Perturbed and non-perturbed brownian taboo processes

ABSTRACT. – In this paper we study the Brownian taboo process, which is a version of Brownian motion conditioned to stay within a finite interval, and the α-perturbed Brownian taboo process, which is an analogous version of an α-perturbed Brownian motion.We are particularly interested in the asymptotic behaviour of the supremum of the taboo process, and our main results give integral tests for upper and lower functions of the supremum as t →∞. In the Brownian case these include extensions of recent results in Lambert [4], but are proved in a quite different way. 2001 Éditions scientifiques et médicales Elsevier SAS AMS classification: 60K05; 60J15 RÉSUMÉ. – Dans cet article, nous étudions le processus Brownien tabou qui est une version du mouvement Brownien, conditionné à rester dans un intervalle fini, et le processus Brownien tabou α-perturbé qui est une version semblable du mouvement Brownien α-perturbé. Nous sommes particulièrement intéressés par le comportement asymptotique du supremum du processus tabou et nos principaux résultats fournissent des intégrales tests pour des fonctions majorantes et minorantes du supremum lorsque t → ∞. Dans le cas Brownien, ces résultats incluent des extensions de résultats récents de Lambert [4], mais ceux-ci sont prouvés de maníère différente. 2001 Éditions scientifiques et médicales Elsevier SAS 1

Machine learning for estimation of building energy consumption and performance:a review

Ever growing population and progressive municipal business demands for constructing new buildings are known as the foremost contributor to greenhouse gasses. Therefore, improvement of energy eciency of the building sector has become an essential target to reduce the amount of gas emission as well as fossil fuel consumption. One most eective approach to reducing CO2 emission and energy consumption with regards to new buildings is to consider energy eciency at a very early design stage. On the other hand, ecient energy management and smart refurbishments can enhance energy performance of the existing stock. All these solutions entail accurate energy prediction for optimal decision making. In recent years, articial intelligence (AI) in general and machine learning (ML) techniques in specic terms have been proposed for forecasting of building energy consumption and performance. This paperprovides a substantial review on the four main ML approaches including articial neural network, support vector machine, Gaussian-based regressions and clustering, which have commonly been applied in forecasting and improving building energy performance