CN102880914A - Short-term load forecasting method based on human body comfortable degree index for power distribution network - Google Patents
Short-term load forecasting method based on human body comfortable degree index for power distribution network Download PDFInfo
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- CN102880914A CN102880914A CN2012102984025A CN201210298402A CN102880914A CN 102880914 A CN102880914 A CN 102880914A CN 2012102984025 A CN2012102984025 A CN 2012102984025A CN 201210298402 A CN201210298402 A CN 201210298402A CN 102880914 A CN102880914 A CN 102880914A
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
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Abstract
The invention relates to a short-term load forecasting method based on human body comfortable degree index for a power distribution network. The method is characterized in that in consideration of the characteristics of the power distribution network, the concept of human body comfortable degree index in meteorology is introduced to give a calculation method of the daily human body comfortable degree index based on the high and low temperature classification, the similarity of load curves with similar daily human body comfortable degree index is taken as the criterion, the change rate of load-human body comfortable degree index is calculated by the similarity of load shapes and daily human body comfortable degree index distance, and a daily load value to be forecasted is calculated by the change rate and a datum curve. The loads of festivals and holidays are solved by the normal day calculation method and the festival and holiday calculation average method, so that the influence on the load, caused by less festival and holiday sample quantity and sudden temperature change, is avoided. The short-term load forecasting method is based on the change of the human body comfortable degree index, avoids the influence on the predicated value from regions and seasons, has a concise and convenient algorithm, requires a small sample space, facilitates large-scale calculation and satisfies the practical requirements of short-term load forecasting of the power distribution network.
Description
Technical field
The invention belongs to network load prediction field, relate in particular to a kind of term load forecasting for distribution.
Background technology
The power distribution network short-term load forecasting is the important component part of Distribution Network Load Data prediction, is the Main Basis of formulating the distribution method of operation and realizing optimizing operation, also is the important evidence of checking distribution safety.Load prediction focuses mostly on Mid-long term load forecasting to power distribution network at present, and the research of short-term load forecasting is focused mostly in the power transmission network field, and main method is divided classic method and intelligent method.Classic method comprises time series forecasting, regression analysis, least square method, exponential smoothing etc.; Intelligent method comprises grey method, expert system approach, wavelet analysis method, fuzzy prediction method, chaology method, neural network, Combinatorial Optimization method etc.
The power distribution network short-term load forecasting is different from the major network short-term load forecasting, and himself characteristic is arranged.Its forecasting object is each 10kV feeder line power switch point, and prediction load total amount is little, and future position quantity is large, affected greatly by feeder line region within the jurisdiction load type.And the weather conditions such as temperature, precipitation, humidity, wind-force, illumination and prediction day type are also marginal on the impact of load.At present the impact of meteorologic factor is processed mainly with fixed form each influence factor is varied to the dimensionless unified value by the selection factor of influence, the method is affected greatly by artificial selection factor.Intelligentized method needs the great amount of samples space, and the training time is long, to the characteristics adaptability of distribution mass data future position and a small amount of history value a little less than.
Summary of the invention
For the problems referred to above, the present invention is in conjunction with the power distribution network characteristics, introduce the concept of Body Comfort Index in the meteorology, the meteorologic factors such as temperature, precipitation, humidity, wind-force are converted into Body Comfort Index, a kind of day Body Comfort Index computing method based on day height temperature classification have been provided, similar as benchmark take the similar load curve of day Body Comfort Index, similar and day Body Comfort Index is apart from calculated load-Body Comfort Index rate of change with the load shape, and calculates daily load value to be predicted by this rate of change and datum curve.Take to calculate and calculated the method for average festivals or holidays and ask in normal day to loading festivals or holidays.Avoided the festivals or holidays sample sizes to reach less the temperature sudden change to the impact of load.The method is succinctly convenient, and required sample space is little, satisfies the actual demand of power distribution network short-term load forecasting.
