Soft computing applications in wind speed and power prediction for wind energy

Choudhary, Anurag; Upadhyay, K. G.; Tripathi, M. M.

doi:10.1109/poweri.2012.6479588

Cited by 9 publications

(2 citation statements)

References 6 publications

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“…Those methods collectively, are called soft computing, which aims to exploit tolerance for imprecision, uncertainty, and partial truth to progressively and adaptively solve practical problems [11]. Soft computing approaches have been successfully applied in various fields such as freight volume prediction [12], process control [13], and wind prediction [14]. Most of the current non-linear and emerging machine learning models, such as the hidden Markov model, support vector machine, and artificial neural network, can be classified as soft computing models.…”

Section: Introductionmentioning

confidence: 99%

A Study Concerning Soft Computing Approaches for Stock Price Forecasting

Shi

Zhuang

2019

Axioms

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Financial time-series are well known for their non-linearity and non-stationarity nature. The application of conventional econometric models in prediction can incur significant errors. The fast advancement of soft computing techniques provides an alternative approach for estimating and forecasting volatile stock prices. Soft computing approaches exploit tolerance for imprecision, uncertainty, and partial truth to progressively and adaptively solve practical problems. In this study, a comprehensive review of latest soft computing tools is given. Then, examples incorporating a series of machine learning models, including both single and hybrid models, to predict prices of two representative indexes and one stock in Hong Kong’s market are undertaken. The prediction performances of different models are evaluated and compared. The effects of the training sample size and stock patterns (viz. momentum and mean reversion) on model prediction are also investigated. Results indicate that artificial neural network (ANN)-based models yield the highest prediction accuracy. It was also found that the determination of optimal training sample size should take the pattern and volatility of stocks into consideration. Large prediction errors could be incurred when stocks exhibit a transition between mean reversion and momentum trend.

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Section: Introductionmentioning

confidence: 99%

A Study Concerning Soft Computing Approaches for Stock Price Forecasting

Shi

Zhuang

2019

Axioms

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“…In a case study conducted in Tasmania, Australia, the ANN model outperformed the Similar Days approach, exhibiting greater accuracy when evaluated based on the daily mean absolute percentage error (MAPE) [34]. Another research conducted in Prince Edward Island, Canada, concluded that ANN outperformed other techniques such as Fuzzy Predictor, Adaptive Neural Fuzzy Inference System, and Committee Machines in wind power prediction [43]. Short-term, medium-term, and long-term wind power prediction models have been developed using different methodologies.…”

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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Electricity Price Forecasting Using LSTM Network and K-Means Clustering by Considering the Effect of Wind Power Generation

Varanasi

Tripathi

2022

Lecture Notes in Electrical Engineering

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Soft computing applications in wind speed and power prediction for wind energy

Cited by 9 publications

References 6 publications

A Study Concerning Soft Computing Approaches for Stock Price Forecasting

A Study Concerning Soft Computing Approaches for Stock Price Forecasting

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

Electricity Price Forecasting Using LSTM Network and K-Means Clustering by Considering the Effect of Wind Power Generation

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