An adaptive short-term prediction scheme for wind energy storage management

Blonbou, Ruddy; Monjoly, Stéphanie; Dorville, Jean-François

doi:10.1016/j.enconman.2011.01.013

Cited by 23 publications

(6 citation statements)

References 8 publications

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“…However, randomness and fluctuation of wind power can cause fluctuation and instability of wind power output and impede large-scale wind power connected power systems, leading to electric power sector's difficulty in formulating generation scheduling and dispatching electric power. The short-term wind power prediction is expected to solve such problems [1].…”

Section: Introductionmentioning

confidence: 99%

Short-term wind power prediction based on LSSVM–GSA model

Yuan

Chen

Yuan

et al. 2015

Energy Conversion and Management

229

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Section: Introductionmentioning

confidence: 99%

Short-term wind power prediction based on LSSVM–GSA model

Yuan

Chen

Yuan

et al. 2015

Energy Conversion and Management

229

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“…The original wind power time series data

y = false[y_{1}, y_{2}, \dots, y_{N} false]

were collected from a wind farm. Most of the wind power prediction methods were based on time series models, in which historical wind power information was solely utilized 28‐30 . For this reason, this study still utilized the time series data to perform clustered wind power predictions.…”

Section: Methodsmentioning

confidence: 99%

A novel two‐stage interval prediction method based on minimal gated memory network for clustered wind power forecasting

Lang

Peng

et al. 2020

Wind Energy

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As the global demand for clean renewable energy has grown, the contribution of wind power in grid systems has significantly improved. Wind power predictions play an important role in the stable and safe operation of grid systems. Considering the shortcomings of traditional wind power point predictions, a novel two‐stage short‐term wind power interval prediction method is proposed in this study. In the proposed method, the minimal gated memory (MGM) network and improved interval width adaptive adjustment strategy, which is an approach that is designed to adjust the prediction interval (PI) labels, are combined for short‐term interval predictions of wind power. First, the point model for subsequence data based on the MGM network is proposed. Subsequently, the interval model is proposed to obtain the final PIs of wind power using the improved interval width adaptive adjustment strategy. Finally, with the purpose of verifying the prediction performance of the proposed model, two datasets and four representative benchmark models are implemented for comparative experiments. The superiority of the proposed model is demonstrated via experimental results, which show that the proposed model can obtain suitable wind power intervals with high confidence and quality.

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“…The first group is based on a statistical approach and uses historical data to find out relationships between the power generated by a wind power plant and the corresponding wind speed [4][5][6][7][8]. The second group represents a physical modeling approach and is based on using information on global and local geographic and atmospheric conditions to develop a model that gives the best solution [9][10][11]. Developers of physical models try to recreate physical conditions that affect power generation and use forecasting tools only during the final stage.…”

Section: Introductionmentioning

confidence: 99%

Bulletin of the Polish Academy of Sciences: Technical Sciences

Popławski,

Szeląg,

Bartnik

2020

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The paper proposes an adaptation of mathematical models derived from the theory of deterministic chaos to short-term power forecasts of wind turbines. The operation of wind power plants and the generated power depend mainly on the wind speed at a given location. It is a stochastic process dependent on many factors and very difficult to predict. Classical forecasting models are often unable to find the existing relationships between the factors influencing wind power output. Therefore, we decided to refer to fractal geometry. Two models based on self-similar processes (M-CO) and (M-COP) and the (M-HUR) model were built. The accuracy of these models was compared with other shortterm forecasting models. The modified model of power curve adjusted to local conditions (M-PC) and Canonical Distribution of the Vector of Random Variables Model (CDVRM). Examples of applications confirm the valuable properties of the proposed approaches.

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An adaptive short-term prediction scheme for wind energy storage management

Cited by 23 publications

References 8 publications

Short-term wind power prediction based on LSSVM–GSA model

Short-term wind power prediction based on LSSVM–GSA model

A novel two‐stage interval prediction method based on minimal gated memory network for clustered wind power forecasting

Bulletin of the Polish Academy of Sciences: Technical Sciences

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