Having solved the data integration problem, we discuss how convergence of 4 technology vectors, namely Big Data, Artificial Intelligence, Cloud Computing, and Internet of Things (IoT) has, for the first time, enabled us to solve a class of problems previously deemed as unsolvable at massive scales. AI applications such as predictive maintenance, fraud detection, sensor network health, supply chain optimization, energy management, anti-money laundering, and customer engagement are among the set of problems that are now solvable at enterprise scale. This is possible, thanks to the platform that brings together all infrastructure, micro services, data sources and research and data scientist on the same platform, and in doing so improves the productivity of the development team by a factor of 10-100x. We will discuss the "Predictive Maintenance" problem applied to distribution networks, aircraft systems and oil and gas assets, "Inventory Optimization" problem solved in the manufacturing industry, and "Fraud Detection" in the electricity space as well as banking space.

As the building stock consumes 40% of the U.S. primary energy consumption, it is critically important to improve building energy efficiency. This involves reducing the total energy consumption of buildings, reducing the peak energy demand, and leveraging renewable energy sources, etc. To achieve such goals, hybrid energy supply has becoming popular, where multiple energy sources such as grid electricity, on-site fuel cell generators, solar, wind, and battery storage are scheduled together to improve energy efficiency.

Building stock consumes 40% of primary energy consumption in the United States. Among various types of energy loads in buildings, HVAC (heating, ventilation, and air conditioning) and EV (electric vehicle) charging are two of the most important ones and have distinct characteristics. HVAC system accounts for 50% of the building energy consumption and typically operates throughout the day, while EV charging is an emerging major energy load that is hard to predict and may cause spikes in energy demand. To maximize building energy efficiency and grid stability, it is important to address both types of energy loads in a holistic framework. Furthermore, on the supply side, the utilization of multiple energy sources such as grid electricity, solar, wind, and battery storage provides more opportunities for energy efficiency, and should be considered together with the scheduling of energy loads. In this paper, we present a novel model predictive control (MPC) based algorithm to co-schedule HVAC control, EV scheduling and battery usage for reducing the total building energy consumption and the peak energy demand, while maintaining the temperature within the comfort zone for building occupants and meeting the deadlines for EV charging. Experiment results demonstrate the effectiveness of our approach under a variety of demand, supply and environment constraints.