Design Automation for Smart Building Systems

Abstract

This paper presents a platform-based design flow for smart buildings. The proposed flow maps high-level specifications of desired building applications to their physical implementations through three intermediate design platforms, namely the virtual device platform, the module platform, and the implementation platform.

Deep Reinforcement Learning Algorithms in Intelligent Infrastructure

“…Infrastructures 2019, 4, 52 16 of 24 Equivalent to the previous validation, there was a slight increment in accuracy with the introduction of deep reinforcement learning, although the values between different memory configurations were not very different (Figure 13): the threshold memory α did not have a great impact on the accuracy, although it peaked at a 0.25 value. Tables 7 and 8 show the root mean square error of the predicted values (Appendix A) against the real measurements for different values of the prediction memory γ across the 19,736 data measurements for the DRL-0M no memory and DRL-FM full memory configurations, respectively, at medium threshold memory (α = 0.5) and a medium learning gradient (β = 1 × 10 3 ).…”

“…The promise of intelligent infrastructure extends far beyond energy efficiency or house comfort services, and the IoT will enable radical changes similar to the ones brought by the internet [51]: cloud integration is democratizing the IoT in intelligent infrastructure to include more complex functionality at a reduced cost (however, it also provides additional issues, such as cybersecurity and privacy, that will be addressed). An IoT platform based implementation for design automatization in smart building systems reuses hardware and software on shared infrastructure to optimize design performance [52]: the methodology consists of a functional design layer with virtual device platforms, function templates and virtual devices; a module design layer with module platforms, virtual device templates, sensing modules and data analytics modules; and finally an implementation platform with building operation systems APIs and programme code run time. Digitalization will merge the intelligence of infrastructure, buildings and transport systems because technology and solutions based on IoT and AI are shared [53] to cover similar functionalities such as route optimization, parking management, accident detection or fare collection.…”

“…Infrastructures 2019, 4, 52 13 of 24 The intelligent infrastructure (Appendix B) consisted of a configuration of five sensor networks with one neuron per external sensor. There were 22 external sensor neurons in total connected to 5 manager neurons associated with energy consumption, indoor and outdoor temperature and humidity, respectively (Figure 11).…”

“…Infrastructures 2019, 4, 52 2 of 24 of cabling: this provides real time status data, therefore enabling proactive maintenance rather than a reactive approach, which is normally at higher cost. In addition to providing data connectivity, Wi-Fi and Bluetooth low energy (BLE) beacons can be used to collect infrastructure occupancy information [3]: these data are key for space management and assessments about usage.…”

Detecting and Diagnosing Incipient Building Faults Using Uncertainty Information from Deep Neural Networks

“…These soft faults, especially in their incipient phase, are hard to detect as their signatures are not generally obvious (due to their magnitudes) and are lurking under measurement/system noise or feedback control actions [10], [27]. Nevertheless, they will impact energy consumption, system performance, and maintenance costs adversely in the long-run if left undetected and unattended [14]. In addition, they can lead to costly maintenance and undesirable replacement operations.…”