The stable supply and management of the grid has become more complex with renewable generation sources and a commitment to reduce emissions without rising costs.
Utilities operators and energy generators are faced with the challenge to manage grid reliability, increase adoption of clean energy, and minimize costs.
Demand and flexible load responses are helping to manage energy efficiency and when real-time and historical data is interconnected and AI technology is applied, the industry can be further optimized with the critical insights and predictions to optimize the grid both now and into the future.
For cost minimisation, operators and generators needs to address downtime, which Continuity Central report on average accounts for 32hrs a month, at a cost of $220,000 per hour. VROC OPUS can drastically improve asset reliability with AI predictive maintenance, allowing for maintenance to be planned, spares to be ordered and work implemented accurately first time, minimising disruptions to the network and reduce rising costs.
Real-time data is collected across the entire enterprise for an integrated approach to advanced analytics and insightsRead More
Distributed data storage and security helps operators and owners implement analytics enterprise wideRead More
Predict asset deviation in advance so you can plan interventions and maintenanceRead More
Produce Ai models in minutes without coding or programmingRead More
Insights acquired from advanced analytics can assist operators with energy management optimization, planning, monitoring, and reporting requirements, helping them meet their carbon reduction and sustainability goals.
Use AI to forecast your energy production hours and days in advance across your diversified network. AI models can factor in variables including real-time asset condition, weather volatility and historical data. Get alerts if production forecast falls beneath thresholds.
Smart grid control can be optimized through the real-time connection to all system components. Automated data collection, storage, processing and AI analytics can give grid operators greater insights into the current and future states to improve business decisions.
Reduce rising costs caused by asset failures and unplanned downtime with predictive maintenance insights. Early intervention and planned maintenance based on AI predictions can lead to significant operational cost savings, for some VROC customers, millions of dollars have been saved.
Client supply of historical data and set-up of real-time streaming
Data ingestion and real-time data streaming connection
Client training and model generation
Models in production. Client starts delivering business value
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Ready to embark on a pilot project or roll-out the innovation enterprise wide? Perhaps you need assistance integrating your systems or accessing your data? We have a solution to help you as you progress through your digital transformation.
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DataHUB4.0 is our enterprise data historian solution, OPUS is our Auto AI platform and OASIS is our remote control solution for Smart Cities and Facilities.
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Ready to embark on a pilot project or roll-out AI innovation enterprise wide? Perhaps you need assistance integrating your systems or storing your big data? Whatever the situation, we are ready to help you on your digital transformation.
The efficient deployment, continuous retraining of models with live data and monitoring of model accuracy falls under the categorisation called MLOps. As businesses have hundreds and even.
Learn more about DataHUB4.0, VROC's distributed enterprise data historian. Complete the form form to download the product sheet.
Discover how you can connect disparate systems and smart innovations in one platform, and remotely control your smart facility. Complete the form to download the product sheet.
'OPUS, an artistic work, especially on a large scale'
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