The integration of renewable energy sources into electricity markets presents significant challenges due to the inherent variability and uncertainty of power generation from wind,solar,and other renewables.Accurate fo...The integration of renewable energy sources into electricity markets presents significant challenges due to the inherent variability and uncertainty of power generation from wind,solar,and other renewables.Accurate forecasting is crucial for ensuring grid stability,optimizing market operations,and minimizing economic risks.This paper introduces a hybrid forecasting framework incorporating fractional-order statistical models,fractal-based feature enginering,and deep learning architectures to improve renewable energy forecasting accuracy.Fractional autoregressive integrated moving average(FARIMA)and fractional exponential smoothing(FETS)models are explored for capturing long-memory dependencies in energy time-series data.Additionally,multifractal detrended fluctuation analysis(MFDFA)is used to analyze the intermittency of renewable energy generation.The hybrid approach further integrates wavelet transforms and convolutional long short-term memory(CNN-LSTM)networks to model shortand long-term dependencies effectively.Experimental results demonstrate that fractional and fractal-based hybrid forecasting techniques significantly outperform traditional models in terms of accuracy,reliability,and adaptability to energy market dynamics.This research provides insights for market participants,policymakers,and grid operators to develop more robust forecasting frameworks,ensuring a more sustainable and resilient electricity market.展开更多
Public cloud computing provides a variety of services to consumersvia high-speed internet. The consumer can access these services anytimeand anywhere on a balanced service cost. Many traditional authenticationprotocol...Public cloud computing provides a variety of services to consumersvia high-speed internet. The consumer can access these services anytimeand anywhere on a balanced service cost. Many traditional authenticationprotocols are proposed to secure public cloud computing. However, therapid development of high-speed internet and organizations’ race to developquantum computers is a nightmare for existing authentication schemes. Thesetraditional authentication protocols are based on factorization or discretelogarithm problems. As a result, traditional authentication protocols arevulnerable in the quantum computing era. Therefore, in this article, we haveproposed an authentication protocol based on the lattice technique for publiccloud computing to resist quantum attacks and prevent all known traditionalsecurity attacks. The proposed lattice-based authentication protocolis provably secure under the Real-Or-Random (ROR) model. At the sametime, the result obtained during the experiments proved that our protocol islightweight compared to the existing lattice-based authentication protocols,as listed in the performance analysis section. The comparative analysis showsthat the protocol is suitable for practical implementation in a quantum-basedenvironment.展开更多
基金funded under research grant from the Research,Development,andInnovation Authority(RDIA),Saudi Arabia,grant No.13010-Tabuk-2023-UT-R-3-1-SE.
文摘The integration of renewable energy sources into electricity markets presents significant challenges due to the inherent variability and uncertainty of power generation from wind,solar,and other renewables.Accurate forecasting is crucial for ensuring grid stability,optimizing market operations,and minimizing economic risks.This paper introduces a hybrid forecasting framework incorporating fractional-order statistical models,fractal-based feature enginering,and deep learning architectures to improve renewable energy forecasting accuracy.Fractional autoregressive integrated moving average(FARIMA)and fractional exponential smoothing(FETS)models are explored for capturing long-memory dependencies in energy time-series data.Additionally,multifractal detrended fluctuation analysis(MFDFA)is used to analyze the intermittency of renewable energy generation.The hybrid approach further integrates wavelet transforms and convolutional long short-term memory(CNN-LSTM)networks to model shortand long-term dependencies effectively.Experimental results demonstrate that fractional and fractal-based hybrid forecasting techniques significantly outperform traditional models in terms of accuracy,reliability,and adaptability to energy market dynamics.This research provides insights for market participants,policymakers,and grid operators to develop more robust forecasting frameworks,ensuring a more sustainable and resilient electricity market.
基金Korean Government (Ministry of Science and ICT)through the National Research Foundation of Korea (NRF)Grant 2021R1A2C1010481.
文摘Public cloud computing provides a variety of services to consumersvia high-speed internet. The consumer can access these services anytimeand anywhere on a balanced service cost. Many traditional authenticationprotocols are proposed to secure public cloud computing. However, therapid development of high-speed internet and organizations’ race to developquantum computers is a nightmare for existing authentication schemes. Thesetraditional authentication protocols are based on factorization or discretelogarithm problems. As a result, traditional authentication protocols arevulnerable in the quantum computing era. Therefore, in this article, we haveproposed an authentication protocol based on the lattice technique for publiccloud computing to resist quantum attacks and prevent all known traditionalsecurity attacks. The proposed lattice-based authentication protocolis provably secure under the Real-Or-Random (ROR) model. At the sametime, the result obtained during the experiments proved that our protocol islightweight compared to the existing lattice-based authentication protocols,as listed in the performance analysis section. The comparative analysis showsthat the protocol is suitable for practical implementation in a quantum-basedenvironment.