Be used for realizing that the technical scheme of above-mentioned purpose is as follows: based on the term load forecasting for distribution of Body Comfort Index, it is characterized in that adopting following steps: (1) at first will load historical data according to regular working day and grouping festivals or holidays, if to be predicted day is regular working day, the steps include:
At first, in the regular working day historical load, calculate working day with to be predicted day Body Comfort Index similarity maximum as the prediction Base day according to daily maximum temperature or the lowest temperature, relative humidity, maximum wind power, wherein, day Body Comfort Index refers to maximal value or the minimum value of per moment Body Comfort Index in a day, it is calculated by the highest temperature when daily maximum temperature surpasses certain value, calculated by the lowest temperature when same day, the lowest temperature was lower than certain value, when temperature falls between, calculated by temperature on average, main formulas for calculating is as follows:
Wherein
DI A ,
DI BBe the day Body Comfort Index of A, B,
HL ABBe the day Body Comfort Index distance of A, B,
RDI ABDay Body Comfort Index similarity for A, B.
（6）
Wherein
DIBe Body Comfort Index;
TBe temperature,
oC;
RHBe per day relative humidity,
VBe wind speed, m/s, desirable day high wind speed and day minimum wind speed mean value;
T N Be reference temperature,
oC, it slightly changes with the region difference.
The same day, maximum temperature surpassed 25
oDuring C, air conditioner load is cooling load, with in one day each constantly Body Comfort Index maximal value be described as the day Body Comfort Index of this day:
DI A = max{
DI A1, DI A2, DI A3, …,
DI A
N } （7）
Wherein,
DI ABe A days day Body Comfort Index,
DI Ai(i=1,2 ... N) be each Body Comfort Index constantly.Each is constantly approximate certain because of wind speed and humidity, therefore the Body Comfort Index under the maximum temperature is the day Body Comfort Index of this day.
When Daily minimum temperature is lower than 5
oDuring C, air conditioner load is cooling load, with in one day each constantly Body Comfort Index minimum value be described as the day Body Comfort Index of this day:
DI A = min{
DI A1, DI A2, DI A3, …,
DI A
N } （8）
Each is constantly approximate certain because of wind speed and humidity, therefore the Body Comfort Index under the minimum temperature is the day Body Comfort Index of this day.
When degree/day was between above two kinds of situations, by formula calculated (7), is averaged the day Body Comfort Index of the Body Comfort Index of temperature computation as this day.
Define Body Comfort Index difference on the two and be two days day Body Comfort Index distance, be described as:
Wherein
HL ABBe A, two days day Body Comfort Index distance of B.
Day Body Comfort Index similarity can with day Body Comfort Index apart from absolute value representation, be described as:
(2)
Wherein
RDI ABBe A, two days day Body Comfort Index similarity of B, two days day Body Comfort Index distance is no more than 50 generally speaking.
Secondly, in the regular working day historical load, search and predict the working day of Base day load shape similarity maximum, and calculate this day and the prediction Base day day Body Comfort Index distance, by this day and prediction per moment Base day load difference with its day Body Comfort Index apart from this load of two days-Body Comfort Index rate of change of calculating, wherein, the load shape similarity is by difference Expectation-based Representation for Concepts of per moment of two load curves:
x Ai,
x BiBe A, each moment value of B two load curves,
NBe sampling number,
R ABBe A, B load curve similarity,
LDI AB, iBe A, each moment load-Body Comfort Index rate of change of B load curve.
Similar two curves that refer to of load curve shape have maximum similarity by translation in vertical direction, and namely the formed envelope size of two curves is minimum, if two curves overlap, then its envelope size is 0.So the load curve shape similarity can by the Expectation-based Representation for Concepts of the different differences of constantly loading of two curves, be described as:
R AB= 1-
E (
Z AB) (10)
Z AB = |
X A -
X B | (11)
Wherein
R ABBe A, two days load curve similarity of B.
