An optimal surrogate-model-based approach to support comfortable and nearly zero energy buildings design

Abstract

The shift from conventional buildings to the so-called Nearly Zero Energy Buildings (NZEBs) is becoming one of the major contemporary challenges in the world. In this work, a multi-objective optimization approach, based on a smart surrogate model, has been developed to minimize the energy consumption, improve the thermal comfort of the occupants and increase the energy self-sufficiency of residential buildings. For this purpose, two main phases have been considered: the first one is related to the development of the surrogate model, based on machine learning utilities, in particular Artificial Neural Networks (ANNs), and the second is related to the optimization process, performed by means of the Multi-Objective Particle Swarm Optimization algorithm (MOPSO). This approach has been applied to a typical Moroccan building, Ground Floor + First Floor (GFFF), in different regulatory climate zones. The results show that the approach was successfully implemented using TRNSYS, Matlab and other numerical simulation tools, leading to different solutions in terms of building envelope design. The best-fit solution achieved a huge improvement potential in most climate zones, averaging about 75%, 50% and 85% respectively for energy consumption, thermal comfort and energy self-sufficiency of the studied building. Finally, we strongly recommend this approach to the various stakeholders in this field, including designers, engineers, architects, consulting firms, etc., since the results have proven its effectiveness as a very promising step towards designing Comfortable and Nearly Zero Energy Buildings. Future work will focus on the implementation of a hardware device that is able to perform all the steps of the proposed framework for possible pre-project optimizations.