Related papers: Forecasting Solar Cycle 25 using Deep Neural Netwo…
We create a continuous series of daily and monthly hemispheric sunspot numbers (HSNs) from 1874 to 2020, which will be continuously expanded in the future with the HSNs provided by SILSO. Based on the available daily measurements of…
In the previous study (Hiremath 2006a), the solar cycle is modeled as a forced and damped harmonic oscillator and from all the 22 cycles (1755-1996), long-term amplitudes, frequencies, phases and decay factor are obtained. Using these…
We consider the flare prediction problem that distinguishes flare-imminent active regions that produce an M- or X-class flare in the future 24 hours, from quiet active regions that do not produce any flare within $\pm 24$ hours. Using…
This note deals with a multivariate stochastic approach to forecast the behaviour of a cyclic time series. Particular attention is devoted to the problem of the prediction of time behaviour of sunspot numbers for the current 23th cycle. The…
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,…
A review of solar cycle prediction methods and their performance is given, including forecasts for cycle 24 and focusing on aspects of the solar cycle prediction problem that have a bearing on dynamo theory. The scope of the review is…
Effective utilization of photovoltaic (PV) plants requires weather variability robust global solar radiation (GSR) forecasting models. Random weather turbulence phenomena coupled with assumptions of clear sky model as suggested by Hottel…
We investigate solar activity by focusing on double maxima in solar cycles and try to estimate the shape of the current solar cycle (Cycle 24) during its maximum. We analyzed data for Solar Cycle 24 by using Learmonth Solar Observatory…
Forecasting future solar activity has become crucial in our modern world, where intense eruptive phenomena mostly occurring during solar maximum are likely to be strongly damaging to satellites and telecommunications. We present a 4D…
Forecasting the solar cycle amplitude is important for a better understanding of the solar dynamo as well as for many space weather applications. We demonstrated a steady relationship between the maximal growth rate of sunspot activity in…
The linear relationship between the maximum amplitudes (R$_{max}$) of sunspot cycles and preceding minima (R$_{min}$) is one of the precursor methods used to predict the amplitude of the upcoming solar cycle. In the recent past this method…
The slopes of the linear relations between sunspot and white light (WL) facular areas at the onset of sunspot Cycles 12-21 correlate well with the amplitudes of those cycles between 1878-1980 (Brown and Evans, 1980). We use continuum images…
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…
For short-term solar irradiance forecasting, the traditional point forecasting methods are rendered less useful due to the non-stationary characteristic of solar power. The amount of operating reserves required to maintain reliable…
The main purpose of this study is the determination of solar minimum date of the new sunspot cycle No 24. It is provided by using of four types of mean daily data values for the period Jan 01. 2006 - Dec 31. 2009: (1) the solar radioindex…
This paper proposes an improved deep learning based maximum power point tracking (MPPT) in solar photovoltaic cells considering various time series based environmental inputs. Generally, artificial neural network based MPPT algorithms use…
We attempt to forecast the Sun's sunspot butterfly diagram in both space (i.e. in latitude) and time, instead of the usual one-dimensional time series forecasts prevalent in the scientific literature. We use a prediction method based on the…
The study of variations in solar activity is important for understanding the underlying mechanism of solar activity and for predicting the level of activity in view of the activity impact on space weather and global climate. Here we have…
The use of different solar activity indices like sunspot numbers, sunspot areas, flare index, magnetic fields, etc., allows us to investigate the time evolution of some specific features of the solar activity and the underlying dynamo…
Deep learning models have gained increasing prominence in recent years in the field of solar pho-tovoltaic (PV) forecasting. One drawback of these models is that they require a lot of high-quality data to perform well. This is often…