Wind power forecasting based on principle component phase space reconstruction

Han, Li; Romero, Carlos E.; Yao, Zheng

doi:10.1016/j.renene.2015.03.037

Cited by 78 publications

(25 citation statements)

References 22 publications

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“…9 In addition, Pm is widely used as a comparison in order to validate any proposed FM related to highly random phenomena, as seen in several works. 4,8,9,[15][16][17][18][19][20][21][22] For very short and short-term forecasts, persistence is the most used due to its simplicity as benchmark since it does not need even the calculation of coefficients. We can highlight the work of Koc xak, 23 where the authors use the persistence model as a benchmark to compare hourly energy forecast models of a wind turbine with many forecast horizons (up to 24-step-forward).…”

Section: Persistencementioning

confidence: 99%

Forecast of daily output energy of wind turbine using sARIMA and nonlinear autoregressive models

García

Calderón

González-A

et al. 2019

Advances in Mechanical Engineering

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Forecast models for wind speed and wind turbine power generation are valuable support tools for operators of Control Energy Center. In this work, a year of daily energy output of a wind turbine is analyzed. The original time series was separated into a high-power sample and a low-power sample. High-power sample has a seasonal pattern while lowpower sample does not. Afterward, a sARIMA model was produced for high-power sample forecast, with a good performance, while for low-power sample any ARIMA model defeated persistence model; thus, a couple of nonlinear autoregressive artificial neural networks are proposed. Mean absolute error and mean square error are reported and demonstrate that the sARIMA model can predict satisfactorily high-power sample, even with limited data, while to forecast low-power sample, it is necessary to use a neural networks approach and all data available to produce accurate forecasts. In each case, a normalized comparison with persistence model is also reported. Finally, a method which uses previous data of daily output energy and forecasted future wind speed values from a numeric weather prediction model is presented to objectively identify whether the current time is in a high-power or low-power regime to choose the ad hoc daily output energy forecast model.

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Section: Persistencementioning

confidence: 99%

Forecast of daily output energy of wind turbine using sARIMA and nonlinear autoregressive models

García

Calderón

González-A

et al. 2019

Advances in Mechanical Engineering

View full text Add to dashboard Cite

show abstract

“…For the very long-term predictions, a new forecasting model based on the resource allocating network (RAN) is proposed recently and applied for wind power forecasting in [37]. The phase space reconstruction (PSR) and PCA for 48 h ahead predictions with 1 h steps is used.…”

Section: Literature Reviewmentioning

confidence: 99%

Current status of wind energy forecasting and a hybrid method for hourly predictions

Okumuş

Dinler

2016

Energy Conversion and Management

239

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“…Phase-space reconstruction [3,9] is foundation of chaos theory. Setting X is the observed chaotic time series.…”

Section: Bp Neural Network and Phase-space Reconstructionmentioning

confidence: 99%

An improved particle swarm optimization algorithm used for BP neural network and multimedia course-ware evaluation

Liu

Yin

2016

Multimed Tools Appl

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The original BP neural network has some disadvantages, such as slow convergence speed, low precision, which is easy to fall into local minimum value. So this paper proposes an improved particle swarm optimization (PSO) algorithm to optimize BP neural network. In this new algorithm, PSO uses improved adaptive acceleration factor and improved adaptive inertia weight to improve the initial weight value and threshold value of BP neural network. And we give the detailed improved process. At the end, simulation results show that the new algorithm can improve convergence rate and precision of prediction of BP neural network, which reduces the error of prediction. At the end, we use multimedia evaluation model to verify the new method's performance.

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Wind power forecasting based on principle component phase space reconstruction

Cited by 78 publications

References 22 publications

Forecast of daily output energy of wind turbine using sARIMA and nonlinear autoregressive models

Forecast of daily output energy of wind turbine using sARIMA and nonlinear autoregressive models

Current status of wind energy forecasting and a hybrid method for hourly predictions

An improved particle swarm optimization algorithm used for BP neural network and multimedia course-ware evaluation

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