Open-SBS: Smart Building Simulator

Abstract

With the rise of machine learning and deep learning techniques in recent years, a representative dataset has become an inspiring source of prediction and knowledge extraction. Furthermore, ambient intelligence environments have emerged as one of the most important research areas. Many complex tasks and challenges face this area to validate and evaluate field research. One of the major challenges in the smart building domain is the lack of a simulation tool capable of simulating complex contexts, executing semantic rules, and collecting representative datasets for experiments. To address these issues, we present in this paper a cross-platform, open-source Smart Building Simulator (Open-SBS). Open-SBS offer an opportunity for researchers in the field of Ambient Intelligence environments to simulate complex contexts and collect representative datasets, save and share their models of experiments. We propose a new simulation process that is divided into four distinct phases: designing, scenario creation, semantic rule addition, and data-set generation. On the System Usability Scale, we conducted a study to assess the ease of use of Open-SBS (SUS).