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

Protocol-free asynchronous iterations termination

In this paper, we tackled the convergence detection problem arisen from the absence of synchronization during asynchronous iterative computation. We showed that, when one arbitrarily takes the local components of a global solution vector, an upper bound can be established on the difference between a residual error evaluated from this global vector and the inconsistent residual error evaluated without synchronizing the involved computing processes. This allows for accurate termination of asynchronous iterations without implementing any particular detection protocol. Termination delay has be handled too for not slowing down the overall asynchronous solver, by appropriately setting the convergence threshold criterion. We therefore ensured effectiveness while reaching better efficiency in terms of overall execution time of the solver, in comparison with the current most efficient exact snapshot-based approach. © 2020 Elsevier Lt

Asynchronous substructuring method with alternating local and global iterations

Until now, almost all investigations of asynchronous iterations within domain decomposition frameworks targeted methods of the parallel Schwarz type. A first, and sole, attempt to deal with a primal substructuring framework resulted in an asynchronous substructuring method where relaxation occurs simultaneously on the subdomains and on the interface between them, which therefore corresponds to a substructured relaxation scheme defined on the whole global domain. In this paper, we propose a Gauss–Seidel kind of improvement consisting of alternating between relaxation on the interface and relaxation on the subdomains, hence, always using the latest solutions in the subdomains when updating the solution on the interface, which is feasible at no additional cost. It turns out that one particular case of our general alternating relaxation scheme corresponds to an asynchronous substructuring method with iterations fully defined on the subdomains’ interface, and where only local Schur complements are involved. Practical performance evaluation on both standard Poisson's and linear elasticity problems has been conducted using a multi-node parallel computational platform with up to 720 CPU cores. © 2021 The Author(s

Convergence analysis of Schwarz methods without overlap for the Helmholtz equation

International audienceIn this paper, the continuous and discrete optimal transmission conditions for the Schwarz algorithm without overlap for the Helmholtz equation are studied. Since such transmission conditions lead to non-local operators, they are approximated through two different approaches. The first approach, called optimized, consists of an approximation of the optimal continuous transmission conditions with partial differential operators, which are then optimized for efficiency. The second approach, called approximated, is based on pure algebraic operations performed on the optimal discrete transmission conditions. After demonstrating the optimal convergence properties of the Schwarz algorithm new numerical investigations are performed on a wide range of unstructured meshes and arbitrary mesh partitioning with cross points. Numerical results illustrate for the first time the effectiveness, robustness and comparative performance of the optimized and approximated Schwarz methods on a model problem and on industrial problems

Parallel scientific computing

353 p. : ill. ; 24 cm

Asynchronous iterations of HSS method for non-Hermitian linear systems

A general asynchronous alternating iterative model is designed, for which convergence is theoretically ensured both under classical spectral radius bound and, then, for a classical class of matrix splittings for (Formula presented.) -matrices. The computational model can be thought of as a two-stage alternating iterative method, which well suits to the well-known Hermitian and skew-Hermitian splitting (HSS) approach, with the particularity here of considering only one inner iteration. Experimental parallel performance comparison is conducted between the generalized minimal residual (GMRES) algorithm, the standard HSS and our asynchronous variant, on both real and complex non-Hermitian linear systems, respectively, arising from convection–diffusion and structural dynamics problems. A significant gain on execution time is observed in both cases. © 2021 Informa UK Limited, trading as Taylor & Francis Group