A hybrid strategy of short term wind power prediction

Peng, Huaiwu; Liu, Fangrui; Yang, Xiaofeng

doi:10.1016/j.renene.2012.07.022

Cited by 102 publications

(48 citation statements)

References 3 publications

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“…Some common error criteria include absolute error (AE), mean absolute error (MAE), mean absolute percentage error (MAPE), root mean-square error (RMSE), standard deviation of error (SDE) [8,15,[27][28][29], etc. Compared to MAE, RMSE is more sensitive to large data samples and is robust when dealing with large errors [30].…”

Section: Error Criteriamentioning

confidence: 99%

“…As a result how to effectively utilize wind resources has received increasing attention [4,5]. Wind power generation in wind farms could make full use of wind energy on a large-scale [6,7], but considering the stochastic volatility nature of wind energy, integration of wind power into power systems becomes a challenge [8][9][10][11]. Necessary measures should be taken to maintain the normal operation of electric systems and decrease losses that could affect people's daily prodivity and life.…”

Section: Introductionmentioning

confidence: 99%

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Lorenz Wind Disturbance Model Based on Grey Generated Components

et al. 2014

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Abstract:In order to meet the needs of wind speed prediction in wind farms, we consider the influence of random atmospheric disturbances on wind variations. Considering a simplified fluid convection mode, a Lorenz system can be employed as an atmospheric disturbance model. Here Lorenz disturbance is defined as the European norm of the solutions of the Lorenz equation. Grey generating and accumulated generating models are employed to explore the relationship between wind speed and its related disturbance series. We conclude that a linear or quadric polynomial generating model are optimal through the verification of short-term wind speed prediction in the Sotavento wind farm. The new proposed model not only greatly improves the precision of short-term wind speed prediction, but also has great significance for the maintenance and stability of wind power system operation.

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Section: Error Criteriamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Lorenz Wind Disturbance Model Based on Grey Generated Components

et al. 2014

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“…Each type of prediction model has its own specific characteristics. Compared to physical models, statistical models are typically simple and more appropriate for small farms [6]. The performance of hybrid models is usually better than that of single models, in wind speed forecasting [7].…”

Section: Introductionmentioning

confidence: 99%

Advanced Wind Speed Prediction Model Based on a Combination of Weibull Distribution and an Artificial Neural Network

et al. 2017

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Abstract:One of the most crucial prerequisites for effective wind power planning and operation in power systems is precise wind speed forecasting. Highly random fluctuations of wind influenced by the conditions of the atmosphere, weather and terrain result in difficulties of forecasting regardless of whether it is short-term or long-term. The current study has developed a method to model wind speed data predictions with dependence on seasonal wind variations over a particular time frame, usually a year, in the form of a Weibull distribution model with an artificial neural network (ANN). As a result, the essential dependencies between the wind speed and seasonal weather variation are exploited. The proposed model utilizes the ANN to predict the wind speed data, which has similar chronological and seasonal characteristics to the actual wind data. This model was applied to wind speed databases from selected sites in Malaysia, namely Mersing, Kudat, and Kuala Terengganu, to validate the proposed model. The results indicate that the proposed hybrid artificial neural network (HANN) model is capable of depicting the fluctuating wind speed during different seasons of the year at different locations.

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“…Currently, there are three main ways for short-term wind energy prediction [1]: physical prediction method, statistical prediction method and intelligent prediction method. Physical prediction need to establish a physical model for the wind turbines, which requiring extensive knowledge of meteorology and topography, turbulence and other physical information.…”

Section: Introductionmentioning

confidence: 99%

Short-term Wind Energy Prediction Algorithm Based on SAGA-DBNs

Wang¹,

Wu²,

Li³

2017

dtcse

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Abstract. In order to overcome initial weights direction in the deep conviction network and improve the prediction accuracy of wind energy further, this paper proposes a new algorithm which combined the simulated annealing genetic algorithm and the deep belief network. Firstly, the main influencing factors of wind energy are selected. Then the superior global searching capability of the simulated annealing genetic algorithm is used to train the deep belief network and optimize the weights layer by layer. Finally, the deep belief networks weights were tuned by a BP neural network to make sure the networks are the best wind energy prediction system. The algorithm is verified by the experimental data from a wind farm in western China. The results show that the algorithm not only can overcome initial weights direction in the deep belief networks, but also further improve the prediction accuracy.

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A hybrid strategy of short term wind power prediction

Cited by 102 publications

References 3 publications

Lorenz Wind Disturbance Model Based on Grey Generated Components

Lorenz Wind Disturbance Model Based on Grey Generated Components

Advanced Wind Speed Prediction Model Based on a Combination of Weibull Distribution and an Artificial Neural Network

Short-term Wind Energy Prediction Algorithm Based on SAGA-DBNs

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