Wind power plays a critical role in supporting Ethiopia's electricity generation, particularly during dry seasons when hydropower availability diminishes. This contribution becomes even more crucial for sustaining...
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Wind power plays a critical role in supporting Ethiopia's electricity generation, particularly during dry seasons when hydropower availability diminishes. This contribution becomes even more crucial for sustaining industrial growth. However, accurately estimating wind energy remains challenging due to its random and highly variable nature. Deep learning models for time series prediction have been employed to address this issue, and this study evaluates the effectiveness of long short-term memory (LSTM) and gated recurrent unit (GRU) models in predicting wind speed at Adama Wind Farm II in Ethiopia. Model performance was assessed using R2, RMSE, and MAE. GRU significantly outperformed LSTM across all metrics and forecasting horizons. For daily forecasting, GRU achieved an R2 of 0.9727, exceeding LSTM's 0.9062 by 7.34 %, with corresponding RMSE values of 0.0005 and 0.0018, respectively. Similarly, GRU surpassed LSTM's performance for weekly and monthly forecasting. In weekly forecasting, GRU achieved an R2 of 0.9969 compared to LSTM's 0.9930, with RMSE values of 0.0001 for both models. For monthly forecasting, GRU achieved an R2 of 0.9330 compared to LSTM's 0.9303, with corresponding RMSE values of 0.0019 for both models. MAE values also followed the same pattern, with GRU consistently demonstrating lower values than LSTM across all forecasting horizons. These findings advance renewable energy and wind power forecasting research, highlighting the adaptability and versatility of LSTM and GRU models for capturing wind behavior. Decision-makers can use these findings to optimize wind power production, enhance grid operation efficiency, and promote sustainable and cost-effective wind energy. LSTM and GRU models have emerged as powerful tools for precise wind speed and wind power forecasting, which are essential for modern wind energy planning systems. They lay the foundation for a more environmentally friendly and sustainable en
In this paper we examine the numerical approximation of the limiting invariant measure associated with Feynman–Kac formulae. These are expressed in a discrete time formulation and are associated with a Markov chain a...
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Big data is a concept that deals with large or complex data sets by using data analysis tools(e.g.,data mining,machine learning)to analyze information extracted from several sources *** data has attracted wide attenti...
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Big data is a concept that deals with large or complex data sets by using data analysis tools(e.g.,data mining,machine learning)to analyze information extracted from several sources *** data has attracted wide attention from academia,for example,in supporting patients and health professionals by improving the accuracy of decision-making,diagnosis and disease *** research aimed to perform a Bibliometric Performance and Network Analysis(BPNA)supported by a Scoping Review(SR)to depict the strategic themes,thematic evolution structure,main challenges and opportunities related to the concept of big data applied in the healthcare *** this goal in mind,4857 documents from the Web of science covering the period between 2009 to June 2020 were analyzed with the support of SciMAT *** bibliometric performance showed the number of publications and citations over time,scientific productivity and the geographic distribution of publications and research *** strategic diagram yielded 20 clusters and their relative importance in terms of centrality and *** thematic evolution structure presented the most important themes and how it changes over ***,we presented the main challenges and future opportunities of big data in healthcare.
Resilience is an ability of a system with which the system can adjust its activity to maintain its functionality when it is perturbed. To study resilience of dynamics on networks, Gao et al. [Nature (London) 530, 307 ...
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Resilience is an ability of a system with which the system can adjust its activity to maintain its functionality when it is perturbed. To study resilience of dynamics on networks, Gao et al. [Nature (London) 530, 307 (2016)] proposed a theoretical framework to reduce dynamical systems on networks, which are high dimensional in general, to one-dimensional dynamical systems. The accuracy of this one-dimensional reduction relies on three approximations in addition to the assumption that the network has a negligible degree correlation. In the present study, we analyze the accuracy of the one-dimensional reduction assuming networks without degree correlation. We do so mainly through examining the validity of the individual assumptions underlying the method. Across five dynamical system models, we find that the accuracy of the one-dimensional reduction hinges on the spread of the equilibrium value of the state variable across the nodes in most cases. Specifically, the one-dimensional reduction tends to be accurate when the dispersion of the node's state is small. We also find that the correlation between the node's state and the node's degree, which is common for various dynamical systems on networks, is unrelated to the accuracy of the one-dimensional reduction.
Deep learning (DL) and machine learning (ML) models have shown promise in drug response prediction (DRP), yet their ability to generalize across datasets remains an open question, raising concerns about their real-wor...
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In this paper we examine the numerical approximation of the limiting invariant measure associated with Feynman-Kac formulae. These are expressed in a discrete time formulation and are associated with a Markov chain an...
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Choosing the Fisher information as the metric tensor for a Riemannian manifold provides a powerful yet fundamental way to understand statistical distribution families. Distances along this manifold become a compelling...
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A major challenge in near-term quantum computing is its application to large real-world datasets due to scarce quantum hardware resources. One approach to enabling tractable quantum models for such datasets involves c...
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Gender imbalance in academia has been confirmed in terms of a variety of indicators, and its magnitude often varies from country to country. Europe and North America, which cover a large fraction of research workforce...
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