kinds of solid data attributes and data, the present invention to the raw data analysis of the undressed all kinds of feeder lines load from electrical network collection in worksite, designed Entity-Relationship figure (Entity Relationship Diagram, E-R figure), as shown in Figure 1.
2, Data Warehouse Design
Multidimensional structure in database is divided into two class tables: a class is fact table, is used for storing true metric and the code value of each dimension; Another kind of is dimension table.According to feeder line load and weather data structure, set up and take the data warehouse of feeder line historical load data as fact table and some dimension tables compositions of forming with factors such as date, time, weather, area, microgrids.Data warehouse is the particular database of subject-oriented, is data platform important in data mining technology, and it can reach efficient data query and retrieval.It is generally comprised of fact table (theme) and several dimension tables being associated (time, position, weather parameters etc.).Often there are snowflake type, the model such as star-like.The Data Warehouse Design of Novel distribution network feeder line load, in order to improve retrieval effectiveness, adopts snowflake type model.The corresponding specific object of respectively showing of this data warehouse designs as follows:
(1) feeder line information on load is fact table, comprises time numbering, Position Number, direction numbering, feeder line historical load active power, feeder line historical load reactive power.
(2) date and time information dimension table, comprises: date numbering, year, month, day, week, season and solar term that the date is corresponding.
(3) temporal information dimension table, comprises: time numbering, date numbering, hour, minute information;
(4) regional information dimension table, comprises: area number, regional title;
(5) microgrid Information Dimension kilsyth basalt, comprises: area number, microgrid numbering, microgrid title;
(6) feeder line positional information dimension table, comprises: Position Number, area number, microgrid numbering, feeder line title;
(7) load type Information Dimension kilsyth basalt, comprises: load type numbering, type direction, typonym.
(8) Weather information dimension table, comprises: date numbering, maximum temperature, minimum temperature, weather condition, humidity, air pressure.
The concrete structure design of each table is as shown in table 1-8:
Table 1 feeder line load fact table
| Field name | Type | Length | Attribute implication |
| Time_ID | Numeral (Int) | 4 | Time numbering |
| Date_ID | Numeral (Int) | 4 | Date numbering |
| Location_ID | Numeral (Int) | 4 | Position Number |
| Type_ID | Numeral (Int) | 4 | Load type numbering |
| LoadP | Numeral (Double) | 8 | Feeder line load active power |
| LoadQ | Numeral (Double) | 8 | Feeder line reactive load power |
Table 2 date and time information dimension table
Table 3 temporal information dimension table
Table 4 regional information dimension table
| Field name | Type | Length | Attribute implication |
| Region_ID | Numeral (Int) | 4 | Area ID |
| Region | Text | 20 | Netherlands |
Table 5 microgrid Information Dimension kilsyth basalt
Table 6 feeder line positional information dimension table
Table 7 load type Information Dimension kilsyth basalt
Table 8 Weather information dimension table
3, data pre-service
By FTU(line feed terminals unit), RTU(far-end measurement and control unit) electric network data of device and intelligent electric meter collection sends the host computer system of dispatching center to, these data are undressed raw data, they originate different, structure is chaotic, some data is imperfect, also often there is bad data or network topology mistake, therefore need to re-start extraction to these data, cleaning, pre-service such as integrated grade, and use the identification of state estimation method and revise them, again according to the demand of the novel feeder line load prediction of microgrid, by data-switching by different data according to certain rule, proofread and correct, sequence, polymerization, comprehensively, after concentrating, according to Based Data Warehouse System, be loaded in data warehouse, thereby guarantee integrality and the reliability of data, reach the object that makes full use of various data sources.
, set up feeder line load pattern
By the integrated data of solar term factor and climatic factor is classified, set up feeder line load pattern.
If cluster sample weather day, positive carry data type vector was:
Wherein
;
it is the number of days of or two solar term (containing working day or festivals or holidays);
be
the day maximum temperature of day;
be
the Daily minimum temperature of day;
be
the weather of day is situation rain or shine;
be
the relative humidity of day;
be
the day type (working day or festivals or holidays) of day;
be
the solar term of day.
