A hybrid deep learning model with error correction for photovoltaic power forecasting

Zhang, Rongquan; Li, Gangqiang; Bu, Siqi; Kuang, Guowen; Wang, He; Zhu, Yu Cheng; Aziz, Saddam

doi:10.3389/fenrg.2022.948308

Cited by 7 publications

(4 citation statements)

References 51 publications

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“…This method has achieved significant success in various domains, with error correction codes among them. Deep learning-based error correction codes are employed in communication systems that contend with high levels of noise and signal degradation [85,86]. These techniques enhance error correction efficiency by automatically discerning data characteristics and patterns.…”

Section: B1 Deep Learning-based Error Correction Codesmentioning

confidence: 99%

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Machine Learning-Based Error Correction Codes and Communication Protocols for Power Line Communication: An Overview

Akinci,

Erdemir,

Zengin

et al. 2023

IEEE Access

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This study endeavors to investigate the effectiveness of machine learning-based methodologies in enhancing the performance and reliability of Power Line Communication (PLC) systems. PLC systems constitute a critical component within the domains of energy management, monitoring, and automation. The fundamental objective herein is to contribute significantly to the scholarly discourse by conducting a comprehensive review encompassing research investigations and practical applications documented in the extant literature. The primary motivation underpinning this research is predicated upon the necessity for a meticulous evaluation of machine learning techniques that hold the potential to enhance the efficacy and stability of PLC systems. The deployment of these techniques bears the promise of engendering heightened levels of efficiency across the spectrum of energy management, communication, and automation systems. Within this scholarly quest, the study posits a hypothesis: Machine learning-based methodologies possess the capacity to effect marked improvements in the performance and reliability of PLC systems. Methodological scrutiny is executed through a comprehensive evaluation of diverse machine learning techniques, including, but not limited to, deep learning, support vector machines, and random forests, facilitated by a series of empirical experiments and simulations. Empirical findings resoundingly corroborate the proposition, substantiating a significant enhancement in the operational performance of PLC systems when these machine learning methods are judiciously employed. In summation, this study assumes the role of a catalyst in exploring latent, untapped potential inherent within machine learning-based methodologies, customarily calibrated to resonate within the intricate matrix of PLC systems. The zenith of this rigorous investigation stands poised to illuminate the path toward transformative advancements in the domains of energy management, communication, monitoring, and automation systems. The findings contribute significantly to the academic discourse, offering a compass for future research inquiries and practical applications within this burgeoning and dynamic field. INDEX TERMSPower line communication, error correction codes, machine learning, transmission control protocols, communication protocols, power networks.

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Section: B1 Deep Learning-based Error Correction Codesmentioning

confidence: 99%

“…Deep learning-based error correction codes primarily operate on specialized neural networks for encoding and decoding [86]. These neural networks can be of different types, such as convolutional neural networks or recurrent neural networks.…”

Section: B1 Deep Learning-based Error Correction Codesmentioning

confidence: 99%

Machine Learning-Based Error Correction Codes and Communication Protocols for Power Line Communication: An Overview

Akinci,

Erdemir,

Zengin

et al. 2023

IEEE Access

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“…As a new form of power supply that effectively integrates distributed generations (DGs), microgrids (MGs) can promote the development of DGs and improve the consumption capacity of the grid for DGs (Zhang et al, 2022). The power quality in MGs is the key to determining whether MGs can operate stably.…”

Section: Introductionmentioning

confidence: 99%

Islanding detection method for microgrids based on CatBoost

et al. 2023

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The occurrence of unintentional islanding will seriously threaten the stable operation of a microgrid (MG). Therefore, detecting the islanding of an microgrid timely is an important premise to ensure the microgrid operates safely and stably. However, the problem of dead zone exists in the traditional islanding detection process because the threshold of various electrical feature quantities of the point of common coupling (PCC) cannot be determined effectively. To solve this problem, an islanding detection method based on CatBoost is proposed for an microgrid. The novelty of this method lies in two aspects: 1) To reduce the error brought by the electrical feature quantities with weak correlation in the process of islanding detection, an analysis method based on the Spearman correlation coefficient is used to extract the electrical feature quantities closely related to islanding detection. 2) To determine the threshold of the electrical feature quantities more accurately and reduce the dead zone of island detection, an integrated learning machine is used to dig out correlations between the electrical feature quantities and the operation of an microgrid. The performance of the proposed islanding detection method is verified based on the modified IEEE13-bus system. The results of the example verify that the proposed islanding detection can achieve higher detection accuracy in cases of grid-connected interference and line faults.

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“…In ref. [7], the authors explored the application of deep learning-based hybrid technologies in ultra-short-term PV power forecasting comprising a feature engineering module, a deep learning-based point prediction module, and an error correction module. A chained support vector regression (SVR) and principal component analysis (PCA) are implemented in ref.…”

Section: Introductionmentioning

confidence: 99%

Absorption of PV Power Prediction Errors with Headroom Control by Statistical, Machine Learning and Combined Models

Cui,

Jie,

Fang

et al. 2023

IEEJ Transactions Elec Engng

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Owing to the mass penetration of renewable energy (RE) in Japan, the replacement reserve for feed‐in tariff (RR‐FIT) was introduced in 2021. To promote the further introduction of RE, we attempted to provide reserve power value to the balancing market by controlling the headroom of photovoltaic (PV) power with a PV power plant as a research subject. At the planning stage, the information available is the predicted value, and PV prediction error always occur on the day. Therefore, in this research, we proposed an algorithm to determine the absorption of prediction errors headroom in PV output, thereby maximally reducing negative imbalance. In addition, we developed a statistical model, SVR model, and combined models to calculate and compare the number of imbalance spots and amount of imbalance. Accordingly, the proposed methods can significantly reduce imbalance compared to planning with predicted values, without setting the headroom of prediction errors. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

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A hybrid deep learning model with error correction for photovoltaic power forecasting

Cited by 7 publications

References 51 publications

Machine Learning-Based Error Correction Codes and Communication Protocols for Power Line Communication: An Overview

Machine Learning-Based Error Correction Codes and Communication Protocols for Power Line Communication: An Overview

Islanding detection method for microgrids based on CatBoost

Absorption of PV Power Prediction Errors with Headroom Control by Statistical, Machine Learning and Combined Models

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