Data Analytics in Energy Generation

Put Your Data To Work For Cleaner, Safer, And More Cost-Effective Energy Operations

Make Better Decisions By Leveraging Artificial Intelligence and Predictive Models In Your Work Process

The world faces dramatic energy problems. The global grid and the rising electrification of daily life will require increasingly advanced energy infrastructure to be sustained. Energy companies have a need to improve their data oversight and predictive capabilities to cut costs, save power, and most importantly prepare for the daunting energy conditions across the horizon.

Every player in the energy generation market needs to make real-time decisions about the most economically efficient ratio of consuming energy, selling energy, and storing energy. Employing forecasting models and a comprehensive network of stakeholders are key, as this will ensure cost and CO2 emission optimization.

Artificial intelligence (AI) will play a leading role in aligning the needs of energy stakeholders while offering significant support in the process. At Freya Systems, we analyze various solutions in the roles they can play to optimize energy consumption, storage and generation, while choosing the most economically viable option.

Extract and Organize Data

Extract and Organize Data

The big data generated by industrial IoT is commonly characterized by its varietal sources, unstructured features, noise, and high redundancy. These features can thwart manufacturers trying to effectively use their data. 

Optimize for Key Factors

Optimize for the Key Performance Indicators that Matter Most

KPIs allow you to make critical decisions backed by data to impact quality, profitability and the overall future of your business.  

Improve Access

Improve Access to Knowledge

The big data industry is still working to provide better visualization and customer interaction tools. Presenting information in an intuitive and user-friendly style is important, as it helps users produce better insights and devise unthought of solutions.

The Right Data is Key for Predictive Analytics in the Energy Sector

Even though energy companies collect and manage data, digitizing it with advanced tech solutions can be problematic. There are associated risks of data loss, poor customization, system failure, and unauthorized access. Since the cost of error is high in the energy industry, many companies are reluctant to risk trying novel approaches and turning to experts, like Freya Systems, to partner in solutions.

Freya System Solutions

The right mix of descriptive, predictive and prescriptive analytics leads to great competitive advantages. These can include cost reduction, supply chain optimization and productivity gains, or the use of new disruptive approaches like forecasting manufacturing, proactive maintenance, and ‘predict and fix’ models.


We specialize in predictive models for complex equipment. Using our experience, we can develop algorithms you can use system-wide to optimize production, maintenance protocols, and/or response to emergency events.



Operational efficiency is critical.  We enable operators to leverage AI and machine learning to dramatically improve efficiency, reduce their carbon footprint, and leverage both predictive and prescriptive insights derived from collected enterprise data.

Predictive Modeling


Using analytics, you can combine data across data sources and rapidly identify problems, discover root causes, predict future performance, and automate actions to continuously improve quality, ease compliance and reduce unnecessary costs.


Make Our Team Yours.