Improvements on coronal hole detection in SDO/AIA images using supervised classification

We demonstrate the use of machine learning algorithms in combination with segmentation techniques in order to distinguish coronal holes and filaments in SDO/AIA EUV images of the Sun. Based on two coronal hole detection techniques (intensity-based thresholding, SPoCA), we prepared data sets of manually labeled coronal hole and filament channel regions present on the Sun during the time range 2011 - 2013. By mapping the extracted regions from EUV observations onto HMI line-of-sight magnetograms we also include their magnetic characteristics. We computed shape measures from the segmented binary maps as well as first order and second order texture statistics from the segmented regions in the EUV images and magnetograms. These attributes were used for data mining investigations to identify the most performant rule to differentiate between coronal holes and filament channels. We applied several classifiers, namely Support Vector Machine, Linear Support Vector Machine, Decision Tree, and Random Forest and found that all classification rules achieve good results in general, with linear SVM providing the best performances (with a true skill statistic of ~0.90). Additional information from magnetic field data systematically improves the performance across all four classifiers for the SPoCA detection. Since the calculation is inexpensive in computing time, this approach is well suited for applications on real-time data. This study demonstrates how a machine learning approach may help improve upon an unsupervised feature extraction method.Comment: in press for SWS

An Investigation of Weather Forecasting using Machine Learning Techniques

Customarily, climate expectations are performed with the assistance of enormous complex models of material science, which use distinctive air conditions throughout a significant stretch of time. In this paper, we studied  a climate expectation  strategy that uses recorded information from  numerous climate stations to prepare basic AI models, which can give usable figures about certain climate conditions for the not so distant future inside a brief  timeframe These conditions are frequently flimsy on account of annoyances of the climate framework, making the models give mistaken estimates.[1] The model are for the most part run on many hubs in an enormous High Performance Computing (HPC) climate which burns through a lot of energy.. The modes can be run on significantly less asset serious conditions. In this paper we describe that the sufficient to be utilized status of the workmanship methods. Moreover, we described that it is valuable to use the climate stations information from various adjoining territories over the information of just the region for which climate anticipating is being performed

Development of advanced algorithms to detect, characterize and forecast solar activities

Study of the solar activity is an important part of space weather research. It is facing serious challenges because of large data volume, which requires application of state-of-the-art machine learning and computer vision techniques. This dissertation targets at two essential aspects in space weather research: automatic feature detection and forecasting of eruptive events. Feature detection includes solar filament detection and solar fibril tracing. A solar filament consists of a mass of gas suspended over the chromosphere by magnetic fields and seen as a dark, ribbon-shaped feature on the bright solar disk in Hα (Hydrogen-alpha) full-disk solar images. In this dissertation, an automatic solar filament detection and characterization method is presented. The investigation illustrates that the statistical distribution of the Laplacian filter responses of a solar disk contains a special signature which can be used to identify the best threshold value for solar filament segmentation. Experimental results show that this property holds across different solar images obtained by different solar observatories. Evaluation of the proposed method shows that the accuracy rate for filament detection is more than 95% as measured by filament number and more than 99% as measured by filament area, which indicates that only a small fraction of tiny filaments are missing from the detection results. Comparisons indicate that the proposed method outperforms a previous method. Based on the proposed filament segmentation and characterization method, a filament tracking method is put forward, which is capable of tracking filaments throughout their disk passage. With filament tracking, the variation of filaments can be easily recorded. Solar fibrils are tiny dark threads of masses in Hα images. It is generally believed that fibrils are magnetic field-aligned, primarily due to the reason that the high electrical conductivity of the solar atmosphere freezes the ionized mass in magnetic field lines and prevents them from diffusing across the lines. In this dissertation, a method that automatically segments and models fibrils from Hα images is proposed. Experimental results show that the proposed method is very successful to derive traces of most fibrils. This is critical for determining the non-potentiality of active regions. Solar flares are generated by the sudden and intense release of energy stored in solar magnetic fields, which can have a significant impact on the near earth space environment (so called space weather). In this dissertation, an automated solar flare forecasting method is presented. The proposed method utilizes logistic regression and SVM (support vector machine) to forecast the occurrences of solar flares based on photospheric magnetic features. Logistic regression is used to derive the probabilities of solar flares occurrence, which are then fed to SVM for determining whether a flare will occur. Comparisons with existing methods show that there is an improvement in the accuracy of X-class solar flare forecasting. It is also found that when sunspot-group classification is combined with photospheric magnetic parameters, the performance of flare forecasting can be further lifted

An Assessment of PIER Electric Grid Research 2003-2014 White Paper

This white paper describes the circumstances in California around the turn of the 21st century that led the California Energy Commission (CEC) to direct additional Public Interest Energy Research funds to address critical electric grid issues, especially those arising from integrating high penetrations of variable renewable generation with the electric grid. It contains an assessment of the beneficial science and technology advances of the resultant portfolio of electric grid research projects administered under the direction of the CEC by a competitively selected contractor, the University of California’s California Institute for Energy and the Environment, from 2003-2014

Utilization Of Artificial Intelligence (AI) And Machine Learning (ML) in the Field of Energy Research

Many governments have committed to becoming carbon neutral by 2050. The main argument is that renewable resources are more eco-friendly than fossil fuels. However, the unpredictable nature of solar and wind power results in either excess or lack of energy generation. This article will evaluate the current machine-learning-based solutions for forecasting renewable energy demand and capacity. Many researchers have used machine learning (ML) to anticipate the amount of generated wind or solar energy. SVM, RNN, NN, and ELM are the most utilized algorithms. Prediction accuracy is improved through optimization (metaheuristics and evolution). These methods can forecast renewable energy for periods ranging from seconds to months. This article compares several ML methodologies and metaheuristic strategies and reviews the current state of research. The hybrid MLS outperforms the standalone optimizers. A more extensive data set for ANN, the introduction of NWP, and a shorter prediction timeframe are suggested as alternatives to Bayesian and random grid tuning. Further research on probabilistic predictions and mathematical relationships between inputs and outputs is needed to close the research gap

Cuban energy system development – Technological challenges and possibilities

This eBook is a unique scientific journey to the changing frontiers of energy transition in Cuba focusing on technological challenges of the Cuban energy transition. The focus of this milestone publication is on technological aspects of energy transition in Cuba. Green energy transition with renewable energy sources requires the ability to identify opportunities across industries and services and apply the right technologies and tools to achieve more sustainable energy production systems. The eBook is covering a large diversity of Caribbean country´s experiences of new green technological solutions and applications. It includes various technology assessments of energy systems and technological foresight analyses with a special focus on Cuba