RNN-Based Main Transformer OLTC Control for SMR Integration into a High Renewable Energy Penetrated Grid

Oyando, Harold Chisano; Kanyolo, Timothy Ngumbi; Chang, Choong-koo

doi:10.1007/s42835-022-01354-2

Cited by 7 publications

(4 citation statements)

References 14 publications

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“…The Transformer is a neural network architecture based on self-attention mechanisms, particularly adept at handling longrange dependencies and parallelized computation (Oyando et al, 2023;Zhang et al, 2023). In the GRU-Transformer model, the Transformer network is employed to extract longterm dependencies and global associations within sequence data.…”

Section: Transformer Modelmentioning

confidence: 99%

ISSA-enhanced GRU-Transformer: integrating sports wisdom into the frontier exploration of carbon emission prediction

Jiang,

Liu,

et al. 2024

Front. Ecol. Evol.

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IntroductionCarbon neutrality has become a key strategy to combat global climate change. However, current methods for predicting carbon emissions are limited and require the development of more effective strategies to meet this challenge. This is especially true in the field of sports and competitions, where the energy intensity of major events and activities means that time series data is crucial for predicting related carbon emissions, as it can detail the emission patterns over a period of time.MethodIn this study, we introduce an artificial intelligence-based method aimed at improving the accuracy and reliability of carbon emission predictions. Specifically, our model integrates an Improved Mahjong Search Algorithm (ISSA) and GRU-Transformer technology, designed to efficiently process and analyze the complex time series data generated by sporting events. These technological components help to capture and parse carbon emission data more accurately.ResultsExperimental results have demonstrated the efficiency of our model, which underwent a comprehensive evaluation involving multiple datasets and was benchmarked against competing models. Our model outperformed others across various performance metrics, including lower RMSE and MAE values and higher R2 scores. This underscores the significant potential of our model in enhancing the accuracy of carbon emission predictions.DiscussionBy introducing this new AI-based method for predicting carbon emissions, this study not only provides more accurate data support for optimizing and implementing carbon neutrality measures in the sports field but also improves the accuracy of time series data predictions. This enables a deeper understanding of carbon emission trends associated with sports activities. It contributes to the development of more effective mitigation strategies, making a significant contribution to global efforts to reduce carbon emissions.

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Section: Transformer Modelmentioning

confidence: 99%

ISSA-enhanced GRU-Transformer: integrating sports wisdom into the frontier exploration of carbon emission prediction

Jiang,

Liu,

et al. 2024

Front. Ecol. Evol.

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“…Most notably, the computational cost is relatively high, which may not be practical for resource-constrained environments. Additionally, training and fine-tuning the model may require more specialized knowledge and computational resources, presenting challenges in practical applications (Oyando et al, 2023).…”

Section: Transformer-based Model For Carbon Neutrality Data Analysismentioning

confidence: 99%

Collaborative Application of Deep Learning Models for Enhanced Accuracy and Prediction in Carbon Neutrality Anomaly Detection

Wang,

Wang

et al. 2024

Journal of Organizational and End User Computing

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In the face of intensifying global climate change, carbon neutrality has emerged as a pivotal strategy to curb greenhouse gas emissions and confront the complexities associated with climate challenges. However, achieving carbon neutrality poses a formidable challenge: the identification and mitigation of anomalies within the carbon sequestration process. These anomalies can result in unintended carbon dioxide leakage, emissions, or system failures, thus jeopardizing the feasibility and resilience of carbon neutrality initiatives. This research introduces the ResNet-BIGRU-TPA network, an innovative model that integrates deep learning techniques with time series attention mechanisms. The primary focus centers on addressing the intricate task of anomaly detection within the realm of carbon offsetting, specifically aiming to enhance precision in identifying a wide array of complex anomalous events. Through rigorous experimental validation across four diverse datasets, the model has exhibited exceptional performance.

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“…Previous studies show that SMRs which are characterized by small electrical capacity modules have the ability to operate flexibly in grids for voltage and frequency stabilization [4]. This is because of their load-following capability whereby they can adjust their output to match the change in demand [5,6].…”

Section: Introductionmentioning

confidence: 99%

Microgrid-Based Small Modular Reactor for a High-Renewable-Energy Penetration Grid in Ghana

Asiamah,

Chang

2024

Energies

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Achieving the necessary energy balance entails the capacity to adapt both power supply and demand, which is known as flexible operation. At present, the Ghana National Commission on Culture depends on a combination of well-coordinated measures designed to uphold the system’s integrity when confronted with abnormal system conditions stemming from significant disturbances within the system. The set of coordinated measures comprises manual (controlled load shedding) and automatic (Special Protection Schemes, Under-Frequency Load Shedding, Over-Frequency Control Scheme, scheme against voltage collapse, etc.) steps to control important system parameters to keep the power system stable and cascading effects leading to major blackouts. Ghana’s system suffers from voltage and frequency degradation as a result of a mismatch of power supply and demand; to maintain grid stability and avoid power outages, a flexible operation is required for real-time supply and demand balancing. SMRs, with their load-following capability, can adjust their output to match the change in demand. This research proposes a microgrid-based design for a high-renewable-energy penetration grid in Ghana with the inclusion of an SMR for voltage stability.

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RNN-Based Main Transformer OLTC Control for SMR Integration into a High Renewable Energy Penetrated Grid

Cited by 7 publications

References 14 publications

ISSA-enhanced GRU-Transformer: integrating sports wisdom into the frontier exploration of carbon emission prediction

ISSA-enhanced GRU-Transformer: integrating sports wisdom into the frontier exploration of carbon emission prediction

Collaborative Application of Deep Learning Models for Enhanced Accuracy and Prediction in Carbon Neutrality Anomaly Detection

Microgrid-Based Small Modular Reactor for a High-Renewable-Energy Penetration Grid in Ghana

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