Related papers: A Chaotic Approach on Solar Irradiance Forecasting
A sensor was developed to quantitatively measure perturbations which change the volume of a wave chaotic cavity while leaving its shape intact. The sensors work in the time domain by using either scattering fidelity of the transmitted…
Solar power harbors immense potential in mitigating climate change by substantially reducing CO$_{2}$ emissions. Nonetheless, the inherent variability of solar irradiance poses a significant challenge for seamlessly integrating solar power…
Forecasting load at the feeder level has become increasingly challenging with the penetration of behind-the-meter solar, as this self-generation (also called total generation) is only visible to the utility as aggregated net-load. This work…
The uncertainty associated with solar photo-voltaic (PV) power output is a big challenge to design, manage and implement effective demand response and management strategies. Therefore, an accurate PV power output forecast is an utmost…
Solar-flare forecasting has been extensively researched yet remains an open problem. In this paper, we investigate the contributions of elastic distance measures for detecting patterns in the solar-flare dataset, SWAN-SF. We employ a simple…
We introduce a new method for predicting sunspot number (SSN) that, based on successful back projections, can predict features of the SSN several solar cycles in advance. The method applies Fourier analysis to the annual SILSO SSN record,…
Context. The solar irradiance is known to change on time scales of minutes to decades, and it is suspected that its substantial fluctua- tions are partially responsible for climate variations. Aims. We are developing a solar atmosphere code…
During a solar flare, it is believed that reconnection takes place in the corona followed by fast energy transport to the chromosphere. The resulting intense heating strongly disturbs the chromospheric structure, and induces complex…
Solar wind classification is conducive to understand the physical processes ongoing at the Sun and solar wind evolution in the interplanetary space, and furthermore, it is helpful for early warning of space weather events. With rapid…
The spatial extension of active regions (ARs) of the Sun can vary from one case to the next. This is a problem when studying solar flares with Convolutional Neural Networks (CNNs) as they generally use input images of a fixed size.…
In Chinese building codes, it is required that residential buildings receive a minimum number of hours of natural, direct sunlight on a specified winter day, which represents the worst sunlight condition in a year. This requirement is a…
This work presents a robust framework for quantifying solar irradiance variability and forecastability through the Stochastic Coefficient of Variation (sCV) and the Forecastability (F). Traditional metrics, such as the standard deviation,…
Phase aberration is one of the main contributors to image degradation in ultrasound imaging. Normalized-Cross-Correlation (NCC) is one of the most extensively studied techniques to estimate the arrival delay error and the aberration…
We explore oscillations of the solar $^8$B neutrinos in the Earth in detail. The relative excess of night $\nu_e$ events (the Night-Day asymmetry) is computed as function of the neutrino energy and the nadir angle $\eta$ of its trajectory.…
The output of solar power generation is significantly dependent on the available solar radiation. Thus, with the proliferation of PV generation in the modern power grid, forecasting of solar irradiance is vital for proper operation of the…
Present-day semi-empirical models of solar irradiance (SI) variations employ spectra computed on one-dimensional atmosphere models (1D models) representative of various solar surface features to reconstruct SI changes measured on timescales…
The Challan instrument is a solar full-disk imaging spectroscopic telescope planned to be installed at three sites with a 120-degree longitudinal difference, enabling continuous 24-hour observations of the Sun. It will take data every 2.5…
Given the rarity of significant solar flares compared to smaller ones, training effective machine learning models for solar activity forecasting is challenging due to insufficient data. This study proposes using generative deep learning…
Solar contamination, due to moonlight and atmospheric scattering of sunlight, can cause systematic errors in stellar radial velocity (RV) measurements that significantly detract from the ~10cm/s sensitivity required for the detection and…
Reliable forecasting of Global Horizontal Irradiance (GHI) is essential for mitigating the variability of solar energy in power grids. This study presents a comprehensive benchmark of ten deep learning architectures for short-term (1-hour…