A Short-term Wind Forecasting Framework using Ensemble Learning for Indian Weather Stations

Khochare, Janavi; Rathod, Jash; Joshi, Chaitali; Laveti, Ramesh Naidu

doi:10.1109/inocon50539.2020.9298262

Cited by 4 publications

(1 citation statement)

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“…In [25], the authors used data cleaning and feature extraction techniques for power prediction. In [26], the authors used machine learning algorithms such as light gradient boosting machines (GBMs) and LSTM networks for short-term wind forecasting of weather stations in India and also aimed to enhance wind energy prediction accuracy, contributing to efficient renewable energy integration and management. The authors of [27] implemented an ensemble approach combining algorithms, namely, deep learning and gradient descent, for wind power forecasting and explored the model's effectiveness in improving forecasting accuracy and reliability.…”

Section: Introductionmentioning

confidence: 99%

Hybrid LSTM-Based Fractional-Order Neural Network for Jeju Island’s Wind Farm Power Forecasting

Ramadevi,

Kasi,

Bingi

2024

Fractal Fract

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Efficient integration of wind energy requires accurate wind power forecasting. This prediction is critical in optimising grid operation, energy trading, and effectively harnessing renewable resources. However, the wind’s complex and variable nature poses considerable challenges to achieving accurate forecasts. In this context, the accuracy of wind parameter forecasts, including wind speed and direction, is essential to enhancing the precision of wind power predictions. The presence of missing data in these parameters further complicates the forecasting process. These missing values could result from sensor malfunctions, communication issues, or other technical constraints. Addressing this issue is essential to ensuring the reliability of wind power predictions and the stability of the power grid. This paper proposes a long short-term memory (LSTM) model to forecast missing wind speed and direction data to tackle these issues. A fractional-order neural network (FONN) with a fractional arctan activation function is also developed to enhance generated wind power prediction. The predictive efficacy of the FONN model is demonstrated through two comprehensive case studies. In the first case, wind direction and forecast wind speed data are used, while in the second case, wind speed and forecast wind direction data are used for predicting power. The proposed hybrid neural network model improves wind power forecasting accuracy and addresses data gaps. The model’s performance is measured using mean errors and R2 values.

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Section: Introductionmentioning

confidence: 99%