Day Ahead Wind Power Forecasting Using Complex Valued Neural Network

Çevik, Hasan Hüseyin; Acar, Yunus Emre; Çunkaş, Mehmet

doi:10.1109/sest.2018.8495637

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…Split-Real Backpropagation [5], [6], [11], [25], [30], [36], [37], [39], [43], [45]- [47], [55], [57], [64], [72], [79], [132]- [136] Fully Complex Backpropagation (CR) [8], [33]- [35], [51], [61], [63], [65], [66], [121], [137], [138] MLMVN [82], [86], [89]- [93], [95], [97]- [99], [101], [103], [104], [107], [111], [112], [139]- [142] Orthogonal Least Square [124], [125] Quarternion-based Backpropagation [54] Hebbian Learning [56] Complex Barzilai-Borwein Training Method [137] A. Gradient-based Ap...…”

Section: Error Propagation Methods Corresponding Publicationsmentioning

confidence: 99%

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A Survey of Complex-Valued Neural Networks

Bassey¹,

Qian²,

Li³

2021

Preprint

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Artificial neural networks (ANNs) based machine learning models and especially deep learning models have been widely applied in computer vision, signal processing, wireless communications, and many other domains, where complex numbers occur either naturally or by design. However, most of the current implementations of ANNs and machine learning frameworks are using real numbers rather than complex numbers. There are growing interests in building ANNs using complex numbers, and exploring the potential advantages of the so called complex-valued neural networks (CVNNs) over their real-valued counterparts. In this paper, we discuss the recent development of CVNNs by performing a survey of the works on CVNNs in the literature. Specifically, detailed review of various CVNNs in terms of activation function, learning and optimization, input and output representations, and their applications in tasks such as signal processing and computer vision are provided, followed by a discussion on some pertinent challenges and future research directions.

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Section: Error Propagation Methods Corresponding Publicationsmentioning

confidence: 99%

“…[162] Time Series Prediction [103], [139] Associative Memory [105], [116] Wind Prediction [30], [43], [148] Robotics [38] Traffic Signal Control (robotics) [46], [60] Spam Detection…”

Section: Applications Corresponding Publicationsmentioning

confidence: 99%

A Survey of Complex-Valued Neural Networks

Bassey¹,

Qian²,

Li³

2021

Preprint

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“…Additionally, they emphasised that this method can be applied to real-world scenarios. The authors proposed Gaussian processes integrated with numerical weather prediction (NWP) and complex-valued ANN for day-ahead wind power forecasting and examined their effectiveness in optimising wind energy generation and improving prediction accuracy [10,11].…”

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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“…The estimation error adversely affects the whole electrical system in term of both economic and lack of supply. Therefore, forecast errors are penalized by electricity management system and these errors also adversely affect the profitability of WPPs [2].…”

Section: Introductionmentioning

confidence: 99%

Daily Total Wind Energy Estimation by Using Weather Condition

Çevik¹

2018

Proceedings of the 5th International Conference on Innovation in Science and Technology

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Estimation of the generated wind power is one of the tasks for wind power plant (WPP) companies and these estimations are sent to electricity management system to schedule the electric power plants. There is a transaction trend from traditional carbon-based generation to renewable electricity generation due to their environmental impact. While the generation of energy by traditional methods can be easily controlled, it is not possible to control the generation from renewable sources, because it depends on nature. This uncertainty of the power which will be generated is a disturbing factor for the generation/consumption balance of electricity. In this study, the daily total wind energy estimation is presented for two wind power plants which are in Turkey. While humidity, air pressure, temperature, wind speed, wind direction, season and historical energy generations are selected the system inputs, daily generated wind energy is the output. Adaptive Neuro-Fuzzy Inference System (ANFIS) is considered as forecast method. The normalised mean absolute error (NMAE) values of WPP1 and WPP2 are found as 13.75% and 10.23%, respectively.

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Day Ahead Wind Power Forecasting Using Complex Valued Neural Network

Cited by 6 publications

References 12 publications

A Survey of Complex-Valued Neural Networks

A Survey of Complex-Valued Neural Networks

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

Daily Total Wind Energy Estimation by Using Weather Condition

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