CN116432874A - Distributed photovoltaic power prediction method based on characteristic power - Google Patents

Distributed photovoltaic power prediction method based on characteristic power Download PDF

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CN116432874A
CN116432874A CN202310700554.1A CN202310700554A CN116432874A CN 116432874 A CN116432874 A CN 116432874A CN 202310700554 A CN202310700554 A CN 202310700554A CN 116432874 A CN116432874 A CN 116432874A
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范建华
曹乾磊
杨圣昆
梁浩
王磊
黄晓娅
于雅洁
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Qingdao Dingxin Communication Power Engineering Co ltd
Qingdao Topscomm Communication Co Ltd
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Abstract

The invention relates to the field of photovoltaic power generation power prediction, and discloses a distributed photovoltaic power prediction method based on characteristic power, which comprises the following steps: collecting historical power generation data, power station capacity, meteorological data and time data of each distributed power station in a set area; carrying out normalization processing on the collected historical data of each photovoltaic power station, and then clustering according to the days; processing the clustered power data to obtain the characteristic power of each photovoltaic power station; training a CNN neural network prediction model; s5: and updating the characteristic power of the power station according to a certain frequency, inputting the characteristic power, weather forecast data and time data into a prediction model, and predicting the photovoltaic power. Comprehensively, the invention realizes that the power generation power of each photovoltaic power station is accurately predicted under the condition that part of distributed photovoltaic power stations have no historical power data, thereby ensuring the safe operation of the power grid to a certain extent.

