Cyber-Physical Systems Improving Building Energy Management: Digital Twin and Artificial Intelligence

Abstract

The research explores the potential of digital-twin-based methods and approaches aimed at achieving an intelligent optimization and automation system for energy management of a residential district through the use of three-dimensional data model integrated with Internet of Things, artificial intelligence and machine learning. The case study is focused on Rinascimento III in Rome, an area consisting of 16 eight-floor buildings with 216 apartment units powered by 70% of self-renewable energy. The combined use of integrated dynamic analysis algorithms has allowed the evaluation of different scenarios of energy efficiency intervention aimed at achieving a virtuous energy management of the complex, keeping the actual internal comfort and climate conditions. Meanwhile, the objective is also to plan and deploy a cost-effective IT (information technology) infrastructure able to provide reliable data using edge-computing paradigm. Therefore, the developed methodology led to the evaluation of the effectiveness and efficiency of integrative systems for renewable energy production from solar energy necessary to raise the threshold of self-produced energy, meeting the nZEB (near zero energy buildings) requirements.

Artificial Intelligence-Based Online Education System for University Music

“…You can access the information of other fields in the form by numbering. Table 7 shows the specific teacher user form information [13].…”

Assessing Responsive Building Envelope Designs through Robustness-Based Multi-Criteria Decision Making in Zero-Emission Buildings

“…In this article, a three-criteria robust design problem was addressed, but future studies could extend the analysis to tackle more than three performance indicators. Moreover, the work developed in this study could be developed even further and be integrated with artificial intelligence approaches as part of scenario modelling for digital twins and cyber-physical systems to evaluate the robustness of a system or identify its vulnerabilities [80]. Research Council of Norway.…”

Construction of the Mathematical Model for Teaching Classroom Evaluation in Colleges and Universities Using an Optimized Apriori Algorithm

“…where β d is the multi-port matching node of the effectiveness evaluation of college teaching classroom activities under the multi-dimensional feature distribution mode, D is the sample regression distribution set, and Trust b⟶c is the trust function of the effectiveness evaluation of college teaching classroom activities [14,15]. According to the spectrum feature decomposition, calculate the parameter matching feature quantity of the quality constraint parameters of college teaching classroom activities and get the standard normal distribution of β d .…”