A combination forecasting model of wind speed based on decomposition

Tian, Zhongda; Li, Hao; Li, Feihong

doi:10.1016/j.egyr.2021.02.002

Cited by 71 publications

(27 citation statements)

References 54 publications

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“…Nevertheless, wind speed is a complex, noisy and chaotic time sequence, and ignoring these identities will obviously contribute to deviations between the predicted results and the actual data. [ 24–26 ] As the application of artificial intelligence continues to expand, scholars have found that such methods are quite effective in forecasting nonlinear data. [ 27–31 ] The artificial intelligence model can process a large amount of data in parallel, which makes the learning of historical data more accurate, thus resulting in a prediction trend closer to reality.…”

Section: Brief Literature Reviewmentioning

confidence: 99%

Wind Speed Prediction in the Hexi Corridor Region of China Using an Enhanced Multiscale Nonlinear Ensemble Model

Wang

Cheng

2021

Energy Tech

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Accurate wind speed prediction has become more and more popular. Herein, an enhanced multi‐scale nonlinear ensemble model is proposed to predict short‐term wind speed. First, advanced data preprocessing technology is used to extract these feature sequences. Second, each feature sequence is predicted by bidirectional long short‐term memory. In addition, the gated recurrent unit (GRU) realizes nonlinear ensemble and integrates the prediction information. Finally, GRU with white noise test is utilized to correct the residual to retain the possible effective information in the residual. The performance of the model is evaluated using the daily and hourly wind speeds of two wind farms in the Hexi Corridor region. The experimental results show that the proposed model has the best prediction accuracy among all relevant models. Taking Zhangye wind farm as an example, in the daily wind speed dataset, mean absolute percentage error, square sum error, theil inequality coefficient and the index of agreement of the model are 0.6416, 5.4234, 0.0151, and 0.9979, respectively. In the hourly dataset, they are 0.1037, 0.0042, 0.0003, and 1.0000, respectively. In the wind speed data of the Guazhou wind farm, the hybrid model proposed in this research also performs best.

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Section: Brief Literature Reviewmentioning

confidence: 99%

Wind Speed Prediction in the Hexi Corridor Region of China Using an Enhanced Multiscale Nonlinear Ensemble Model

Wang

Cheng

2021

Energy Tech

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“…If the predicted wind speed at the time of prediction differs from the wind power output trend, the correlation between the wind speed and the wind power output decreases, and thus the prediction accuracy of the wind power output decreases. To improve this, wind speed prediction was performed using variational mode decomposition (VMD) and echo state network (ESN) techniques in [5,6]. In [5], wind speed prediction was performed using a VMD-DE (differential evolution)-ESN hybrid model, and in [6], a wind speed prediction model based on VMD and IWOA-ESN was proposed.…”

Section: Introductionmentioning

confidence: 99%

“…To improve this, wind speed prediction was performed using variational mode decomposition (VMD) and echo state network (ESN) techniques in [5,6]. In [5], wind speed prediction was performed using a VMD-DE (differential evolution)-ESN hybrid model, and in [6], a wind speed prediction model based on VMD and IWOA-ESN was proposed. The wind speed data are decomposed, and noise is removed through VMD, and the main characteristics of each frequency are extracted.…”

Section: Introductionmentioning

confidence: 99%

“…Then the final wind speed is predicted for each subseries data by predicting the wind speed for each series based on the proposed prediction model. If the accuracy of the wind speed predicted through the methodologies proposed in [5] and [6] is high, the accuracy of wind power output prediction using the wind speed prediction value can be increased. In addition, by establishing a wind power generation operation plan (scheduling), it is possible to increase the wind energy utilization rate and reduce power generation costs.…”

Section: Introductionmentioning

confidence: 99%

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A Short-Term Power Output Forecasting Based on Augmented Naïve Bayes Classifiers for High Wind Power Penetrations

Kim

Hur

2021

Sustainability

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Renewable-power-generating resources can provide unlimited clean energy and emit at most minute amounts of air pollutants and greenhouse gases, whereas fossil fuels are contributing to environmental pollution problems and climate change. The share of global power capacity comprising renewable-power-generating resources is increasing. However, due to the variability and uncertainty of wind resources, predicting the power output of these resources remains a key problem that must be resolved to establish stable power system operation and planning. In this study, we propose an ensemble prediction model for wind-power-generating resources based on augmented naïve Bayes classifiers. To select the principal component that affects the wind power outputs from among various meteorological factors, such as temperature, wind speed, and wind direction, prediction of wind-power-generating resources was performed using multiple linear regression (MLR) and a naïve Bayes classification model based on the selected meteorological factors. We proposed applying the analogue ensemble (AnEn) algorithm and the ensemble learning technique to predict the wind power. To validate this proposed hybrid prediction model, we analyzed empirical data from the wind farm of Jeju Island in South Korea and found that the proposed model has lower error than the single prediction models.

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“…The characterization of wind speed is achieved by measurements, and there are factors that can make it difficult to proceed, such as the presence of adverse weather conditions, meter errors, lack of measurements due to maintenance periods or communication problems. In addition, it can happen that the accuracy of the measurement devices is not as good as expected, and this can result in an alteration of the values of the collected data [13,14]. Additionally, in general, the wind speeds are measured at a given height that does not necessarily coincide with those of the wind turbine hubs [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Simulation of Wind Speeds with Spatio-Temporal Correlation

et al. 2021

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Nowadays, there is a growing trend to incorporate renewables in electrical power systems and, in particular, wind energy, which has become an important primary source in the electricity mix of many countries, where wind farms have been proliferating in recent years. This circumstance makes it particularly interesting to understand wind behavior because generated power depends on it. In this paper, a method is proposed to synthetically generate sequences of wind speed values satisfying two important constraints. The first consists of fitting the given statistical distributions, as the generally accepted fact is assumed that the measured wind speed in a location follows a certain distribution. The second consists of imposing spatial and temporal correlations among the simulated wind speed sequences. The method was successfully checked under different scenarios, depending on variables, such as the number of locations, the duration of the data collection period or the size of the simulated series, and the results were of high accuracy.

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A combination forecasting model of wind speed based on decomposition

Cited by 71 publications

References 54 publications

Wind Speed Prediction in the Hexi Corridor Region of China Using an Enhanced Multiscale Nonlinear Ensemble Model

Wind Speed Prediction in the Hexi Corridor Region of China Using an Enhanced Multiscale Nonlinear Ensemble Model

A Short-Term Power Output Forecasting Based on Augmented Naïve Bayes Classifiers for High Wind Power Penetrations

Simulation of Wind Speeds with Spatio-Temporal Correlation

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