The flare likelihood and region eruption forecasting (FLARECAST) project: flare forecasting in the big data & machine learning era

The European Union funded the FLARECAST project, that ran from January 2015 until February 2018. FLARECAST had a research-to-operations (R2O) focus, and accordingly introduced several innovations into the discipline of solar flare forecasting. FLARECAST innovations were: first, the treatment of hundreds of physical properties viewed as promising flare predictors on equal footing, extending multiple previous works; second, the use of fourteen (14) different machine learning techniques, also on equal footing, to optimize the immense Big Data parameter space created by these many predictors; third, the establishment of a robust, three-pronged communication effort oriented toward policy makers, space-weather stakeholders and the wider public. FLARECAST pledged to make all its data, codes and infrastructure openly available worldwide. The combined use of 170+ properties (a total of 209 predictors are now available) in multiple machine-learning algorithms, some of which were designed exclusively for the project, gave rise to changing sets of best-performing predictors for the forecasting of different flaring levels, at least for major flares. At the same time, FLARECAST reaffirmed the importance of rigorous training and testing practices to avoid overly optimistic pre-operational prediction performance. In addition, the project has (a) tested new and revisited physically intuitive flare predictors and (b) provided meaningful clues toward the transition from flares to eruptive flares, namely, events associated with coronal mass ejections (CMEs). These leads, along with the FLARECAST data, algorithms and infrastructure, could help facilitate integrated space-weather forecasting efforts that take steps to avoid effort duplication. In spite of being one of the most intensive and systematic flare forecasting efforts to-date, FLARECAST has not managed to convincingly lift the barrier of stochasticity in solar flare occurrence and forecasting: solar flare prediction thus remains inherently probabilistic

Multiobjective combinatorial optimization: mathematical programming and application in energy planning and econometrics

This PhD Thesis comprises nine chapters. The present first chapter includes the introduction, the methodological framework, the object and the goals of the Dissertation. In the second chapter the problem of the MOCO is described and the research effort that has been done in this field. The third chapter is referred to the MOKPMC. At least the algorithm MCBB for the MOMCKP is described for the algorithms max-sort, lex-sort, ave-sort and relax-sum-round. In the fourth chapter the problem of synthesis, design and operation of combined heat and power for a hospital of Athens under uncertainty regarding fuel costs, interest rate and loads. The fifth chapter is dedicated to the computation of the maximum score estimator of Manski (Manski; 1975; Horowitz, 1993; Greene; 2003). In the next chapter the MOMSMP estimator is analysed in the present framework. In the seventh chapter the MOMCKP with additional continuous variables besides the 0-1 variables MIMOMCKP. The eigth chapter is referred to the SMS-UTA method which is an extension to the classic method UTA of (Siskos and Jackuete-Lagreze; 1982). Finally, in the last chapter the fundamental conclusions of the present work are presented in summary.Η διδακτορική διατριβή αποτελείται από εννέα κεφάλαια. Το παρόν πρώτο κεφάλαιο περιλαμβάνει την εισαγωγή. Στο δεύτερο κεφάλαιο περιγράφεται στο πρόβλημα της ΠΚΣΒ η μέχρι τώρα ερευνητική προσπάθεια που έχει μείνει στο πεδίο αυτό. Το τρίτο κεφάλαιο αναφέρεται στο ΠΠΣΠΠ. Συγκεκριμένα, παρουσιάζεται ο αλγόριθμος ΠΚΜΔΟ για το ΠΠΣΠΠ με τους ενσωματωμένους αλγόριθμους maximum-sort, lexicographic sort, average sort και relax sum round. Στο τέταρτο κεφάλαιο περιγράφεται το πρόβλημα σύνθεσης, σχεδιασμού και λειτουργίας της συμπαραγωγής ηλεκτρισμού θερμότητας και ψύξης για ένα νοσοκομείο της Αττικής υπό συνθηκές αβεβαιότητας ως προς τα κόστη καυσίμων, το επιτόκιο προεξόφλησης και τη ζήτηση ηλεκτρισμού – θερμότητας. Το πέμπτο κεφάλαιο είναι αφιερωμένο στον υπολογισμό του εκτιμητή maximum score estimator του Manski (Manski, 1975; Horowitz, 1993; Greene; 2003). Γίνεται εφαρμογή στη μελέτη για τη μετάβαση στην εργασία στην Ουάσινγκτον των ΗΠΑ τη δεκαετία του 1980. Στο έκτο κεφάλαιο παρουσιάζεται ο ΠΚΕΜΣ που προτείνεται στη διδακτορική διατριβή. Στο έβδομο κεφάλαιο περιγράφεται το ΠΠΣΠΠ με επιπλέον συνεχείς μεταβλητές εκτός από τις 0-1 μτβλ – ΜΑΠΠΣΠΠ. Στο όγδοο κεφάλαιο παρουσιάζεται η μέθοδος SMS-UTA επέκταση της κλασικής UTA. Τέλος, στο έννατο κεφάλαιο παρουσιάζεται συμπεράσματ