Z ABBe A, B two daily load difference curve ordered series of numbers,
X A,
X BBe A, two days load curve ordered series of numbers of B, be described as:
X A={
x A1, x A2,
x A3,…,
x A
N }
X B={
x B1, x B2,
x B3,…,
x B
N }
Z AB={
z AB,1, z AB,2,
z AB,3,…,
z AB,
N } (12)
Wherein
x A
i ,
x B
i (
i=1,2,
N) be A, B daily load curve
iThe sampled point load value,
z AB,
i (
i=1,2,
N) be A, B two daily load difference curve
iThe sampled point load value.
NBe the load curve sampling number, generally get 96.
E(
Z AB) be ordered series of numbers
Z ABExpectation, be described as:
Wherein
P(
z AB,
i ) be A, B two daily load difference curve
iThe probability of sampled point.Be described as:
P(
z AB,
i ) = 1/
N (14)
Can draw formula (3) A, B load curve similarity by (10)-(14)
R AB
At last, calculated to be predicted day and prediction Base day Body Comfort Index distance, calculate load value to be predicted to predict each moment value of Base day load and each moment load-Body Comfort Index rate of change of second step gained, be specially:
（5）
Wherein,
X MBe daily load value to be predicted,
X SBe prediction Base day load value.
(2) if be festivals or holidays in to be predicted day, the steps include:
At first, with festivals or holidays to be predicted as regular working day, in regular working day historical load packet, calculate load value festivals or holidays to be predicted according to (1) joint first to third step
X M,
Secondly, in historical load packet festivals or holidays, according to (1) joint the first to the 3rd step calculating load value festivals or holidays to be predicted
X M ',
At last, festivals or holidays to be predicted, load value was that above-mentioned two step calculated values are average, namely (
X M+
X M ')/2.
Advantage of the present invention is: the present invention introduces the concept of Body Comfort Index in the meteorology, based on day height temperature classification, provide a kind of day human comfort computing method, by day the Body Comfort Index rate of change calculate daily load to be predicted, both meteorologic factor is converted to unified dimension numerical value, and had avoided again region, season difference on the impact of predicted value.Festivals or holidays loads is taked to calculate and calculated the method for average festivals or holidays and ask in normal day, avoided the festivals or holidays sample sizes to reach less the temperature sudden change to the impact of load.Required sample size is few, and algorithm is succinctly convenient, and the program that is beneficial to realizes and large-scale calculations.
Embodiment
Utilize method proposed by the invention to carry out the detailed process of load prediction as follows:
Embodiment one
Table 1 is somewhere in June, 2012 part Weather information, and June 2, June 3, June 9 are two-day weekend, and other are regular working day, and the daily load that will work is put into formation
Q 1, festivals or holidays, load was put into formation
Q 2,The load sampled value is 96 points.
June 8 was to be predicted day, and be regular working day to be predicted day June 8, at first according to distribution short-term load forecasting calculation procedure,
Search and its day maximum day of Body Comfort Index similarity by formula (1) and (2), be June 7.
Secondly, in formation
Q 1In search and predict the working day of Base day load shape similarity maximum, and calculate this day and the day Body Comfort Index distance of predicting the Base day.
Search and maximum day of daily load shape similarity in June 7 according to formula (3), be June 6.
At last, calculate load between 6 days and 7 days-Body Comfort Index rate of change according to formula (4).Take 7 daily loads as benchmark, with 6 days with 7 days load-Body Comfort Index rate of change approximate replace 8 days with 7 days Body Comfort Index, according to formula (5) calculating 96 point load values on the 8th.
Predict the outcome as shown in table 2 with the actual value error statistics.Average error rate is 7.235%; The least error rate is 0.015%, and going out now is the 93rd point; Maximum error rate is 42.936%, and going out now is the 48th point.
Embodiment two
Table 1 is somewhere in June, 2012 part Weather information, and June 9 was to be predicted day, and June 2, June 3, June 9 are two-day weekend, and other are regular working day, and the daily load that will work is put into formation
Q 1, festivals or holidays, load was put into formation
Q 2, the load sampled value is 96 points.
Be two-day weekend to be predicted day June 9, and concrete steps are:
1, at first, according to distribution short-term load forecasting calculation procedure,
（1）
Search and Body Comfort Index similarity maximum functional day its day by formula (1) and (2), be June 6.
