Energy & Natural Resources
Optimising Mineral Exploration
Background:
An Australian ASX-listed mining company approached AC SmartData to enhance its exploration capabilities and identify high potential mineral deposits.
Traditional methods had proven time consuming and inefficient, prompting the company to seek AI-driven solutions to improve accuracy and streamline the exploration process.
Challenge:
The objective was to leverage AI to identify high-potential mineralization zones and detect geological anomalies across vast regions.
The solution also needed to comply with Australian regulations, ensuring responsible, transparent, and secure data handling throughout the process.
Results:
AC SmartData developed an AI-powered solution that integrated large datasets and uncovered previously undetected patterns, leading to more targeted and efficient exploration.
The AI system identified several high-potential areas missed by traditional methods, reducing exploration time and lowering operational costs by minimizing wide area investigations.
Additionally, data-driven insights boosted the geological team’s confidence in making more precise targeting decisions.
Advancing Renewable Energy
Challenge:
In an ambitious partnership with a leading player in the renewable energy sector, we embarked on a mission to revolutionize energy forecasting for one of their wind farms. The client faced significant challenges with their existing forecasting methods, which were often inaccurate and inconsistent.
These inefficiencies negatively impacted multiple aspects of their operations, from the day to day management of energy production to the long-term planning of maintenance schedules and strategic investment decisions.
Solution:
Our team developed a cutting-edge solution tailored to the unique needs of the wind farm. This innovative solution synthesized a variety of crucial factors, including data in different formats from various sources, market dynamics and regulations, and the latest technology advancements. By integrating these diverse data points, the model provided a holistic view of potential energy output.
In addition, the foundation of our AI solution was built on three pillars: accuracy, adaptability, and compliance with AI safety standards and regulations.
Outcome:
The results were remarkable:
The model achieved an impressive 95% accuracy rate in predicting daily energy output, marking a 20% improvement over previous methods.
With more reliable forecasts, the wind farm was able to optimize energy production, aligning output more effectively with market demand and environmental fluctuations.
Enhanced forecast precision allowed for more strategic and timely maintenance scheduling, reducing both downtime and unnecessary maintenance expenditures.
With enhanced forecast reliability, the client is now better equipped for strategic planning and informed investment decisions.