Study on Forecast of Time Series of Wind Velocity for Wind Power Generation by Using Wide Meteorological Data

Taniguchi, Kengo; Ichiyanagi, Katsuhiro; Yukita, Kazuto; Goto, Yasuyuki

doi:10.1541/ieejpes.128.416

Cited by 14 publications

(12 citation statements)

References 8 publications

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“…The other method is a statistical method using neural networks (NNs), which are powerful application tools for nonlinear data and do not require complicated calculation with mathematical models. That is why there are papers reporting on wind speed prediction by using feedforward neural networks (FNNs) [2–4] or recurrent neural networks (RNNs) [5–7]. FNNs can predict time‐series data by converting a time‐series pattern to spatial patterns, and RNNs are suitable for treating them because of their internal feedback structure.…”

Section: Introductionmentioning

confidence: 99%

Study on output prediction system of wind power generation using complex‐valued neural network with multipoint GPV data

Komamizu

Yasuno

Sori

2012

IEEJ Transactions Elec Engng

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In recent years, wind power generation has seen rapid growth as a solution to the depletion of fossil fuels and to global warming. However, the output of the wind power generator fluctuates widely depending on the wind speed, which affects the power grid. In this paper, we propose a prediction system of wind power generation ahead of 24 h by using a complex‐valued neural network (CVNN). To predict the output of wind power generation, it is necessary to predict the wind speed accurately. Generally, wind data is expressed as a vector, which has both magnitude and direction. Therefore, it is also possible to treat wind data by a complex number and then use it as input information of the CVNN, which is very useful for operation of a complex number and nonlinear data. In computer simulations, we use multipoint numerical weather prediction data that surround the wind prediction point to realize long‐time prediction. Thereby, we expect that the CVNN can take into account wind dynamics in two‐dimensional space. Several simulation results and t‐tests demonstrate the effectiveness of the proposed prediction system. © 2012 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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Section: Introductionmentioning

confidence: 99%

Study on output prediction system of wind power generation using complex‐valued neural network with multipoint GPV data

Komamizu

Yasuno

Sori

2012

IEEJ Transactions Elec Engng

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“…The methods use actual wind power of the past several hours [7][8][9]. The methods use actual wind power of the past several hours [7][8][9].…”

Section: Wind Speed At the Height Of Wind Wheelmentioning

confidence: 99%

“…The best air pressure surface for wind power forecasts is not known, because each conventional study has used different surface data. Ground wind speed that is used by the conventional method [7][8][9]11] is different from actual wind speed at the height of the wind wheel, because the correlation of AMeDAS in Table 1 is low. The wind speed at the height of the target wind wheel is calculated as where h p is the height of given air pressure above the target wind wheel; W p is the wind speed of the given air pressure surface above the target wind wheel; W f0 is the wind speed height of 10 m from the ground; h w is the height of the target wind wheel; W f1 is the wind speed at the height of the target wind wheel.…”

Section: Wind Speed At the Height Of Wind Wheelmentioning

confidence: 99%

A Wind Power Forecasting Method and Its Confidence Interval Estimation

Iizaka

Jintsugawa

Kondo

et al. 2013

Electrical Engineering Japan

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This paper describes a wind power forecasting method and its confidence interval estimation. Recently, flat control of wind power generators using various batteries has been required. In flat control, accurate wind power forecasts and their error confidence intervals are needed. In this paper, wind speed forecasts are calculated by regression models using Grid Point Value (GPV) weather forecasts. The forecasts are adjusted by fuzzy inference using the latest errors. The wind power forecasts are translated from the wind speed forecasts using two power curves. The power curves are selected or combined by fuzzy inference depending on the wind direction. The error confidence interval models are generated for each forecasting target time. Each confidence interval is combined by the other fuzzy inference. The proposed methods were applied to actual wind power generators, and it was found that the forecasting errors were smaller than in the conventional methods. Almost all of the forecasts can be within the error confidence intervals estimated by the proposed methods. The results show the effectiveness of the proposed methods.

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“…Intelligent system methods such as fuzzy [1], ANN [2][3] [4], time series analysis [5] have many examples of application for this purpose. On the other hand, CFD (Computational Fluid Dynamics) calculation has been long pointed out to be important in this field [6].…”

Section: Introductionmentioning

confidence: 99%

Accuracy Improvement in One-day Ahead Wind Power Output Prediction by Computational Fluid Dynamics Calculation

Kumano¹

2012

REPQJ

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Study on Forecast of Time Series of Wind Velocity for Wind Power Generation by Using Wide Meteorological Data

Cited by 14 publications

References 8 publications

Study on output prediction system of wind power generation using complex‐valued neural network with multipoint GPV data

Study on output prediction system of wind power generation using complex‐valued neural network with multipoint GPV data

A Wind Power Forecasting Method and Its Confidence Interval Estimation

Accuracy Improvement in One-day Ahead Wind Power Output Prediction by Computational Fluid Dynamics Calculation

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