Secondly, in formation
Q 1In search and predict the working day of Base day load shape similarity maximum, and calculate this day and the day Body Comfort Index distance of predicting the Base day.
Secondly, searching and maximum day of 6 daily load shape similarities according to formula (3), is 7.
Again, calculate load between 6 days and 7 days-Body Comfort Index rate of change, and calculate 96 point load values on the 9th according to formula (5)
X 6
2, in formation
Q 2In, search and festivals or holidays of Body Comfort Index similarity maximum everyday in June 9, be June 3.Search and maximum day of 3 daily load shape similarities, be June 2.Calculate load between 2 days and 3 days-Body Comfort Index rate of change.
3 and calculate 96 point load values on the 9th according to formula (5)
X ' 6, then 9 daily load prediction values be (
X 6+
X ' 6)/2.
It predicts the outcome as shown in table 2 with the actual value error statistics.Average error rate is 7.798%; The least error rate is 0.03547, and going out now is the 29th point; Maximum error rate is 48.4906%, and going out now is the 78th point.
Table
1
| Date | Maximum temperature ( oC) | Minimum temperature ( oC) | Humidity (%) | Wind-force (m/s) |
| June 1 | 26.5 | 17 | 65 | 4 |
| June 2 | 29.7 | 17.3 | 65 | 4 |
| June 3 | 30.1 | 18.3 | 54 | 3 |
| June 4 | 32.2 | 19.3 | 58 | 4 |
| June 5 | 35 | 18.6 | 44 | 4 |
| June 6 | 32 | 22.4 | 51 | 4 |
| June 7 | 29.8 | 19.8 | 63 | 3 |
| June 8 | 29.6 | 19.8 | 69 | 3 |
| June 9 | 35.2 | 17.7 | 58 | 4 |
Table
2
| Date | Maximum error rate (%) | Least error rate (%) | Average error rate (%) |
| June 8 | 42.936 | 0.015 | 7.235 |
| June 9 | 48.4906 | 0.03547 | 7.798 |
| Date | Maximum error (MW) | Least error (MW) | Average error (MW) |
| June 8 | 0.56126 | 0.000199 | 0.094582 |
| June 9 | 0.665675 | 0.000487 | 0.107051 |
| Date | The maximum error moment point | The least error moment point | |
| June 8 | 48 | 93 | |
| June 9 | 78 | 29 |
Claims (1)
1. based on the term load forecasting for distribution of Body Comfort Index, it is characterized in that adopting following steps: (1) at first will load historical data according to regular working day and grouping festivals or holidays, if be regular working day in to be predicted day, the steps include:
At first, in the regular working day historical load, calculate working day with to be predicted day Body Comfort Index similarity maximum as the prediction Base day according to daily maximum temperature or the lowest temperature, relative humidity, maximum wind power, wherein, day Body Comfort Index refers to maximal value or the minimum value of per moment Body Comfort Index in a day, it is calculated by the highest temperature when daily maximum temperature surpasses certain value, calculated by the lowest temperature when same day, the lowest temperature was lower than certain value, when temperature falls between, calculated by temperature on average, main formulas for calculating is as follows:
（2）
Wherein
DI A ,
DI BBe the day Body Comfort Index of A days, B days,
HL ABBe the day Body Comfort Index distance of A, B,
RDI ABDay Body Comfort Index similarity for A, B;
Secondly, in the regular working day historical load, search and predict the working day of Base day load shape similarity maximum, and calculate this day and the prediction Base day day Body Comfort Index distance, by this day and prediction per moment Base day load difference with its day Body Comfort Index apart from this load of two days-Body Comfort Index rate of change of calculating, wherein, the load shape similarity is by difference Expectation-based Representation for Concepts of per moment of two load curves:
Wherein
x Ai,
x BiBe A, each moment value of B two load curves,
NBe sampling number,
R ABBe A, B load curve similarity,
LDI AB, iBe A, each moment load-Body Comfort Index rate of change of B load curve;
