AI Powered Renewables
Data scientists for green energy assets, p2x applications, green hydrogen and electrolyser plants
Cost competitive Renewables with best locations
Find ideal locations for your renewable sources maximising return on investments, reach carbon emissions reduction goals faster and reduce the cost of each unit of renewable energy produced for your solar, wind and biogas applications. Scale up and optimise with higher costs efficiencies in comparison to fossil fuels.
Cost competitive renewables compared to fossil fuels
Reduce the cost of per unit of renewable energy produced through your solar generators, wind energy farms and biogas plants. Become cost competitive much faster in renewable hydrogen production, in comparison to the cost of fossil fuels. Dramatically reduce the cost of electrolytic hydrogen with reduction in material costs.
Identify ideal locations for your renewable plants
Leverage on the best renewable resources in the world, with ideal locations, for solar and wind energy plants, maximising sun-hours and ensuring minimised rainfall, resulting in the low-cost, high-capacity renewables that is essential for low-cost power-to-gas, hydrogen production, or other power-to-x applications with higher return on investments.
Demo plants for green hydrogen or other emerging applications
Data-driven, automated or semi-automated demo plants or pilot projects for further use of green hydrogen in oil & refinery industry to desulphurise fuels, as feed stock in chemicals or fertilisers manufacturing, energy efficient steel production through DRI, cement factories, and carbon neutral data centres. Production of synthetic fuels, natural gas (SNG), via electrolytic production of hydrogen, using renewable energy sources.
Low cost giga-watt scale and industrial electrolysers
Optimise and scale up electrolysis processes for industrial scale production of renewable hydrogen. Increase the capacity utilisation through high-efficiency electrolysis, thereby decreasing variable per unit costs of hydrogen production. Deliver bulk, low-cost and zero-carbon hydrogen through gigawatt-scale PEM electrolysis processes.
Cost efficient renewable storage solutions
Faster prototyping of storage solutions in the smallest space, cost-efficient transportation of hydrogen and establishing end-to-end hydrogen networks connected to the utilities grid, with an integrated value chain that includes industrial customers, hydrogen filling stations and end users.
High capacity utilisation and investor transparency
Identify system faults and restore failure faster with predictive diagnostic models. With higher capacity utilisation you can reduce the cost of running your power-to-x (p2x) applications and electrolysis plants, and also build innovative storage solutions for renewables. Get higher transparency for the banks, investment funds and asset owners with changing environmental, operational, trading, market, demand and supply data.
Low cost Transmission and Distribution
High Capacity Utilisation & Forecasting
Trading, Markets & Investments
Smart inter-connected green grids with deep analytics and edge AI
Through millions of data points acquired from inter-connected systems in your renewable plants and green hydrogen production facilities, monitor and predict with confidence. You can pro-actively optimise processes, deploy IIoT and IoT wireless networks, and create AI models for managing smart transmission and distribution operations. Integrate your financial and investment models with macro data and plant level micro-data for maximum return on investment.
AI driven, adaptive, pro-active process control optimization, plant floor intelligence, by integrating real-time operational data, and minimizing downtime with regards to industrial machinery at plants, logistics facilities, and construction sites. With number of skilled engineers declining, utilize digital technologies for optimization and greater sophistication in repair services for machinery and OEM products. Detect any unwanted machine behaviour using an anomaly detection process, which is an example of “unsupervised” machine learning. An algorithm learns the typical data patterns of normal machine behaviour based on historical data.
Machine Learning for Renewable Energy
Get intelligent machine learning models to drive efficient green energy solutions by integrating data from sensors, drones, laser scanners, robotics applications, virtual reality, augmented reality, IIoT platform, edge devices, cloud, IoT. Run the AI models to power up your Deep Analytics, Mobile Apps, Web Apps, Remote Plant Access, Process Control solutions.
Digital Transformation for Power-to-X Applications and Green Hydrogen
For renewable plants, industrial power-to-x applications and green hydrogen production, the prime markets are ammonia, oil refining, iron and steel making, liquids for aviation, feedstock for synthetic organic materials production (for example electrofuels or e-fuels that are part of a power-to-X strategy), but there are huge cost and efficiency barriers that need to be overcome. Hydrogen can help tackle many critical energy challenges and decarbonise a range of sectors, where it is proving difficult to meaningfully reduce emissions. In addition, it increases flexibility in power systems, help reduce curtailment in grids with a high share of variable renewable electricity. For the costs to be lowered, plants must run at a higher utilisation rate, have better predictive capabilities, and strike a balance with daily market prices.
With a digitally powered, reliable, sustainable, resilient and modern green energy infrastructure accelerate your climate change goals and achieve cost feasibility faster.