If the negative load data type vector of cluster sample weather day photovoltaic is:
Wherein
;
it is the number of days of or two solar term (containing working day or festivals or holidays);
,
,
be respectively
intensity of solar radiation maximal value, mean value, the minimum value of day;
,
,
be respectively
maximum temperature, medial temperature, the minimum temperature of day.
Weather integrated data is standardized, by attribute data bi-directional scaling, make it to fall into specific [0,1] interval.By minimum-maximum specification, raw data is carried out to linear transformation.Standardization integrated data can calculate in the following manner:
If the cluster data type vector after standardization is:
Wherein
,
be respectively data
minimum and maximal value,
k=1,2 ...,
p;
l=1,2 ..., 6,
s=1,2.
Use C-mean fuzzy clustering, the feeder line load sample of prediction closer to each other day can be classified as to a class, be designated as respectively
x 1(
t),
x 2(
t) ...,
x n (
t), and form thus a feeder line load pattern
m , that is:
M =(
X 1(
t),
X 2(
t), … ,
X n(
t)) (5) 。Wherein n is the number of feeder line load curve in pattern,
tit is the time that feeder line load sample is corresponding.
5, forecast model is chosen
According to all kinds of feeder line load operation rules, set up Forecasting Methodology storehouse (model such as regretional analysis, neural network prediction, ARMA time prediction model), from method base, choose suitable method and determine feeder line load forecasting model, design derivation algorithm, and look-ahead feeder line information on load, to meet the demand of decision-makings at different levels.
6,
forecast model derivation algorithm
All kinds of novel feeder line Short Term Load algorithm flow charts as shown in Figure 2.Take 1 hour as each interval period of feeder line load curve.All kinds of novel feeder line Short Term Load algorithm steps are as follows:
Step 1: the year, month, day of selecting prediction; Select area, microgrid, the feeder line of prediction.
Step 2: according to solar term and climatic factor, select integrated data type (1) formula.
Step 3: by cluster sample data type
by (4) formula minimax standardization data type
.
Step 4: use C-mean fuzzy clustering, choose and predict that day is close
nindividual novel feeder line load curve
x 1(
t),
x 2(
t) ...,
x n (
t) as the sample that belongs to identical feeder load pattern (5) formula.
Step 5: determine forecast model output vector
y and input matrix
x .
y =
x n (
t) be at novel feeder line load pattern
m in from day nearest sample curve of prediction,
x=(
x 1(
t),
x 2(
t) ...,
x n-1
(
t)) be same
m in other sample curve.
Step 6: according to all kinds of feeder line load characteristics, choose forecast model (robust regression model, L-M neural network prediction model, ARMA time prediction model etc.)
y =
cX , wherein
c =(c
1 , c
2 , .,
c n-1
).
Step 7: the weight coefficient vector that calculates forecast model
c .The error of calculation
e, that is:
if,
, perform step 8, otherwise, weight coefficient vector again revised
c .Wherein
εit is precision of prediction.
Step 8: export novel feeder line load power curve
y (
t).
Claims (6)
1. the Forecasting Methodology that the novel feeder line of the microgrid based on data mining is loaded, is characterized in that Forecasting Methodology is:
(1) to the raw data analysis of the undressed all kinds of feeder line loads from electrical network collection in worksite, design entity-contact figure;
(2) according to feeder line load and weather data structure, set up data warehouse, to giving birth to data, extract, change, be loaded in the novel feeder line load data warehouse of subject-oriented after cleaning, integrated processing;
(3) adopt fuzzy clustering, all kinds of feeder lines in data warehouse are loaded by setting up working day and festivals or holidays all kinds of feeder line load patterns;
(4) according to all kinds of novel feeder line load operation rules, in method base, choose optimization method and determine novel feeder line load forecasting model, by historical data sample, train and learn, calculate or estimation prediction model parameters;
(5) test that actual observation sample predicts the outcome, statistical study predicated error, revises forecast model.
