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Wind power probabilistic forecasting method based on longitudinal moment Markov chain model

A markov chain, wind power prediction technology, applied in forecasting, data processing applications, instruments, etc., can solve problems such as processing, unpredictable error correction, etc.

Active Publication Date: 2014-08-20
SHANDONG UNIV
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Problems solved by technology

[0015] In the processing of deterministic prediction results, literature [Zhou Feng, Jin Lisi, Wang Bingquan, et al. Performance analysis of wind power prediction based on high-order Markov chain model[J]. Power System Protection and Control, 2012, 40(6) :6-10.] and [Zhou Feng, Jin Lisi, Liu Jian, et al. Probabilistic Prediction of Wind Power Based on Multi-state Space Hybrid Markov Chains[J]. Automation of Electric Power Systems, 2012,36(6):29-33 .] did not process the forecast results, resulting in the inability to correct the forecast error

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  • Wind power probabilistic forecasting method based on longitudinal moment Markov chain model
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  • Wind power probabilistic forecasting method based on longitudinal moment Markov chain model

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Embodiment Construction

[0068] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0069] 1.1.1.11 Traditional Markov chain and probability transition matrix

[0070] Let time and state be discrete random processes {X n =X(n), the state space of n=0,1,2,...} is I={a 1 ,a 2 ,...},a · ∈R. Assume that as long as the process is in state a at the current moment x , there is a fixed probability P x,y Let the process be in the state ax at the next moment, that is, assuming that for all states and all n≥0, there is P{X n =a y |X 1 =a 1 , X 2 =a 2 ,...X n-1 =a x} (1)=P{X n -a y |X n-1 -a x},a · ∈ I

[0071] Such random processes are called Markov chains. For a Markov chain, in a given past state X 0 , X1 ,...,X n-1 and the current state X n , the future state X n+1 The conditional distribution of is independent of past states and only depends on the present state.

[0072] P means the process is in state a x The next ...

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Abstract

The invention discloses a wind power probabilistic forecasting method based on a longitudinal moment Markov chain model. Wind power historical data of corresponding moments are recorded through reasonable longitudinal moment and state partition to form a longitudinal moment Markov chain probability transfer matrix which embodies state transition probability characteristics of longitudinal moments; on the basis of wind power probabilistic forecasting results, forecast is carried out by recording the probability distribution of wind power output variation of an adjacent moment and setting expectation of a confidence interval over probabilistic forecasting, probabilities exceeding the confidence interval are amended through the expectation of variation, and precision of determinacy predicted values is improved. Through verification of actual wind field data and comparison of various error indicators, the effectiveness of the model and the forecasting method is confirmed.

Description

technical field [0001] The invention relates to a wind power probability prediction method based on a longitudinal moment Markov chain model. Background technique [0002] With the increasingly acute energy and environmental issues, wind power has developed rapidly due to its clean, renewable, and huge reserves. According to the latest statistics from the China Wind Energy Association, in 2013, China (excluding Taiwan) newly added installed capacity of 16,088.7MW, a year-on-year increase of 24.1%; cumulative installed capacity of 91,412.89MW, a year-on-year increase of 21.4%. Both new installed capacity and cumulative installed capacity rank first in the world. Although the wind power generation technology continues to mature, the randomness, volatility and uncontrollability of wind power output still bring many problems to the large-scale grid connection of wind power. Therefore, accurate wind power forecasting is of great significance to the flexible scheduling and safe ...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06
Inventor 贠志皓孙景文
Owner SHANDONG UNIV
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