Paper Title
Using Digital Twins, IOT, and Multivariate Anomaly Detection for Smart Energy Consumption for Intelligent Buildings

Nowadays, energy is one of the most critical and expensive resources worldwide. Intelligent energy is one of the areas growing continuously because of the energy cost and demand for the environmentally friendly industry. One of the most critical questions in modern technology is how to build cost-effective modern intelligent solutions for managing energy consumption for buildings. The energy consumption of modern buildings is a complex system, dependent on many parameters. It is critical to find a formal approach to model and analyze such a system because the custom implementation of such a solution usually is quite expensive. One possible option is to empower analysis using the Digital Twins concept and analyze possible anomalies regarding the expected parameters using the multivariate anomaly detector. These systems are also real Internet of Things (IoT) solutions because such solutions gather data from sensors mounted in building spaces. The modern industry allows using cloud-based components such as Azure IoT Suite, Azure Digital Twins, and Azure Anomaly Detector service, offered by Microsoft, to simplify the implementation of complex analytics systems related to anomaly detection and predictive analytics. This paper proposes a conceptual framework for how to build intelligent solutions for energy consumption management in the building industry. The research overviews several popular technical solutions to implement such systems with pros and cons. A sample prototype is created based on the proposed framework and Microsoft Azure components for IoT, Digital Twins, and Anomaly Detection, which demonstrate how to manage the complex system's energy consumption using multivariate anomaly detection. Keywords - Digital Twin, IOT, Industry 4.0, Microsoft Azure, Azure Iot Stack, Anomaly Detector, Predictive Analytics, Smart Energy, Intelligent Buildings