Novel application of Relief Algorithm in cascaded artificial neural network to predict wind speed for wind power resource assessment in India

Malik, Hitendra K.; Yadav, Amit Kumar; Márquez, Fausto Pedro Garcı́a; Pérez, Jesús María Pinar

doi:10.1016/j.esr.2022.100864

Cited by 13 publications

(3 citation statements)

References 128 publications

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“…These factors can have a detrimental impact on the quality and reliability of electric power, necessitating accurate forecasting of wind power generation to mitigate these negative effects [14]. Wind power forecasting plays a crucial role in maintaining power system reliability, reducing costs, and facilitating informed decision-making by government entities and policymakers [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Unleashing the power of artificial neural networks: accurate estimation of monthly averaged daily wind power at Adama wind farm I, Ethiopia

Woldegiyorgis,

Benti,

Habtemicheal

et al. 2024

Environ. Res. Commun.

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Wind power plays a vital role in the electricity generation of many countries, including Ethiopia. It serves as a valuable complement to hydropower during the dry season, and its affordability is crucial for the growth of industrial centers. However, accurately estimating wind energy poses significant challenges due to its random nature, severe variability, and dependence on wind speed. Numerous techniques have been employed to tackle this problem, and recent research has shown that Artificial Neural Network (ANN) models excel in prediction accuracy. This study aims to assess the effectiveness of different ANN network types in estimating the monthly average daily wind power at Adama Wind Farm I. The collected data was divided into three sets: training (70%), testing (15%), and validation (15%). Four network types, namely Feedforward Backpropagation (FFBP), Cascade Feedforward Backpropagation (CFBP), Error Backpropagation (EBP), and Levenberg–Marquardt (LR), were utilized with seven input parameters for prediction. The performance of these networks was evaluated using Mean Absolute Percentage Error (MAPE) and R-squared (R2). The EBP network type demonstrated exceptional performance in estimating wind power for all wind turbines in Groups GI, GII, and GIII. Additionally, all proposed network types achieved impressive accuracy levels with MAPE ranging from 0.0119 to 0.0489 and R2 values ranging from 0.982 to 0.9989. These results highlight the high predictive accuracy attained at the study site. Consequently, we can conclude that the ANN model’s network types were highly effective in predicting the monthly averaged daily wind power at Adama Wind Farm I. By leveraging the power of ANN models, this research contributes to improving wind energy estimation, thereby enabling more reliable and efficient utilization of wind resources. The findings of this study have practical implications for the wind energy industry and can guide decision-making processes regarding wind power generation and integration into the energy mix.

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Section: Introductionmentioning

confidence: 99%

Unleashing the power of artificial neural networks: accurate estimation of monthly averaged daily wind power at Adama wind farm I, Ethiopia

Woldegiyorgis,

Benti,

Habtemicheal

et al. 2024

Environ. Res. Commun.

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“…Hence prediction of wind speed and direction, 9 and 30 hours ahead is required for issuing TAFs. Previously many studies have attempted to the prediction of wind speed but they are not for the aviation sectors (Chen et al 2018;Khosravi et al 2018a;Liu et al 2018;Li and Jin 2018;Yu et al 2018;Demolli et al 2019;Navas et al 2019;Malik et al 2019;Santhosh et al 2019;Memarzadeh and Keynia 2020;Peng et al 2020;Liu et al 2020;Jiajun et al 2020;Liu et al 2021;Gupta et al 2021;Verma et al 2021;Malik et al 2022). They are mainly in renewable energy sectors.…”

Section: Introductionmentioning

confidence: 99%

An analysis of wind characteristics of Kolkata airport and prediction of wind speed and direction using a new machine learning technique

Naskar

2023

Preprint

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In this study, we have attempted to determine the wind characteristics of Netaji Subhas Chandra Bose International Airport (NSCBIA) and predict wind speed and direction 9, 30 hours ahead using a new machine learning (ML) technique. For this, we have collected METAR wind speed and direction data from the NSCBIA for the period 2016-2021. On analysis of the wind speed and direction data, it is observed that a substantial amount of calm wind exists in NSCBIA. The wind speed varies throughout the day with a maximum around 09-11 UTC. The maximum randomness in wind direction is observed around the 06-09 UTC. The maximum wind speed is observed during April-July. Minimum wind speed is observed in December and January. Generally, wind speed remains low during 23-00 UTC but for winter months (October-January) low wind speed is observed during 15-21 UTC. MAE, RMSE and SSE are low for SSA-LSTM in predicting the wind speed. MAE, RMSE, and SSE are higher in predicting wind direction but within tolerance limits owing to wind direction randomness.

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“…Therefore, to further improve the efficiency and accuracy of forecasting, researchers have started to develop hybrid models that combine WT and deep learning algorithms. These hybrid models are used to predict time series, such as wind [18], solar energy [19], water quality [20], nickel futures price [21], gold returns [22], and stock prices [23]. Exploring this approach, we propose a hybrid model that combines a Wavelet Transform (WT), a Convolutional Neural Network (CNN), and a Recurrent Neural Network (RNN) with a Gated Recurrent Unit (GRU) layer.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Approach of Wavelet Transform, Convolutional Neural Networks and Gated Recurrent Units for Stock Liquidity Forecasting

Houad¹,

Mestari²,

Bentaleb³

et al. 2022

IJACSA

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Stock liquidity forecasting is critical for investors, issuers, and financial market regulators. The object of this study is to propose a method capable of accurately predicting the liquidity of stocks. The few studies on stock liquidity forecasting have focused on single models such as Seasonal Auto-Regressive Integrated Moving Average with eXogenous factors, the nonlinear autoregressive network with exogenous input, and Deep Learning. A new trend in forecasting which attempts to combine several approaches is emerging at the moment. Inspired by this new trend, we propose a hybrid approach of Wavelet Transform, Convolutional Neural Networks, and Gated Recurrent Units to predict stock liquidity. Our model is tested on daily data of companies listed on the Casablanca Stock Exchange from 2000 to 2021. Its forecasting performances are evaluated based on the Mean Absolute Error, the Root Mean Square Error, the Mean Absolute Percentage Error, Theil's U statistic, and the correlation coefficient. Finally, the outperformance of the proposed model is confirmed by comparison with other reference forecasting models. This study contributes to the enrichment of the field of prediction of financial risks and can constitute a framework of analysis allowing to help the stakeholders of the financial markets to forecast the liquidity of the actions.

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Novel application of Relief Algorithm in cascaded artificial neural network to predict wind speed for wind power resource assessment in India

Cited by 13 publications

References 128 publications

Unleashing the power of artificial neural networks: accurate estimation of monthly averaged daily wind power at Adama wind farm I, Ethiopia

Unleashing the power of artificial neural networks: accurate estimation of monthly averaged daily wind power at Adama wind farm I, Ethiopia

An analysis of wind characteristics of Kolkata airport and prediction of wind speed and direction using a new machine learning technique

A Hybrid Approach of Wavelet Transform, Convolutional Neural Networks and Gated Recurrent Units for Stock Liquidity Forecasting

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