FogHorn and Energia Communications are working together to help Japan’s Chugoku Electric Power, Co., Inc. to improve safety and operations at its hydroelectric facilities through the use of Internet of Things (IoT) solutions.

Chugoku Electric Power employs nearly 9200 workers across 114 hydroelectric power generation facilities in the Chugoku area. As a 24-hour operation, the implementation of IoT is helping the company address the labour shortage and streamline operations, while enabling the collection of advanced data through remote, real-time analytics which ultimately enhance decision making and safety. In addition, edge capabilities, powered by FogHorn Lightning, can detect early signs of equipment failures to quickly mitigate issues, reduce costs and drive business outcomes.

“We’re proud to help address the long-term operational, safety and compliance goals at Chugoku Electric Power through our partnership with Energia Communications,” said Yuta Endo, VP/GM of APAC Operation, Business Development at FogHorn. “Resulting from FogHorn’s strong business alliance with Energia Communications, we were enlisted to improve operations to drive sustainability and ensure green energy sources stay online by harnessing the power of IoT and edge capabilities.”

Energia Communications and FogHorn officially launched the demonstration test in 2017 and began operation in April 2021. Ahead of its completion, the test became an “industrial security advancement promotion cost subsidy” in 2020 by the Ministry of Economy, Trade and Industry (METI) of the Japanese government. Aimed to drive national developments for energy and environmental policy and safety and security, this test serves as a model for other utilities and organizations to deploy IoT and achieve greater operational efficiency and safe workplaces.

As a key component of the demonstration test, FogHorn integrated its Lightning™ Edge AI Platform into Energia Communications’ IoT solutions to automate power generators. The platform enables remote, real-time collection, storage and analysis of data, such as current temperature and vibration for equipment control and monitoring, which has historically been a non-timely and inefficient function requiring more costs. In addition, the utilization of data collected and processed at the edge delivers predictive maintenance capabilities and power generation forecasting to remotely address equipment vulnerabilities and enhance decision making in real-time.