Description

Distributed photovoltaic power prediction method based on characteristic power
Technical Field
The invention relates to the field of photovoltaic power generation power prediction, in particular to a distributed photovoltaic power prediction method based on characteristic power.
Background
Photovoltaic power generation is a technology that converts solar energy into electrical energy. Centralized photovoltaic is a relatively mature power generation mode at present, generally has perfect power and weather monitoring devices, can store a large amount of historical data, and can establish a special prediction model for a centralized photovoltaic power station to predict photovoltaic power. The distributed photovoltaic starting is relatively late, and has the following characteristics: firstly, the investment cost of a single power station is low, a meteorological monitoring device is not generally provided, and the inclination angle, the phase angle, the cleanliness degree and the like of different photovoltaic power stations are different; secondly, the installation time of the distributed photovoltaic is uneven, and part of the photovoltaic has no historical power data. Thus, distributed photovoltaics are difficult to achieve one-station-one prediction like centralized photovoltaics.
With the increasing number of distributed power stations, the randomness and fluctuation of the output of the distributed power stations cause pressure on the safe operation of a power grid, so that the accurate prediction of the generated power of the distributed photovoltaic power stations in a certain area is more and more important. At present, a method for accurately predicting the power generated by each photovoltaic power station under the condition that part of distributed photovoltaic power stations have no historical power data is still lacking in the industry.
Disclosure of Invention
Aiming at the defects and drawbacks existing in the prior art, the invention provides a distributed photovoltaic power prediction method based on characteristic power, which uses the characteristic power to represent the characteristics of a photovoltaic power station in a fuzzy manner and establishes a unified prediction model for the photovoltaic in a set area; for a distributed photovoltaic power station without historical data, the characteristic power can be obtained through several days of power, and a unified prediction model is used for more accurate prediction.
The object of the invention can be achieved by the following technical scheme.
A distributed photovoltaic power prediction method based on characteristic power comprises the following steps.
S1: collecting historical power generation power data and power station capacity of each distributed photovoltaic power station in a set area; meteorological data and time data of a set area are collected.
S2: carrying out normalization processing on each historical power generation power data based on the capacity of each power station acquired by the S1, and clustering according to days;
s3: and processing the clustered data to obtain the characteristic power of each distributed photovoltaic power station.
S4: and taking meteorological data, time data and characteristic power as input, taking normalized historical power generation power data as output, and training by using a CNN neural network to obtain a photovoltaic power unified prediction model of a set area.
S5: and updating the characteristic power of the distributed photovoltaic power station according to a certain frequency, inputting the updated characteristic power, meteorological data and time data into the photovoltaic power unified prediction model obtained in the step S4, and outputting a photovoltaic power prediction result.
Preferably, in the step S1, the set area is a county or a district; the meteorological data comprise ambient temperature, relative humidity, wind speed and air pressure; the time data comprises month, day, time and minute.
Preferably, the clustering method in S2 is k-means clustering.
And intercepting part of data from the normalized daily historical power generation data, and extracting the sum, the discrete difference and the third derivative average value of the power curve from the intercepted data as clustering features.
Preferably, the clustering in S3 is k-means clustering.
And processing the class data with highest sum of power in the k class data by using an exponential weighted average method to obtain the characteristic power of each distributed photovoltaic power station at different time.
The formula of the exponential weighted average method:
Figure SMS_1
。
in the method, in the process of the invention,
Figure SMS_2
is the characteristic power after smoothing at time point i, < >>
Figure SMS_3
Is the historical actual power at time point i, α is the memory decay factor, +.>
Figure SMS_4
Is the characteristic power smoothed at the time point i-1.
Preferably, in S4, the meteorological data, the time data, and the characteristic power of the same day are taken as one input sample, and different characteristics are taken as different channels of the input.
Preferably, the method for updating the characteristic power in S5 is as follows: and (3) classifying the daily power generation power curve by using the clustering center obtained in the step (S2), and then executing the step (S3) to obtain updated characteristic power.
The beneficial technical effects of the invention are as follows: the method comprises the steps of clustering and processing historical power data of photovoltaic power stations to obtain continuously-changed characteristic power of each power station, taking the continuously-changed characteristic power, meteorological data and time data as input, and training a unified prediction model of a distributed photovoltaic power station in a set area; for a distributed photovoltaic power station without historical data, the characteristic power can be obtained through several days of power, and a unified prediction model is used for more accurate prediction. Comprehensively, the invention realizes that the power generation power of each photovoltaic power station is accurately predicted under the condition that part of distributed photovoltaic power stations have no historical power data, thereby ensuring the safe operation of the power grid to a certain extent.
Drawings
Fig. 1 is a general flow chart of the present invention.
FIG. 2 is a schematic diagram of the clustering result of the power curve according to the present invention.
Figure 3 shows the characteristic power of the multi-station calculated on a certain day according to the invention.
FIG. 4 shows the prediction results of the generated power of the multiple power stations according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Examples: as shown in fig. 1, a distributed photovoltaic power prediction method based on characteristic power includes the following steps.
S1: historical power generation data and power station capacity of each distributed photovoltaic power station in a set area are collected, and meteorological data and time data of the set area are collected.
The set area is an area with the weather condition close to that of the county or the district, and the acquired weather data comprise the ambient temperature, the relative humidity, the wind speed and the air pressure; the time data comprises month, day, time and minute. In this embodiment, the plant history generated power data used is from several distributed photovoltaic plants in the urban and sunny region of the Qingdao city.
S2: after normalization processing is carried out on each historical power generation power data based on the capacity of each power station acquired by the S1, the normalized daily historical power generation power data is divided by using a k-means clustering method, and k-class power data are obtained.
When k-means clustering is carried out, part of power data is intercepted in normalized daily historical power generation power data, and features such as the sum of power, discrete difference, power curve third-order derivative average value and the like are extracted and clustered to be used as clustering features. In this embodiment, the data from 6:00 to 18:00 are intercepted as input data, and the clustering result is shown in fig. 2, and it can be seen that the power curve of the photovoltaic power station is classified into 4 types according to this embodiment, wherein the second type of curve has relatively high power and stable variation, and accords with the generating power curve in sunny days.
S3: and (3) using the power data after clustering, which is the highest sum of the power in the 4 types of data, as the power generated on sunny days, and processing the power data by using an exponential weighted average method to obtain the characteristic power of the photovoltaic power station at different times.
Firstly, averaging normalized power of each distributed power station in the area in a sunny day for a period of time to obtain average power, wherein the average power is used as initial characteristic power of each distributed power station; and updating the characteristic power of each power station by using an exponential weighted average method according to the generated power of each power station under sunny days so as to achieve the purpose of representing the power generation characteristics of each power station. The formula of the exponential weighted average method is:
Figure SMS_5
。
in the method, in the process of the invention,
Figure SMS_6
is the characteristic power after smoothing at time point i, < >>
Figure SMS_7
Is the historical actual power at time point i, α is the memory decay factor, +.>
Figure SMS_8
Is the characteristic power smoothed at the time point i-1. In the embodiment, alpha is 0.2 so as to ensure that the characteristic power changes correspondingly with seasons, dust coverage conditions and aging conditions of the photovoltaic panel on the premise that the characteristic power of the power station is basically stable.
The characteristic power is updated and calculated for the power stations 13, 25, 29 of the area, and the calculated characteristic power is shown in fig. 3 on a certain day, wherein the characteristic power of the power stations 13, 25 is similar, and the characteristic power of the power station 29 is higher than that of the power stations 13, 25.
S4: and taking meteorological data, time data and characteristic power as input, taking normalized historical power generation power data as output, and training by using a CNN neural network to obtain a photovoltaic power unified prediction model.
S5: and updating the characteristic power of the distributed photovoltaic power station according to a certain frequency, inputting the updated characteristic power, meteorological data and time data into the photovoltaic power unified prediction model obtained in the step S4, and outputting a photovoltaic power prediction result.
As a result of the prediction, as shown in fig. 4, the power generated by the distributed photovoltaic power stations 13, 25, 29 on a certain day is predicted together. It can be seen that the power generated by the power stations 13, 25 is almost uniform over the day, whereas the power generated by the power station 29 is significantly higher than 13, 25, since the characteristic power characterizing the power generation characteristics of each power station is used, even if the same predictive model is used, the predicted powers of the power stations 13, 25 are similar on the basis of the predicted results, whereas the predicted power of the power station 29 is higher than 13, 25.
The above embodiments are illustrative of the specific embodiments of the present invention, and not restrictive, and various changes and modifications may be made by those skilled in the relevant art without departing from the spirit and scope of the invention, so that all such equivalent embodiments are intended to be within the scope of the invention.