At last, calculated to be predicted day and prediction Base day Body Comfort Index distance, calculate load value to be predicted to predict each moment value of Base day load and each moment load-Body Comfort Index rate of change of second step gained, be specially:
（5）
Wherein,
X MBe daily load value to be predicted,
X SBe prediction Base day load value;
(2) if to be predicted day be festivals or holidays, the steps include: at first, with festivals or holidays to be predicted as regular working day, in regular working day historical load packet, save first according to (1) and calculate load value festivals or holidays to be predicted to third step
X M
Secondly, in historical load packet festivals or holidays, according to (1) joint the first to the 3rd step calculating load value festivals or holidays to be predicted
X M '
At last, festivals or holidays to be predicted, load value was that above-mentioned two step calculated values are average, namely (
X M+
X M ')/2.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103606015A (en) * | 2013-11-26 | 2014-02-26 | 国网安徽省电力公司 | Short-term load forecasting method based on hourly comprehensive meteorological indexes |
| CN106018682A (en) * | 2016-05-10 | 2016-10-12 | 深圳星普森信息技术有限公司 | Method for computing and grading indoor air quality index |
| CN108960657A (en) * | 2018-07-13 | 2018-12-07 | 国网上海市电力公司 | One kind being based on the preferred building Load Characteristic Analysis method of feature |
| CN110688622A (en) * | 2019-09-17 | 2020-01-14 | 国网江苏省电力有限公司徐州供电分公司 | Power load prediction method based on human body comfort |
| CN112686449A (en) * | 2020-12-31 | 2021-04-20 | 国网河南省电力公司经济技术研究院 | Residential electricity consumption prediction method based on human body weather comfort index |
| CN113011045A (en) * | 2021-04-13 | 2021-06-22 | 河海大学 | Method for acquiring air-conditioning power in electrical load in summer and computer readable medium |
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| CN102819770A (en) * | 2012-08-02 | 2012-12-12 | 积成电子股份有限公司 | Method for forecasting short-term load of distribution network based on human body comfort index |
-
2012
- 2012-08-21 CN CN2012102984025A patent/CN102880914A/en not_active Withdrawn
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102819770A (en) * | 2012-08-02 | 2012-12-12 | 积成电子股份有限公司 | Method for forecasting short-term load of distribution network based on human body comfort index |
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| CN103606015A (en) * | 2013-11-26 | 2014-02-26 | 国网安徽省电力公司 | Short-term load forecasting method based on hourly comprehensive meteorological indexes |
| CN106018682A (en) * | 2016-05-10 | 2016-10-12 | 深圳星普森信息技术有限公司 | Method for computing and grading indoor air quality index |
| CN106018682B (en) * | 2016-05-10 | 2018-01-26 | 深圳星普森信息技术有限公司 | A kind of calculating of IAQ index and stage division |
| CN108960657A (en) * | 2018-07-13 | 2018-12-07 | 国网上海市电力公司 | One kind being based on the preferred building Load Characteristic Analysis method of feature |
| CN108960657B (en) * | 2018-07-13 | 2021-11-12 | 国网上海市电力公司 | Building load characteristic analysis method based on feature optimization |
| CN110688622A (en) * | 2019-09-17 | 2020-01-14 | 国网江苏省电力有限公司徐州供电分公司 | Power load prediction method based on human body comfort |
| CN112686449A (en) * | 2020-12-31 | 2021-04-20 | 国网河南省电力公司经济技术研究院 | Residential electricity consumption prediction method based on human body weather comfort index |
| CN113011045A (en) * | 2021-04-13 | 2021-06-22 | 河海大学 | Method for acquiring air-conditioning power in electrical load in summer and computer readable medium |
| CN113011045B (en) * | 2021-04-13 | 2022-09-30 | 河海大学 | Method for acquiring air-conditioning power in electrical load in summer and computer readable medium |
| CN114159065A (en) * | 2022-02-14 | 2022-03-11 | 深圳市心流科技有限公司 | Method and device for evaluating intermittent meditation training based on electroencephalogram signals |
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Application publication date: 20130116 |