Short-term wind power forecasts by a synthetical similar time series data mining method

Sun, Gaiping; Jiang, Chuanwen; Pan, Cheng; Liu, Yangyang; Wang, Xu; Fu, Yang; He, Yuehui

doi:10.1016/j.renene.2017.08.071

Cited by 94 publications

(26 citation statements)

References 26 publications

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“…Neural network approaches require particular attention to select the appropriate and relevant structural parameters of the model such as the number of layers and neurons. Alencar et al [19] and Sun et al [34] revealed that the forecasting error increases with the increase in the time horizon. In a comparison between ANN and a hybrid model (ANN and computational fluid dynamics (CFD)) against the Supervisory Control and Data Acquisition (SCADA) in a wind farm in Italy, Burlando et al [48] found that both methods gave similar results, however, ANN forecasts were better at medium wind speed ranges while the hybrid model forecasts were better at low and high wind speed ranges.…”

Section: Artificial Intelligence Methodsmentioning

confidence: 99%

“…On the other hand, wind forecasting methods are classified into four categories: physical methods, statistical methods, hybrid methods, and artificial intelligence methods [17,19,33]. Other authors, such as Sun et al [34] listed the persistence method or "Naïve Predictor", which assumes a correlation between the wind speed at a time "t + x" and the current wind speed at the time "t", where both speeds are assumed to be the same. The precision of this model decreases quickly in subsequent prediction time.…”

Section: Related Workmentioning

confidence: 99%

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Using Artificial Intelligence to Predict Wind Speed for Energy Application in Saudi Arabia

Brahimi

2019

Energies

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Predicting wind speed for wind energy conversion systems (WECS) is an essential monitor, control, plan, and dispatch generated power and meets customer needs. The Kingdom of Saudi Arabia recently set ambitious targets in its national transformation program and Vision 2030 to move away from oil dependence and redirect oil and gas exploration efforts to other higher-value uses, chiefly meeting 10% of its energy demand through renewable energy sources. In this paper, we propose the use of the artificial neural networks (ANNs) method as a means of predicting daily wind speed in a number of locations in the Kingdom of Saudi Arabia based on multiple local meteorological measurement data provided by K.A.CARE. The suggested model is a feed-forward neural network model with the administered learning technique using a back-propagation algorithm. Results indicate that the best structure is obtained with thirty neurons in the hidden layers matching a minimum root mean square error (RMSE) and the highest correlation coefficient (R). A comparison between predicted and actual data from meteorological stations showed good agreement. A comparison between five machine learning algorithms, namely ANN, support vector machines (SVM), random tree, random forest, and RepTree revealed that random tree has low correlation and relatively high root mean square error. The significance of the present study relies on its ability to predict wind speeds, a necessary prerequisite to executing sustainable integration of wind power into Saudi Arabia’s electrical grid, assisting operators in efficiently managing generated power, and helping achieve the energy efficiency and production targets of Vision 2030.

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Section: Artificial Intelligence Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Using Artificial Intelligence to Predict Wind Speed for Energy Application in Saudi Arabia

Brahimi

2019

Energies

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“…These charging stations were classified based on 14 substation locations to aggregate charging power data. The Euclidean distance in [16] was used as a method of mapping EV charging stations to their corresponding substation location, and the point where the Euclidean distance between the charging station and the substation was the minimum was selected. Table 1 shows the EV charging demand In the first step, the uncertainty in security assessment is modeled by performing probabilistic analysis of the EV charging demand and wind power output.…”

Section: Gaussian Mixture Distribution Of Evs Charging Demandmentioning

confidence: 99%

A Probabilistic Modeling Based on Monte Carlo Simulation of Wind Powered EV Charging Stations for Steady-States Security Analysis

Kim

Hur

2020

Energies

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As renewable energy resources such as wind and solar power are developing and the penetration of electric vehicles (EVs) is increasingly integrated into existing systems, uncertainty and variability in power systems have become important issues. The charging demands for EVs and wind power output are recognized as highly variable generation resources (VGRs) with uncertainty, which can cause unexpected disturbances such as short circuits. This can deteriorate the reliability of existing power systems. In response, research is required to identify the uncertainties presented by VGRs and is required to examine the ability of power system models to reflect those uncertainties. The deterministic method, which is the most basic method that is currently in use, does not reflect the uncertainty of system components. Therefore, this paper proposes a probabilistic method to assess the steady-state security of power systems, reflecting the uncertainty of VGRs using Monte Carlo simulation (MCS). In the proposed method, the empirical EVs charging demand and wind power output data are modeled as a probability distribution, and then MCS is performed, integrating the power system operation to represent the steady-state security as a probability index. To verify the method proposed in this paper, a security analysis was performed based on the systems in Jeju Island, South Korea, where the penetration of wind power and EVs is expanding rapidly.

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“…The forecast methods based on statistical model establish a linear or non-linear mapping between the actual and the forecast wind power by summarising the statistical rules from the historical wind power data. Since the historical data sequence can reflect the influence of fluid, thermal force, topography and other factors, the statistical model can avoid the difficulty of grasping the physical mechanism [4,5], which mainly includes the time series method [6], support vector machine [7], artificial neural network [8][9][10][11], Bayesian [12], the similar days method [13] and so on. Due to the characteristics of wind power are complex, these statistical models have limited learning ability for long-term time series.…”

Section: Introductionmentioning

confidence: 99%

Multi‐step wind power forecast based on VMD‐LSTM

Han

Zhang

Wang

et al. 2019

IET Renewable Power Generation

124

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To improve the accuracy of multi-step wind power forecast, a variational mode decomposition-long short-term memory (VMD-LSTM) forecast method is proposed. Firstly, the variational mode decomposition method is adopted to decompose the wind power data into three constituent modes, named as the long-term component, the fluctuation component and the random component. Secondly, long short-term memory network is utilised to deeply learn the characteristics of the three constituent modes. Profit from its unique forget gate and memory gate structure, the association with long-term time series is learned to build a multi-step forecast model. Finally, the wind power data from ELIA and NERL are used to test. The error analysis shows that the proposed method has superior performance in the multi-step forecast and real-time forecast.

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Short-term wind power forecasts by a synthetical similar time series data mining method

Cited by 94 publications

References 26 publications

Using Artificial Intelligence to Predict Wind Speed for Energy Application in Saudi Arabia

Using Artificial Intelligence to Predict Wind Speed for Energy Application in Saudi Arabia

A Probabilistic Modeling Based on Monte Carlo Simulation of Wind Powered EV Charging Stations for Steady-States Security Analysis

Multi‐step wind power forecast based on VMD‐LSTM

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