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The rapid growth of the wind energy sector underscores the urgent need to optimize turbine operations and ensure effective maintenance through early fault detection systems. While traditional empirical and physics-based models offer…
The prediction of solar power generation is a challenging task due to its dependence on climatic characteristics that exhibit spatial and temporal variability. The performance of a prediction model may vary across different places due to…
The solar corona and young solar wind may be characterized by critical surfaces -- the sonic, Alfv\'en, and first plasma-$\beta$ unity surfaces -- that demarcate regions where the solar wind flow undergoes certain crucial transformations.…
Wind energy is becoming an increasingly crucial component of a sustainable grid, but its inherent variability and limited predictability present challenges for grid operators. The energy sector needs novel forecasting techniques that can…
The energy market relies on forecasting capabilities of both demand and power generation that need to be kept in dynamic balance. Today, when it comes to renewable energy generation, such decisions are increasingly made in a liberalized…
Local scaling and singularity properties of solar wind and geomagnetic time series were analysed using H\"{o}lder exponents $\alpha$. It was shown that in analysed cases due to multifractality of fluctuations $\alpha$ changes from point to…
Inspired by the concept of relativity of simultaneity used in the theory of special relativity, a new approach is proposed to simulate future solar wind conditions at any point in the inner solar system. An important distinctive feature of…
Solar energy is one of the most dependable renewable energy technologies, as it is feasible almost everywhere globally. However, improving the efficiency of a solar PV system remains a significant challenge. To enhance the robustness of the…
We investigate the properties of the ion sky maps produced by the Dual Ion Spectrometers (DIS) from the Fast Plasma Investigation (FPI). We have trained a convolutional neural network classifier to predict four regions crossed by the MMS on…
To model the structure and dynamics of the heliosphere well enough for high-quality forecasting, it is essential to accurately estimate the global solar magnetic field used as inner boundary condition in solar wind models. However, our…
Solar flares, as one of the most prominent manifestations of solar activity, have a profound impact on both the Earth's space environment and human activities. As a result, accurate solar flare prediction has emerged as a central topic in…
Solar flare forecasting can be realized by means of the analysis of magnetic data through artificial intelligence techniques. The aim is to predict whether a magnetic active region (AR) will originate solar flares above a certain class…
Solar forecasting from ground-based sky images has shown great promise in reducing the uncertainty in solar power generation. With more and more sky image datasets open sourced in recent years, the development of accurate and reliable deep…
The solar atmosphere is a dynamic environment, constantly evolving to form a wide range of magnetically dominated structures (coronal loops, spicules, prominences, etc.) which cover a significant percentage of the surface at any one time.…
Space weather predictions of the solar wind impacting Earth are usually first based on remote-sensing observations of the solar disc and corona, and eventually validated and/or refined with in-situ measurements taken at the Sun$-$Earth…
The properties of the solar wind, as measured in situ throughout the heliosphere, depend both on the characteristics of its coronal source and on the intrinsic processes governing its interplanetary evolution. Recently, radial and Parker…
Accurately representing surface weather at the sub-kilometer scale is crucial for optimal decision-making in a wide range of applications. This motivates the use of statistical techniques to provide accurate and calibrated probabilistic…
Remarkable progress has been made in understanding turbulent astrophysical plasmas in past decades including, notably, the solar wind and the interstellar medium. In the case of the solar wind, much of this progress has relied on in situ…
The ability to predict wind is crucial for both energy production and weather forecasting. Mechanistic models that form the basis of traditional forecasting perform poorly near the ground. In this paper, we take an alternative data-driven…
We present a new approach to study the properties of the sun. We consider small variations of the physical and chemical properties of the sun with respect to Standard Solar Model predictions and we linearize the structure equations to…