Forecasting Solar Activity with Two Computational Intelligence Models (A Comparative Study)
Solar activity It is vital to accurately predict solar activity, in order to decrease the plausible damage of electronic equipment in the event of a large high-intensity solar eruption. Recently, we have proposed BELFIS (Brain Emotional Learning-based Fuzzy Inference System) as a tool for the forecasting of chaotic systems. The structure of BELFIS is designed based on the neural structure of fear conditioning. The function of BELFIS is implemented by assigning adaptive networks to the components of the BELFIS structure. This paper especially focuses on performance evaluation of BELFIS as a predictor by forecasting solar cycles 16 to 24. The performance of BELFIS is compared with other computational models used for this purpose, and in particular with adaptive neuro-fuzzy inference system (ANFIS).
Code (0)
등록된 구현이 없습니다.
Tasks
Vocal Bursts Valence PredictionSimilar Papers 제목 키워드 기반
Optimal activity and battery scheduling algorithm using load and solar generation forecasts
Energy usage optimal scheduling has attracted great attention in the power system community, where various methodologies have been proposed. However, in real-world applications, the optimal scheduling problems require re…
SchedulingA comparative study of non-deep learning, deep learning, and ensemble learning methods for sunspot number prediction
Solar activity has significant impacts on human activities and health. One most commonly used measure of solar activity is the sunspot number. This paper compares three important non-deep learning models, four popular de…
Deep LearningEnsemble LearningTowards Hybrid Embedded Feature Selection and Classification Approach with Slim-TSF
Traditional solar flare forecasting approaches have mostly relied on physics-based or data-driven models using solar magnetograms, treating flare predictions as a point-in-time classification problem. This approach has l…
feature selectionTime SeriesSolarSeer: Ultrafast and accurate 24-hour solar irradiance forecasts outperforming numerical weather prediction across the USA
Accurate 24-hour solar irradiance forecasting is essential for the safe and economic operation of solar photovoltaic systems. Traditional numerical weather prediction (NWP) models represent the state-of-the-art in foreca…
Solar Irradiance ForecastingThe geomagnetic storm and Kp prediction using Wasserstein transformer
The accurate forecasting of geomagnetic activity is important. In this work, we present a novel multimodal Transformer based framework for predicting the 3 days and 5 days planetary Kp index by integrating heterogeneous …
Time Series