相关论文: The McNish and Lincoln Solar Activity Predictions:…

Context. Solar activity, dominated by the 11-year cyclic evolution, has been observed directly since 1610. Before that, indirect cosmogenic proxy data are used to reconstruct it over millennia. Recently, the precision of radiocarbon…

The minimum - maximum method, belonging to the precursor class of the solar activity forecasting methods, is based on a linear relationship between relative sunspot number in the minimum and maximum epochs of solar cycles. In the present…

Despite the known general properties of the solar cycles, a reliable forecast of the 11-year sunspot number variations is still a problem. The difficulties are caused by the apparent chaotic behavior of the sunspot numbers from cycle to…

The subject of this paper is the existence and stability of solar cycles with durations in the range of 20-250 years. Five types of data series are used: 1) The Zurich series (1749-2009 AD), the mean annual International sunspot number Ri,…

This work presents a set of optimal machine learning (ML) models to represent the temporal degradation suffered by the power conversion efficiency (PCE) of polymeric organic solar cells (OSCs) with a multilayer structure…

Solar based electricity generations have experienced strong and impactful growth in recent years. The regulation, scheduling, dispatching, and unit commitment of intermittent solar power is dependent on the accuracy of the forecasting…

Solar activity cycle varies in amplitude. The last Cycle 24 is the weakest in the past century. Sun's activity dominates Earth's space environment. The frequency and intensity of the Sun's activity are accordant with the solar cycle. Hence…

Detailed models of the solar cycle require information about the starting time and rise time as well as the shape and amplitude of the cycle. However, none of these models includes a discussion of the variations in the length of the cycle,…

With recent advances in the field of machine learning, the use of deep neural networks for time series forecasting has become more prevalent. The quasi-periodic nature of the solar cycle makes it a good candidate for applying time series…

Solar activity seems quite understandable when considered on the scales comparable with a solar cycle, i.e. about 11 years, and on a short time scale of about a year. A solar cycle looks basically (anti)symmetric with respect to the solar…

The combined Greenwich and Solar Optical Observing Network (SOON) sunspot group data during 1874-2013 are analyzed and studied the relatively long-term variations in the annual sums of the areas of sunspot groups in 0-10 deg, 10-20 deg, and…

There are many proposed prediction methods for solar cycles behavior. In a previous paper we updated the full-shape curve prediction of the current solar cycle 24 using a non-linear dynamics method and we compared the results with the…

To study and forecast the solar activity data a quite perspective method of singular spectrum analysis (SSA) is proposed. As known, data of the solar activity are usually presented via the Wolf numbers associated with the effective amount…

Using Greenwich and SOON sunspot group data during the period 1874 -- 2005, we find that the sums of the areas of the sunspot groups in $0^\circ$ -- $10^\circ$ latitude-interval of the Sun's northern hemisphere and in the time-interval,…

Solar flares create adverse space weather impacting space and Earth-based technologies. However, the difficulty of forecasting flares, and by extension severe space weather, is accentuated by the lack of any unique flare trigger or a single…

Using 55 years of daily average temperatures from a local weather station, I made a least-absolute-deviations (LAD) regression model that accounts for three effects: seasonal variations, the 11-year solar cycle, and a linear trend. The…

Sunspot numbers form a comprehensive, long-duration proxy of solar activity and have been used numerous times to empirically investigate the properties of the solar cycle. A number of correlations have been discovered over the 24 cycles for…

Sub-seasonal climate forecasting (SSF) focuses on predicting key climate variables such as temperature and precipitation in the 2-week to 2-month time scales. Skillful SSF would have immense societal value, in areas such as agricultural…

The growth rate of solar activity in the early phase of a solar cycle has been known to be well correlated with the subsequent amplitude (solar maximum). It provides very useful information for a new solar cycle as its variation reflects…

Numerical weather prediction (NWP) and machine learning (ML) methods are popular for solar forecasting. However, NWP models have multiple possible physical parameterizations, which requires site-specific NWP optimization. This is further…