Short-term forecasting of wind speed and related electrical power

Alexiadis, Minas C.; Dokopoulos, P.S.; Sahsamanoglou, H. S.; Manousaridis, I.M.

doi:10.1016/s0038-092x(98)00032-2

Cited by 291 publications

(140 citation statements)

References 7 publications

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“…For instance, Kalman filter [4] , persistence algorithm [5] , ARMA algorithm [6,7] , linear regression model [8] , and adaptive fuzzy-logic algorithm.…”

Section: Related Workmentioning

confidence: 99%

Wind Power Forecasting Algorithm Based on Similarity of Multivariate Time Series

Hui-ying¹,

Yang²,

Wang³

et al. 2017

dteees

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Abstract. An accurate prediction of wind power plants has great significance to the power leveling and stability of a power grid. While the electricity market gradually introducing competition mechanism, it could maximize the growth of economic benefits for electricity companies. From a complex system's point of view, this paper introduces the forecasting of wind power by considering the power generation of wind plants as a black box system, using weighted average and analysis of the historical time series similarity between the meteorological forecast data and the relevant factors affecting wind power generation. The experiment has proof this algorithm simpler, more accurate and maneuverable compared than other wind power algorithms.

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“…For instance, Kalman filter [4] , persistence algorithm [5] , ARMA algorithm [6,7] , linear regression model [8] , and adaptive fuzzy-logic algorithm.…”

Section: Related Workmentioning

confidence: 99%

Wind Power Forecasting Algorithm Based on Similarity of Multivariate Time Series

Hui-ying¹,

Yang²,

Wang³

et al. 2017

dteees

View full text Add to dashboard Cite

show abstract

“…Generally, the physical model is the prediction model that considers the changes of the regional meteorological model accordingly with medium-scale weather changes and fits in mid-term prediction after 6 hours [6]. So for a short-term prediction within 6 hours, the prediction method can consider that the characteristics of statistical model have to be applied.…”

Section: Prediction Of Short/mid-term Wind Velocitymentioning

confidence: 99%

The Prediction of Wind Power Generation: A Case Study in South Korea

Kim¹,

Shin²,

Park³

et al. 2017

JOCET

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Abstract-It is difficult to predict the power generation amount of wind turbines because it is depending on weather condition. However it is important to predict wind power generation amount accurately, because it is possible to maximize the utilization of wind turbines, and also establish more effective operation plan of grid. The purpose of this study is to increase the accuracy of prediction of the power generation amount. To increase accuracy, we built the 3 dimensional weather research and forecasting model that is the most applicable to our developing district environment. In case of short-term prediction within 6 hours, to overcome the weakness that cannot reflect the regional characteristics for particular areas enough, we predicted weather information considering statistical characteristics such as weather pattern change information of wind plants. Using the result of weather prediction as a basic data, we computed the power generation amount by plants and wind generators. In the proposed approaches, inaccuracies are analyzed based on the combination of physical model and statistical model. The result of the analysis showed 15.7% (within 24 hours), and 17.5% (within 48 hours) error rate.Index Term-Short and midterm prediction of wind power, wind power generation forecasting system, distributed generation.

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“…Kalman Filter [8], [9] Fuzzy Time Series [17], [19] Grey Predictor [10] Self-exciting Threshold Autoregressive [20]- [22] Takagi-Sugeno [11]- [14] Smooth Transition Autoregressive [20]- [22] Discrete Hilbert Transform [15], [16] Markov-switching Autoregressive [20]- [22] Abductive Networks (GMDH) [18] Adaptive Fuzzy Logic Models [23], [24] Adaptive Linear Models [23], [24] ARIMA time series models [25]- [35] Neural Networks [19], [36]- [41] Adaptive Neural Fuzzy Inference System [31], [42], [43] …”

Section: Very-short-term Wind Power Forecastingmentioning

confidence: 99%