Sensor Networks, South Africa’s leader in smart home and energy-management technology, has introduced a solar farm geyser management system built for large-scale residential developments.
As estates increasingly shift to solar power, the upgraded system gives developers real-time visibility and centralised control over thousands of geysers, reducing electricity costs, improving grid stability, and unlocking greater value from solar installations in our country.
“Large estates often experience synchronised geyser usage during morning and evening peaks, creating strain on PV and battery systems. Our solution installs a smart controller on every geyser, all connected to SensorDesk, our company’s cloud-based energy management platform. Developers can instantly switch off units during low-generation periods, automate heating schedules, and redirect excess solar into water heating using real-time data and advanced algorithms,” says Mark Allewell, CEO of Sensor Networks.
South Africa is moving rapidly toward solar-powered living, but without intelligent load management, developers carry the same problems from the grid into renewable systems: “If you can’t manage hot water at scale, you can’t stabilise or optimise a solar development. Our tech and system aims to solve that challenge, automatically and reliably.”
With the upgraded system, developers have real-time control over thousands of geysers – all at once. By directing surplus solar into water heating and protecting inverters during peak demand, the local tech company is giving developers a level of precision the industry has never had access to before.
More than 12,000 South African households already use Sensor Networks’ smart geyser technology, and over 2,000 homes in major developments run the solar farm system today. Developers report immediate efficiency gains, lower grid reliance, and dramatically improved operational control.
Sensor Networks is now preparing to scale the technology to tens of thousands of homes nationwide, with upcoming enhancements including predictive optimisation and AI-driven decision-making.