It takes a great depth of domain expertise, data science, and analytics to tackle the unprecedented challenges facing the energy sector: 

• Unpredictability of the global market – highlighting the UK’s dependence on imported gas and oil. 
• Estimated cost of £30 billion to modernise the grid – to ensure more efficient transmission of electricity, faster restoration after power disturbances, reduction in peak demand, and better integration of renewable energy generation. 
• How to achieve NetZero climate goals – in line with the Climate Change Conference (COP) commitments.  

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How EES Data Solutions can tackle the energy sector’s challenges

Our work is actively aligned with achieving NetZero in the UK. By using our Data UX, Business Intelligence, and Advanced Analytics services, we can provide a secure web-based energy management dashboard that will collect, analyse, and report on key information. This will: 

• identify consumption and energy savings to manage power outages​. 
• provide smart grid management with weather predictive capabilities to maximise the efficiency of renewable energy sources. ​ 

• optimise production processes. 
• support refineries and distribution processes in adjusting to market demands ​ 
• improve safety and prevention of accidents in the field. 
• reduce operational costs. 
• support achieving NetZero​. 

A current case study

We’re currently developing a system that will automate weather alerts and risk analysis. Weather conditions are becoming increasingly more extreme than the current infrastructure assets are designed to withstand. Also, the complexity of the environmental conditions that need monitoring is increasing. This places unsustainable pressure on people and assets to maintain a consistently good supply of services given the number of daily weather alerts that could be missed; the reliance on human interpretation; knowledge availability; and time-consuming, labour-intensive decision-making. 

By implementing an automated alert system, we will digitally transform the process by connecting sensors to existing assets. The data provided by those sensors is collated and analysed so that asset failures can be predicted allowing for informed, and automated, decision-making.  

This leads to improved asset performance, faster and more reliable decision-making, and reduced operational costs.  

Think this is something you’d like to be part of? Get in touch and we can talk.