For reasons that span geopolitics, macroeconomics, and climate change, the energy sector faces complex challenges that demand collaboration across markets and geographies.  

Energy companies require partners with deep technology expertise and both sector-specific and cross-industry experience to enhance the ingenuity of their own experts. Weather forecasting is an example of a key energy challenge where innovation can be accelerated by sharing experiences and solutions across the sector.  

This blog outlines three ways BJSS has helped clients improve their weather forecasting capabilities to optimise their operations and supply management. It then explores six principles for designing your energy trading data and AI solution, ensuring it’s not only efficient and scalable but also delivers real-time capabilities, comprehensive data visibility, and a minimised environmental impact. 

Crop yield forecasting


One of our global commodity trading clients faced significant limitations with its crop yield forecasting system, with single queries taking up to eight hours to process. The extent of these delays rendered the tool impractical as it could only reliably analysed data after the growing season had ended, rather than providing real-time, actionable insights from the start.  

By migrating this client to the public cloud, BJSS enabled it to capture and analyse eight separate data streams every six hours, including weather data from the National Oceanic and Atmospheric Administration. Moreover, the Extract, Transform, Load (ETL) processes BJSS introduced soon managed billions of data rows daily, delivering accurate models within 30 minutes of data receipt. 

The result of this big data solution? The client’s forecasting efficiency was enhanced by a factor of 10,000 

Key takeaway:  

Accelerating insights is crucial, but the real focus should be on the business value these insights generate. 

Global weather forecast for the Met Office


The UK’s national meteorological service, the Met Office, is renowned globally for its weather forecasting accuracy and innovation. 

BJSS's collaboration with the Met Office has led to significant advancements in weather forecasting, enhancing the accuracy and efficiency of weather predictions in various sectors. 

Flood risk forecasting: Designed the user experience for hydrometeorologists to predict flood risks for the next five days. 

Aviation turbulence data: Piloted a service providing turbulence data for global aviation, enhancing forecasts for governments and flight planners. 

Rail network forecasting: Delivered a proof of concept to Network Rail, improving route planning accuracy with the latest forecasting capabilities. 

Space weather forecasting: Supported space weather forecasts, such as predicting solar flares and ensuring mitigations within eight hours to protect global telecommunications and power grids. 

Key takeaways: 

Innovation is the tip of the iceberg. Underpinning it all is cloud transformation, governance, and service management.  

Addressing complex challenges requires tackling them from different perspectives, one use case at a time, with the objective of working towards a unified target architecture. 

Streamlining data ingestion


To enhance data access and scalability, BJSS developed a data ingestion platform for the digital trading analysts of an energy company.  

Previously, each desk managed its own ingestion pipelines, which caused maintenance difficulties when key personnel departed. Inefficiencies were also widespread, with proof-of-concepts routinely moved straight to production and the same weather data — all written differently — being ingested multiple times. 

The new platform improved data availability, including unstructured data, such as cloud cover images. Designed for easy maintenance, it offers opportunities for offshoring or employing graduate engineers. 

Key takeaways:  

Prioritise user experience by integrating customer and employee needs into engineering, data management, and governance.  

Demonstrating value quickly promotes adoption by improving user experience and embedding governance and efficiency. 

The power of 'just enough'


For energy companies, successful innovation involves empowering teams with rapid access to unique, timely and trusted data to generate insights and models while ensuring governance and security. 

Many energy companies already manage large-scale projects but can still accelerate value by focusing on high-impact use cases that drive business value and stakeholder momentum. This can be achieved by delivering ‘just enough’ of the platform to function as intended but sufficiently scalable to ensure a future-proof architecture and business model. 

Six principles to design your data and AI technology solution in energy trading


Success in volatile energy markets demands reliable trading insights. Traders and analysts need the autonomy and flexibility to develop fast, accurate, and evolving predictive models, leveraging the increasing volume, velocity, and complexity of available data. 

With over 30 years of experience supporting clients in the energy sector, BJSS has a deep understanding of the industry's unique challenges and opportunities. We help our clients turn data into a competitive advantage, leveraging our expertise to design and implement the right solutions for their specific needs.  

Here are six principles we work through with energy companies to help them design the right solutions: 

  1. Latency and velocity: Right-size your architecture based on data needs. Determine if you require reporting solutions, half-hourly data, or real-time data.

  2. Scalability: Flex architecture modules based on your tech stack, scaling up as necessary.

  3. Modularity: Choose components that work well together while maintaining flexibility for specific use cases.

  4. Discoverability and democratisation: Enable traders and analysts to discover and use new datasets with built-in assurance quickly.

  5. Visibility of everything: Allow users access to data at every transformation stage as soon as it’s ingested.

  6. Cost and carbon efficiency: Design your data platform to minimise costs and carbon footprint from the start, incorporating #FinOps and #CarbonOps principles. 

By focusing on these principles, energy companies can build robust, efficient, and sustainable data and AI technology solutions that drive success in energy trading. 

Are you looking to transform your weather forecasting and energy trading capabilities? Find out how you can harness the power of advanced data and AI solutions tailored to your business.