Smart Grid is gradually adopted by most of the countries. AMI (Advanced Metering Infrastructure) is one of the key enabler technologies which consists of smart meters, communication network and MDMS (Meter Data Management System). The timely information, for example consumption data and event logs are important for detection of NTL (non-technical loss) such as electricity theft and other customer malfeasance. It contributes up to 40% of revenue loss of utility companies in some countries throughout the world. The proposed detection mechanisms include energy balance method, smart meters in AMI, physical detection sensors and the AI (artificial intelligence) methods namely fuzzy logic and SVM (support vector machine). The problematic issue is that those mechanisms are merely implemented separately with dissatisfaction outcomes due to inherent limitations and weaknesses. In this paper, a novel design of smart metering system is developed as GUI-based NTL detection platform. A 3-tier design of detection algorithm is proposed to combine three mechanisms to complement each other for enhanced performance. The triangulation technique facilitates validation of detection result through cross verification from three sources of measurement data. Furthermore, the system also support better flexibility with built-in or external developed AI methods and user-friendly GUI-based platform to monitor and analyse NTL status in real time of power grid for revenue recovery.

Industrial 4.0 technology of cyber-physical system enables real-time monitoring, sensing and actuating of physical machinery for predictive maintenance that replaces the conventional labor-intensive approach. This paper presents the design and development of a universal, cost-effective and internet of thing (IoT)-based proof-of-concept prototype universal cyber-physical system (UniCPS) with a cloud platform with an open and modular-based design of three-tier system architecture. The prototype demonstrates promising precision and accuracy for predictive maintenance on a pilot use case with MAPE of 3.77%, and average RMSE of 0.50. Besides, real-time visualization and detection of anomaly were also demonstrated with a cloud-based solution. The maintenance alert sent out by the actuator serves to notify the authorized personnel immediately for corrective action. As an extension to this work, a wireless sensor network can be incorporated in future work to acquire various data from diverse locations to overcome the limitations of sensor data.

The Sustainable Development Goals of the United Nations strive to maximize development needs, while minimizing environmental deterioration, without jeopardizing the needs of future generations. Nevertheless, due to urbanization, the escalating trend in natural-resource use, particularly electricity and water, is currently a crucial challenge for sustainable development. One of the promising options is the smart home, which is an extension of building automation with smart characteristics in monitoring, analyzing, controlling, and cloud computing with networked smart devices. Due to the lack of appropriate infrastructure and conscious consumption, its global adoption in the construction industry remains low. We present a technical feasibility of a multi-functional experimental smart home to support the Sustainable Development Goals of the United Nations in terms of water and energy conservation. The layered architecture of the cloud platform with an application program interface enables seamless integration of heterogeneous smart-home technologies and data sources. Use cases demonstrated its capacity to conserve electrical energy and water resources in support of the United Nations’ Sustainable Development Goals. Aside from that, the smart home’s electricity self-consumption of at least three autonomy days was confirmed with zero emissions and electricity bills, and a reduced supply-water consumption.