Identify patterns and anomalies to predict future trends, and recommend optimisation strategies. Maximised efficiency and minimised environmental impacts.
Respond faster to market changes with AI. Gain insights into your trading asset portfolios to compare against the market, track asset-level profitability, and find poor performers.
Cost-efficient network planning and optimisation for new forms of energy production and small-scale production. Balance the grid with location analytics and external data.
Boost energy network reliability in an era of rising prices energy with AI. Preventive grid maintenance, powered by machine learning, detects issues before they occur, optimizing maintenance and enabling rapid responses to faults and disruptions.
The demand for high-quality and cost-efficient energy services is increasing rapidly. Energy producers can optimize the complex demand-production system by improving the predictability, reliability and utilisation of production, storage and distribution elements.
/ Collect data, predict and optimise
Improve production optimisation and power plant operations with real-time insights. Scenario modelling and recommended optimal operating strategies help you to maximise efficiency, reduce costs, and minimise environmental impact.
Data collected from various sources, such as external sources, sensors, SCADA systems, and historical data provide a comprehensive view of energy production and consumption patterns.
Detect patterns and anomalies within the data, predict future trends, and propose novel optimization strategies. Enhanced forecasting accuracy enables the identification of opportunities for improving asset execution in energy production.
Increase production accuracy by using more accurate and real-time forecasts for energy production
Enhance resiliency by optimising the production, storage and distribution
Reduce costs by finding weak spots in asset portfolios and processes
Artificial intelligence offers the capability to identify potential opportunities for automated trading in intraday markets. With an advanced analytics approach, it's possible find potential spots for improvement behind the AI hype, and find the optimal solutions to accommodate the diverse asset portfolios of individual customers.
Data collected from asset-level data sources and across all physical markets can be utilized to deliver accurate follow-ups on changing situations on the fast-moving energy markets:
Several practical implementations of AI for enhancing trading performance include analytics for portfolio hedging follow-up, monitoring asset-level performance and profitability, and conducting comparative analyses of trading performance against the market.
AI enables the seamless integration of asset planning, optimization, operational, and financial systems, delivering real-time analytics and actionable insights.
Catch increasing revenues in 15-minute interval energy markets
Get better feedback on selected hedging and forecasting models
Gain insights into poor performers in the trading asset portfolios
React faster to market changes
In recent years, the distributed small-scale production connected to the distribution network has seen a significant increase. The costs of construction, materials, and fuels have risen, causing consumers to struggle with spiraling prices. Furthermore, the electricity market law requires companies to report the cost-effectiveness of their development initiatives.
Meeting reliability requirements alone is no longer sufficient; cost-effectiveness has emerged as an equally important consideration. Achieving cost-effectiveness demands even more careful planning.
This entails acquiring more precise information for investment planning with fewer personnel, forecasting changes in the customer landscape, and simplifying the determination of construction costs. By combining internal and external data, sufficient information can be provided to support planning and decision-making.
Cost-efficiency for new forms of energy production and small-scale production
Network balancing with smart network planning
Location planning in an early phase minimise construction costs
Preventive maintenance relies on diverse data sources, including asset records, historical data, IoT sensor data, and the power of AI-driven predictive analytics. This synergy of data can pinpoint vulnerabilities within the grid system and identify when components of the grid are about to fail.
A smart and resilient grid help you in both finding an optimised maintenance plan and react fast to incoming disturbances.
Increase the reliability of energy networks in a era of rising energy prices
Avoid unnecessary renewals of devices and components with precise and timely maintenance
Find the fault location immediately, e.g. after a storm