To effectively reduce water waste and generate accurate predictions through an automated solution, multiple elements must seamlessly work together to support decision-making activities. Indeed, artificial intelligence can be an incredibly powerful tool in this field: it is possible to build a model to interpret large amounts of real-time and historical data from different environments and soils and to generate accurate predictions in real-time. Here it comes Helicon to be integrated into smart systems to improve water and energy saving.
How Streaming ML saved Christmas: a Helicon use case for Santa’s delivery optimization
This Christmas we teamed up with Santa to maximise the efficiency of sleigh-based delivery, for a safe and effortless gifts distribution worldwide. We immediately thought of using Helicon to monitor in real time sleigh and reindeers’ performance, leveraging data to dynamically adjust the gift-delivery strategy.
How IoT and AI can increase energy efficiency for large buildings
AI can definitely increase energy efficiency for buildings; applied to IoT sensors to collect real-time data, it suggests changes to avoid energy waste. By using AI-enabled algorithms, it can continuously monitor and adjust the energy usage of large buildings, as well as automatically adjust thermostats and lights in accordance with the occupancy of a particular space. Check out our solution to save energy in large buildings.
Industry application of Machine Learning: the Alcoplast use case
It’s no secret that the technologies that can help in building a value extraction process are numerous, and for this reason, choosing the right architecture can be difficult, expensive, and time-consuming. Let’s get deeper into Radicalbit’s latest platform – Helicon- application to chemical industry sector.
How to reduce energy waste with AI
Applying AI to real-time IoT data brings prompt advantages for companies, like a considerable reduction in energy and raw material waste.