In the digital and green transitions, rapidly growing renewable energies are accumulating more and more data. Big data gives room to apply emerging data science to solve challenges in the energy sector. Offshore wind power receives accelerating attention due to its sufficient resources and cleanness. This paper uses data science, including statistical analysis and machine learning, to systematically ...

Accurate wind power forecasting plays a critical role in the operation of wind parks and the dispatch of wind energy into the power grid. With excellent automatic pattern recognition and nonlinear mapping ability for big data, deep learning is increasingly employed in wind power forecasting. However, salient realities are that in-situ measured wind data are relatively expensive and inaccessible and ...

Norway's Arctic region is rich in wind resources and developing wind energy in the region can promote a green transition and economic development. However, the region's unique topography with fjords and mountains and cold climate conditions make wind resource assessment, generation analysis, and power forecasting particularly challenging. The accumulation of wind data and the emergence of data ...

In this paper, we propose a novel data-driven approach for removing trends (detrending) from nonstationary, fractal and multifractal time series. We consider real-valued time series relative to measurements of an underlying dynamical system that evolves through time. We assume that such a dynamical process is predictable to a certain degree by means of a class of recurrent networks called Echo ...

The application of artificial neural networks with the involvement of a modified homogeneity factor to predict exergetic terms from combustive and/or mixing dynamics in a marine engine is considered in this study. This is a significant step since the mathematical formulation of exergy in combustion is complicated and even unconvincing due to the turbulent and highly nonlinear nature of the combustion ...

Biological data analysis is typically implemented using a deep pipeline that combines a wide array of tools and databases. These pipelines must scale to very large datasets, and consequently require parallel and distributed computing. It is therefore important to choose a hardware platform and underlying data management and processing systems well suited for processing large datasets. There are many ...

Variability in sea ice conditions, combined with strong couplings to the atmosphere and the ocean, lead to a broad range of complex sea ice dynamics. More in-situ measurements are needed to better identify the phenomena and mechanisms that govern sea ice growth, drift, and breakup. To this end, we have gathered a dataset of in-situ observations of sea ice drift and waves in ice. A total of 15 ...

We created and carried out a cross-sectional anonymous structured questionnaire on what motivates users of mobile health applications and wearables to share their collected health related data. The questionnaire was distributed online in English, French, and Norwegian. In addition, a flyer with information of where to locate the online questionnaire was distributed during a Swiss health conference. ...

Submarine landslides are prevalent on the modern-day seafloor, yet an elusive problem is constraining the timing of past slope failure. We present a novel age-dating technique based on perturbations to underlying gas hydrate stability caused by slide-impacted seafloor changes. Using three-dimensional (3-D) seismic data, we mapped an irregular bottom simulating reflection (BSR) underneath a submarine ...

Sports today are more competitive than ever, with an eye for extreme details. Small margins can differentiate between succeeding or failing. That is why it is essential for a team to pay attention to all these margins and use them to their advantage. Technology today plays a huge part in sports. With video cameras and sensors following the players’ every move, it is vital to use this technology ...