Technology Strategy, Smart SCADA, and AI-Based Analytics Implementation at City of Chino, CA

Buzz words in technology innovation and adoption abound, but remarkably few case studies have been presented in the water sector demonstrating the power of a technology strategy and the use of tools such as machine learning (ML) and artificial intelligence (AI)-based decision support systems, despite the availability of packages and platforms that can be implemented at water utilities. While there is much interest in innovation at utilities, there is often a perceived barrier that new technologies are too expensive, require too much time to implement, and have an unknown return on investment. Likewise, few case studies have been made available to demonstrate how these technologies can be implemented and what benefits they provide to the utility, making it more difficult to garner support from key decision makers. The City of Chino (CA) began a digital transformation journey starting in 2019 to improve access to information including maps, SCADA, hydraulic models, and various enterprise systems. As part of this project, a technology strategy was used to identify key objectives including creating a connected digital environment that could be easily expanded in future years. Likewise, the City identified the importance of building upon existing digital assets while creating the space for new applications to be incorporated in the future. Using the digital strategy as a guidepost, City staff and Hazen evaluated several vendor-driven technologies that included intelligent SCADA systems with advanced analytics and capability to incorporate their hydraulic model from Innovyze, the GIS maps from ESRI, their advanced metering infrastructure (AMI) data from Sensus and Neptune, their SCADA data, along with laboratory, maintenance, asset, and other enterprise data. The City selected a platform called GoAigua due to its ability to connect systems and processes together, bring artificial intelligence to analytics of SCADA, meter, and hydraulic data and providing real time visibility and operator access to their systems and processes. In its current state, the platform integrates production data, real-time demand analytics, sensor data, and distribution system data, among other sources, with a powerful AI-guided system to provide operators with information to support critical operational decision making. This presentation will cover the key aspects to the technology strategy, how the strategy influenced final vendor selection, and will discuss the implementation and final state of the platform. We will also provide information regarding challenges and strategies to manage those challenges from project start to completion.