Wind Speed Forecasting Methods for Wind Energy Generation

Tiwari, Seemant

doi:10.1109/ici53355.2022.9786880

Cited by 7 publications

(3 citation statements)

References 24 publications

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“…This forecasting is vital for optimizing wind power generation and has significant applications in various industries, including aviation, marine, environmental sciences, and electricity production (Charakopoulos, Karakasidis et al, 2019). While wind resources gain prominence for their renewable and sustainable characteristics, the challenge lies in wind speed variability, which is highly influenced by geographical and climatic conditions (Lipu et al, 2021;Tiwari, 2022). This variability can lead to irregular power generation, negatively affecting the quality and stability of electrical systems.…”

Section: Introductionmentioning

confidence: 99%

Wind speed prediction model based on DWT and Randon Forest

Santos,

Luiz Souza Monteiro,

Santos

et al. 2023

CLIUM

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Wind energy is one of the fastest power generation technologies in the power generation industry and one of the most cost-effective methods of generating electrical power. For system reliability, improving highly appropriate wind speed forecasting methods is desirable. The wavelet transform is a powerful mathematical technique that converts an analyzed signal into a time-frequency representation. This technique helps forecast non-stationary time series. The objective is to evaluate the performance of the "DWT-Random Forest" model in predicting wind speed through a comparative analysis of performance metrics (MSE, RMSE, and MAPE) with similar studies. Our motivation is rooted in the pressing need to improve wind forecasting methods to optimize renewable energy generation. The method involved implementing the model, which presented performance metrics: MSE of 0.0099, RMSE of 0.0996, and MAPE of 0.0779. However, comparative analysis with previous studies reveals that our model demonstrates competitive performance. The main result of this study is the finding that the "DWT-Random Forest" model exhibits a respectable performance in predicting wind speed, although there is room for improvement.

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Section: Introductionmentioning

confidence: 99%

Wind speed prediction model based on DWT and Randon Forest

Santos,

Luiz Souza Monteiro,

Santos

et al. 2023

CLIUM

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“…Estimating research in the field of renewable energy is extensive, with numerous scenarios and structures presented and examined regarding their efficacy for technological solutions. Wind energy production is the industry with the most suggested estimation techniques [2].…”

Section: Introductionmentioning

confidence: 99%

Implications of Machine Learning in Renewable Energy

Tiwari

2023

Ecp 2023

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“…Forecasting is a critical component of the electricity system. Forecasting systems are now used for both wind power generation (Seemant & Ling, 2021), and wind speed (Tiwari, 2022). Forecasting electrical load is also beneficial to the power grid and power corporations.…”

Section: Introductionmentioning

confidence: 99%

Electrical Load Forecasting Methodologies and Approaches

TIWARI¹

2022

EPSTEM

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Wind Speed Forecasting Methods for Wind Energy Generation

Cited by 7 publications

References 24 publications

Wind speed prediction model based on DWT and Randon Forest

Wind speed prediction model based on DWT and Randon Forest

Implications of Machine Learning in Renewable Energy

Electrical Load Forecasting Methodologies and Approaches

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