Using TensorFlow for Analysis, Learning and Optimization of Net-Zero Homes Energy Data

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I-EMS Group Ltd (I-EMS) is an advanced software and analytics solution provider for optimal planning, operation and control of smart distribution grids with multiple distributed energy resources. I-EMS provides an energy management system (EMS) as a coherent solution for the distributed energy market, utilizing cutting-edge technologies in forecasting, network analysis, and energy trade. The EMS is an autonomous program connected to all sensors within physical buildings that monitor the generation and consumption of energy assets and their respective loads. An abundance of data are generated and available for use from the array of Internet-of-Things (IoT) sensors and control devices and can be used to improve the efficiency and energy performance of connected systems. The goal of this collaborative project is to add advanced data analytics and big data components to I-EMS Group's existing EMS infrastructure, which can be implemented quickly to improve system efficiency and lower energy usage for I-EMS customers. Utilizing a year of IoT sensor/device data from net-zero energy homes, I-EMS Group is collaborating with Professor Nooshin Beheshti and Seneca's School of Information Technology Administration and Security to design, develop and test AI-based algorithms and data mining methods and implement them into new energy profile optimization modules.