2. the Forecasting Methodology of the novel feeder line of the microgrid based on data mining according to claim 1 load, is characterized in that described data warehouse forms by take some dimension tables that feeder line historical load data form as fact table with date, time, weather, area, microgrid.
3. the Forecasting Methodology of the novel feeder line load of the microgrid based on data mining according to claim 1, is characterized in that described data warehouse is snowflake type.
4. the Forecasting Methodology of the novel feeder line load of the microgrid based on data mining according to claim 1, is characterized in that in described step 3, and by the integrated data of solar term factor and climatic factor is classified, set up feeder line load pattern, concrete steps are as follows:
1) establishing cluster sample weather day positive carry data type vector is:
2) establishing the negative load data type vector of cluster sample weather day photovoltaic is:
3) weather integrated data is standardized, by attribute data bi-directional scaling, make it to fall into specific [0,1] interval;
4) use C-mean fuzzy clustering, the feeder line load sample of prediction closer to each other day is classified as to a class, be designated as respectively
x 1(
t),
x 2(
t) ...,
x n (
t), and form thus a feeder line load pattern
m , that is:
M =(
X 1(
t),
X 2(
t), … ,
X n(
t)) ；
Wherein
;
it is the number of days of one or two solar term;
be
the day maximum temperature of day;
be
the Daily minimum temperature of day;
be
the weather of day is situation rain or shine;
be
the relative humidity of day;
be
the day type of day;
be
the solar term of day;
,
,
be respectively
intensity of solar radiation maximal value, mean value, the minimum value of day;
,
,
be respectively
maximum temperature, medial temperature, the minimum temperature of day; N is the number of feeder line load curve in pattern,
tit is the time that feeder line load sample is corresponding.
5. the Forecasting Methodology of the novel feeder line load of the microgrid based on data mining according to claim 4, is characterized in that in described step 3), and standardization integrated data calculates in the following manner:
If the cluster data type vector after standardization is:
Wherein
,
be respectively data
minimum and maximal value,
k=1,2 ...,
p;
l=1,2 ..., 6,
s=1,2.
6. the Forecasting Methodology that the novel feeder line of the microgrid based on data mining according to claim 1 is loaded, is characterized in that the concrete steps of described Forecasting Methodology are as follows:
Step 1: the year, month, day of selecting prediction; Select area, microgrid, the feeder line of prediction;
Step 2: according to solar term and climatic factor, select according to the following formula integrated data type:
; Wherein
;
it is the number of days of or two solar term (containing working day or festivals or holidays);
be
the day maximum temperature of day;
be
the Daily minimum temperature of day;
be
the weather of day is situation rain or shine;
be
the relative humidity of day;
be
the day type (working day or festivals or holidays) of day;
be
the solar term of day;
Step 3: by cluster sample data type
by following formula minimax standardization data type
:
; Wherein
,
be respectively data
minimum and maximal value,
k=1,2 ...,
p;
l=1,2 ..., 6,
s=1,2;
Step 4: use C-mean fuzzy clustering, choose and predict that day is close
nindividual novel feeder line load curve
x 1(
t),
x 2(
t) ...,
x n (
t) as the sample that belongs to identical feeder load pattern, wherein feeder line load pattern
m =(
x 1(
t),
x 2(
t) ...,
x n(
t));
Step 5: determine forecast model output vector
y and input matrix
x ,
y =
x n (
t) be at novel feeder line load pattern
m in from day nearest sample curve of prediction,
x=(
x 1(
t),
x 2(
t) ...,
x n-1
(
t)) be same
m in other sample curve;
Step 6: according to all kinds of feeder line load characteristics, choose forecast model
y =
cX , wherein
c =(c
1 , c
2 , .,
c n-1
);
Step 7: the weight coefficient vector that calculates forecast model
c , the error of calculation,
if,
, perform step 8, otherwise, weight coefficient vector again revised
c ;
Step 8: export novel feeder line load power curve
y (
t).
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