Domestic hot water (DHW) heating accounts for up to 30% of average household energy use. Compared to gas fired water heater, Electric Water Heater (EWH) can be powered by renewable generation resources, thus making it a potential renewable heat option. Furthermore, over 50 million EWHs already exist in the United States, possessing about 50GWh energy storage capacity which can be utilized to incorporate intermittent renewable resources into the power systems. With various initiatives worldwide to decarbonize the energy systems, the adoption of EWHs, i.e., electrification of DHW heating, is expected to continue the rapid growth. Even with a combined effort from industry and academia, many commercial products with functionalities like monitoring and alerting lack the intelligence to optimize and perform predictive control with data; on the other hand, literature with refined models and simulations come short in incorporating real-time data and providing robust optimal controls under uncertainties in real-world settings. Presented in this thesis is an EWH Smart Scheduling and Control System, enabled by a closed loop of monitoring, forecasting, and controlling, which continuously adjusts schedule and controls based on real-time data and future uncertainties for various objectives. An inexpensive noninvasive water meter using Hall-Effect sensors is developed with about 97.8% accuracy during lab tests. The smart meter also has the capability to transmit data to the cloud through the LoRa network. Testing with an EWH dataset, predictions with uncertainties and robust Model Predictive Control (MPC) simulations are conducted on a DHW generation system with one EWH supplying a multi-unit apartment building. Results show the capability to anticipate DHW demand with an uncertainty interval covers up to 97% of the actual demand during the test days. The MPC simulation shows superior performance by reducing electricity cost up to 33.2% as well as maintaining a desired DHW temperature without affecting user comfort. Further, the flexibility of the system to alter load profile under different Demand Response (DR) programs are shown. Reductions in both average power and gross consumption can be achieved.