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Mohammad Barooni and Deniz Velioglu Sogut
The design and optimization of floating offshore wind turbines (FOWTs) pose significant challenges, stemming from the complex interplay among aerodynamics, hydrodynamics, structural dynamics, and control systems. In this context, this study introduces an...
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Qianyang Li and Xingjun Zhang
For time series forecasting, multivariate grey models are excellent at handling incomplete or vague information. The GM(1, N) model represents this group of models and has been widely used in various fields. However, constructing a meaningful GM(1, N) mo...
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Aymane Ahajjam, Jaakko Putkonen, Emmanuel Chukwuemeka, Robert Chance and Timothy J. Pasch
Local weather forecasts in the Arctic outside of settlements are challenging due to the dearth of ground-level observation stations and high computational costs. During winter, these forecasts are critical to help prepare for potentially hazardous weathe...
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Charalampos M. Liapis and Sotiris Kotsiantis
The use of deep learning in conjunction with models that extract emotion-related information from texts to predict financial time series is based on the assumption that what is said about a stock is correlated with the way that stock fluctuates. Given th...
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Nika Nizharadze, Arash Farokhi Soofi and Saeed Manshadi
Predicting the price gap between the day-ahead Market (DAM) and the real-time Market (RTM) plays a vital role in the convergence bidding mechanism of Independent System Operators (ISOs) in wholesale electricity markets. This paper presents a model to pre...
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Soumyashree Kar, Jason R. McKenna, Glenn Anglada, Vishwamithra Sunkara, Robert Coniglione, Steve Stanic and Landry Bernard
While study of ocean dynamics usually involves modeling deep ocean variables, monitoring and accurate forecasting of nearshore environments is also critical. However, sensor observations often contain artifacts like long stretches of missing data and noi...
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Gaurav Narkhede, Anil Hiwale, Bharat Tidke and Chetan Khadse
Day by day pollution in cities is increasing due to urbanization. One of the biggest challenges posed by the rapid migration of inhabitants into cities is increased air pollution. Sustainable Development Goal 11 indicates that 99 percent of the world?s u...
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Ashish Sedai, Rabin Dhakal, Shishir Gautam, Anibesh Dhamala, Argenis Bilbao, Qin Wang, Adam Wigington and Suhas Pol
The Machine Learning/Deep Learning (ML/DL) forecasting model has helped stakeholders overcome uncertainties associated with renewable energy resources and time planning for probable near-term power fluctuations. Nevertheless, the effectiveness of long-te...
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Nikolaos Stasinos, Anestis Kousis, Vangelis Sarlis, Aristeidis Mystakidis, Dimitris Rousidis, Paraskevas Koukaras, Ioannis Kotsiopoulos and Christos Tjortjis
The impact of COVID-19 and the pressure it exerts on health systems worldwide motivated this study, which focuses on the case of Greece. We aim to assist decision makers as well as health professionals, by estimating the short to medium term needs in Int...
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Fatma Yaprakdal and Merve Varol Arisoy
In the smart grid paradigm, precise electrical load forecasting (ELF) offers significant advantages for enhancing grid reliability and informing energy planning decisions. Specifically, mid-term ELF is a key priority for power system planning and operati...
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