AI data analytics for renewables
The New Energy industry is growing rapidly as governments and corporations take steps towards reducing our impact on the environment, and pursue more sustainable energy sources for the future.
These new renewable energy technologies produce billions of data points every minute from inbuilt IoT sensors, making data analytics highly complex and time consuming.
VROC’s Artitifical Intelligence Predictive Analytics platform provides producers of renewable energy with insights that enable them to;
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- Optimize renewable energy integration into existing power grids and networks
- Co2 emissions and omission advanced analytics
- AI predictive analytics for predictive maintenance on wind turbines and other critical assets
- Energy management analytics and optimisation insights
- Advanced predictive analytics for energy production forecasts with AI modelling
- Real time remote monitoring and predictive analytics for renewable energy infrastructure
- Renewable energy production optimization with big data AI analytics
Wind Energy ai analytics
Wind turbines whilst relatively new assets are often in remote off-shore or land locations, making breakdown-maintenance methods impractical and costly. The vast amounts of data produced by these modern assets makes it difficult for reliability teams to process rapidly, increasing the risk of failures and drops in production levels. AI Predictive Analytics provides advance warning for predictive maintenance with 5-10 day lead time for time to failure notifications, ensuring enough time to schedule maintenance.
The VROC platform also provides reliability engineers with the critical insights to assist in optimizing the performance and production of renewable power, assisting in the growth of this emerging renewable market.
AI Data Analytics for Solar Energy
Solar farms and the industrial use of solar panels is growing in popularity as a reliable source of alternative renewable energy.
AI Data Analytics is providing solar energy producers and industrial users with insights for;
- Forecasting energy production and demand to help plan and optimizeenergy integrations within existing power networks, grids and battery usage
- Monitor, report and analyse carbon offsets and emission tracking for compliance
- Predictive analytics for solar network performance, degradation and predictive maintenance
See our photovolataic panels case study
AI for Hydrogen
Hydrogen, the fuel of the future is an emerging industry, with corporations and governments investing vast amounts of money to get the industry established.
The Australian Renewable Energy Agency estimates that there will be a demand for 3.2 million tonnes of hydrogen by 2030. It’s possible to produce hydrogen gas or liquid through a number of different means (natural gas, coal, biomass, water) however ensuring the production method is sustainable, green and cost effective is of upmost priority.
Artificial Intelligence can play a big role in the optimisation of production, increasing reliability and bringing down overall costs.