Claims (6)

1. The distributed photovoltaic power prediction method based on the characteristic power is characterized by comprising the following steps of:
s1: collecting historical power generation power data and power station capacity of each distributed photovoltaic power station in a set area; collecting meteorological data and time data of a set area;
s2: carrying out normalization processing on each historical power generation power data based on the capacity of each power station acquired by the S1, and clustering according to days;
s3: processing the clustered data to obtain the characteristic power of each distributed photovoltaic power station;
s4: taking meteorological data, time data and characteristic power as input, taking normalized historical power generation power data as output, and training by using a CNN neural network to obtain a photovoltaic power unified prediction model of a set area;
s5: and updating the characteristic power of the distributed photovoltaic power station according to a certain frequency, inputting the updated characteristic power, meteorological data and time data into the photovoltaic power unified prediction model obtained in the step S4, and outputting a photovoltaic power prediction result.
2. The method for predicting the distributed photovoltaic power based on the characteristic power according to claim 1, wherein in S1, the set area is a county or a district; the meteorological data comprise ambient temperature, relative humidity, wind speed and air pressure; the time data comprises month, day, time and minute.
3. The method for predicting the distributed photovoltaic power based on the characteristic power according to claim 1, wherein the clustering method in S2 is k-means clustering;
and intercepting part of data from the normalized daily historical power generation data, and extracting the sum, the discrete difference and the third derivative average value of the power curve from the intercepted data as clustering features.
4. The method for predicting the distributed photovoltaic power based on the characteristic power according to claim 1, wherein the clustering in the S3 is k-means clustering;
processing class data with highest sum of power in the k class data by using an exponential weighted average method to obtain characteristic power of each distributed photovoltaic power station at different time;
the formula of the exponential weighted average method is as follows:
Figure QLYQS_1
；
in the method, in the process of the invention,
Figure QLYQS_2
is the characteristic power after smoothing at time point i, < >>
Figure QLYQS_3
Is the historical actual power at time point i, α is the memory decay factor, +.>
Figure QLYQS_4
Is the characteristic power smoothed at the time point i-1.
5. The method according to claim 1, wherein in S4, weather data, time data, and characteristic power of the same day are taken as one input sample, and different characteristics are taken as different channels of the input.
6. The method for predicting the distributed photovoltaic power based on the characteristic power according to claim 1, wherein the method for updating the characteristic power in S5 is as follows: and (3) classifying the daily power generation power curve by using the clustering center obtained in the step (S2), and then executing the step (S3) to obtain updated characteristic power.
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Application publication date: 20230714