# Novel Internet of Things Platform for In-Building Power Quality Submetering
## 6 Conclusions and Future Work  
Uninterrupted operations and business continuity are key requirements for any highly automated building located under the Industry 4.0 paradigm, for which Power Quality plays an extremely important role. Power Quality is a complex domain, covering over a dozen problem areas. Early detection and analysis of such Power Quality disturbances are essential for improving the quality of service and enhancing the productivity of the system. At present, most energy-intensive buildings suffer from a certain degree from poor Power Quality, adopting partial solutions to each problem. However, there is no single solution. Relatively little attention has been focused on Power Quality. The advantage of the system presented is that sensor data could also be indicative of equipment performance regarding efficiency and Power Quality standards. With the current Internet of Things prototype, the user will be able to detect not only most Power Quality steady-state disturbances but events such as voltage sag, while also monitoring the energy consumption. To our knowledge, there does not exist any Internet of Things system devoted to Power Quality monitoring. This is very suitable for industrial or commercial facility submetering. Through the Cloud-based Internet of Things platform, the information can be accessed, analyzed, stored, and visualized through different devices in a user-friendly manner, avoiding the need for significant investment in proprietary software infrastructure typically required for other measurement systems. Future work can be done towards customizing Power Quality statistics that are relevant to a particular appliance or system to better forecast overall facility health. In fact, a mesh of Internet of Things Power Quality sensors and their raw measurement data can be aggregated to correlate and help identify sources of disturbances in the near future using machine-learning strategies. Those sensors can  
Figure 20: SEMI-F47-curve showing the sags programmed in the experiment. also be embedded into the appliance for tighter integration and surveillance. This could support higher understanding of the currently consumed electricity e.g., by the comparison between identical devices consumption, monitoring trends and conditions, or even further, allowing an optimized maintenance scheduling focused on preventative maintenance only when it is required. Moreover, the detection of Power Quality disturbance leads to the anticipation of faults with early-stage alerts based on on-time data streams. These data can be used to continuously track and diagnose ride-through capabilities within devices to withstand the fluctuations in the electrical supply, pro-actively and remotely identify and manage reliability issues, establishing whether the responsibility is from the distribution system or because of incidents inside customer premises, thereby boosting the ability to act and react in real-time, or near real-time, for the effective and efficient use of electricity. In the future, authors will make the prototype smarter from two points of view. At the initial level, algorithms might be designed and executed within the prototype to help in selecting only the relevant information from an energy point of view. At a higher level, on the Cloud side, authors would consider applying machine learning to exploit all the received information from the Internet of Things Power Quality sensors to discover possible disturbances and anomalous behavior. [PERSON] made a comprehensive review of the existing literature. [PERSON] address the conception, research, and design of the work presented here. [PERSON] and [PERSON] were involved in the development and integration of the system's components. [PERSON] guided the whole work, edited the language, and provided their comments on the manuscript. All authors contributed to writing and reviewing the paper. This research received no external funding.