A Fuzzy Model for Wind Speed Prediction and Power Generation in Wind Parks Using Spatial Correlation

Damousis, I. G.; Alexiadis, Minas C.; Theocharis, John B.; Dokopoulos, P.S.

doi:10.1109/tec.2003.821865

Cited by 470 publications

(173 citation statements)

References 11 publications

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“…The model with best performance was the simple one, an NN with two layers and three neurons. [109] and Damousis et al [110] present a Takagi-Sugeno FIS [111] that is based on wind measures of the target location and on the wind speed forecasts of neighboring locations for a time horizon of between 30 and 240 min. A genetic algorithm is used in order to optimize the FIS parameters.…”

Section: Kavasseri Et Almentioning

confidence: 99%

“…[109], [110], [118], [119] Smooth Transition Autoregressive [136]- [138] Discrete Hilbert Transform [120], [121] Markov-switching Autoregressive [136]- [138] Abductive Networks (GMDH) [114] Adaptive Fuzzy Logic Models [122], [123] Adaptive Linear Models [122], [123] ARIMA time series models [94]- [100], [106], [128]- [130] Neural Networks [104]- [108], [112], [131] Adaptive Neural Fuzzy Inference System [106], [116], [127] …”

Section: Synthesis Of the Literature Overviewmentioning

confidence: 99%

See 1 more Smart Citation

Wind power forecasting : state-of-the-art 2009.

Monteiro¹,

Bessa²,

Miranda³

et al. 2009

302

264

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Section: Kavasseri Et Almentioning

confidence: 99%

Section: Synthesis Of the Literature Overviewmentioning

confidence: 99%

Wind power forecasting : state-of-the-art 2009.

Monteiro¹,

Bessa²,

Miranda³

et al. 2009

302

264

View full text Add to dashboard Cite

“…Although two main classes of techniques have been recognized for the wind prediction, (in [7] and [8], comprehensive reviews of these methods are prepared), as aforementioned, combination of statistical and physical methods are more prevalent than the others [9], [10]. Furthermore, several other spatial correlation techniques are proposed for short term wind power forecasting with the goal of achieving higher prediction accuracy [11].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Wind Power Forecasting Using Artificial Neural Networks for Resource Scheduling in Microgrids

Tesfaye¹,

Zhang²,

Zheng³

et al. 2016

IJSEA

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Use of wind power as one of renewable resources of energy has been growing quickly all over the world. Wind power generation is significantly vacillating due to the wind speed alteration. Therefore, assessment of the output power of this type of generators is always associated with some uncertainties. A precise wind power prediction aims to support the operation of large power systems or microgrids in the scope of the intraday resources scheduling model, namely with a time horizon of 5-10 minutes, and this can efficiently uphold transmission and distribution system operators to improve the power network control and management. A Shortterm ANN based wind power prediction model, for day-ahead energy management and scheduling in microgrids, is developed utilizing a real database of 12 months with 10 minutes time interval data from measured information of online supervisory control and data acquisition (SCADA) of Goldwind microgrid wind turbine system (Beijing, China) as well as Numerical Weather Prediction (NWP). The ANN predicted wind power has been compared with the actual power of Goldwind wind turbine system. The predicted wind power has shown acceptable agreement with the actual power output that has been recorded by the SCADA which confirms the robustness and accuracy of the ANN wind power forecasting model developed in this research paper.

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“…However, ARMA is a linear model but the nonlinear wind speed TS is not suitable for linear modelling. Some Computational Intelligence (CI) based models were applied for wind speed/power TS forecasting [3], [4]. These CI based models were evaluated and compared with statistical models and the results showed that they usually outperform statistical models.…”

Section: Introductionmentioning

confidence: 99%

Power Quality Improvement Strategy for Wind Energy Conversion System

Gandhare¹,

Hete²

2014

JPEE

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Hybrid model is a popular forecasting model in renewable energy related forecasting applications. Wind speed forecasting, as a common application, requires fast and accurate forecasting models. This paper introduces an Empirical Mode Decomposition (EMD) followed by a k Nearest Neighbor (kNN) hybrid model for wind speed forecasting. Two configurations of EMD-kNN are discussed in details: an EMD-kNN-P that applies kNN on each decomposed intrinsic mode function (IMF) and residue for separate modelling and forecasting followed by summation and an EMD-kNN-M that forms a feature vector set from all IMFs and residue followed by a single kNN modelling and forecasting. These two configurations are compared with the persistent model and the conventional kNN model on a wind speed time series dataset from Singapore. The results show that the two EMD-kNN hybrid models have good performance for longer term forecasting and EMD-kNN-M has better performance than EMD-kNN-P for shorter term forecasting.

show abstract

A Fuzzy Model for Wind Speed Prediction and Power Generation in Wind Parks Using Spatial Correlation

Cited by 470 publications

References 11 publications

Wind power forecasting : state-of-the-art 2009.

Wind power forecasting : state-of-the-art 2009.

Short-Term Wind Power Forecasting Using Artificial Neural Networks for Resource Scheduling in Microgrids

Power Quality Improvement Strategy for Wind Energy Conversion System

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