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Title
Digitally optimizing energy assets for decarbonization and cost savings
…et a Demo Login Contact Us Try Databricks Customer Stories / SSE CUSTOMER STORY Digitally optimizing energy assets for decarbonization and cost savings 12x Reduction in time spent on manual forecasting £350K Savings per year due to…
Description
SSE Energy Solutions, a UK/Ireland renewable-focused energy company, replaced a manual virtual-machine-based ML workflow with the Databricks Data + AI Platform on Azure to run machine learning optimization models for its combined heat and power (CHP) networks, including digital twins and demand forecasting. The move cut time spent on manual forecasting by 12x and is predicted to save £350K per year through energy optimization.
…R STORY Digitally optimizing energy assets for decarbonization and cost savings 12x Reduction in time spent on manual forecasting £350K Savings per year due to energy optimization predicted The drive for renew…
…arbonization and cost savings 12x Reduction in time spent on manual forecasting £350K Savings per year due to energy optimization predicted The drive for renewable energy and the mission to reach net-zero is decentraliz…
Company
SSE Energy Solutions
…rity and Trust Ready to get started? Get a Demo Login Contact Us Try Databricks Customer Stories / SSE CUSTOMER STORY Digitally optimizing energy assets for decarbonization and cost…
Industry
Energy
…ation on hundreds of different types of energy assets.” Share this post Details Industry : Energy Cloud : Azure Product : Agent Bricks Ready to get started? Try Databricks for f…
Problem
SSE Energy Solutions initially ran its ML-powered CHP optimization models on virtual machines, which required large amounts of manual input and was a hugely inefficient use of data scientists' time as the energy generation mix grew more complex.
…igital twins, process optimizations and the use of machine learning techniques. The team initially ran the ML-powered optimization models on virtual machines (VMs). “We quickly realized that each run required large amounts of manual input,” sai…
Solution
SSE Energy Solutions moved its ML workflow optimization model to Azure Databricks, feeding into edge optimization of four of SSE Heat Network's CHP-powered networks across the UK. The team automated the ETL of historic and current pricing regime data, created digital twins using historic demand data to simulate and predict future behavior, and used clustering and classification techniques to simplify optimization results for the edge optimization framework, with results fed via Power BI integration into dashboards for stakeholders.
…ation jobs and product pipelines, that it simply wasn’t able to when using VMs. The team now runs an ML workflow optimization model on Azure Databricks. This feeds into the optimization of the asset which is done on the edge to opti…
Business value
A forecasting job that once took a day a week now takes no more than two hours, and the company predicts £350K in savings per year from energy optimization. The team now runs weekly optimization jobs across each CHP network site, able to spin up and schedule multiple runs simultaneously, and has moved from near-year-ahead optimization to week-ahead optimization.
…ptimize for carbon reduction. Forecasting future demand is more efficient, too. A job that once took a day a week now takes no more than two hours. And using Databricks has also decreased the time to release. Running jobs on cl…
AI capabilities
Predictive Analytics, Digital Twins & Simulation, Machine Learning
…sing different slices of historic data and the application of simple rules, and to create digital twins using historic demand data to simulate and predict future behavior. Furthermore, the use of clustering and classification techniques to simplify t…
Technology
Databricks Data + AI Platform, Azure Databricks, Power BI, Agent Bricks
…ation jobs and product pipelines, that it simply wasn’t able to when using VMs. The team now runs an ML workflow optimization model on Azure Databricks. This feeds into the optimization of the asset which is done on the edge to opti…
Deployment model
Cloud
…of different types of energy assets.” Share this post Details Industry : Energy Cloud : Azure Product : Agent Bricks Ready to get started? Try Databricks for free Learn more…
Deployment options
cloud
…of different types of energy assets.” Share this post Details Industry : Energy Cloud : Azure Product : Agent Bricks Ready to get started? Try Databricks for free Learn more…
Implementation approach
Treated the CHP optimization work as a test case and MVP for a broader optimization framework intended to scale across hundreds of different types of energy assets.
…can run our processes and optimizations efficiently every week. Looking ahead, we see this as a test case, an MVP for a broad optimization framework under which we scale the optimization on hundreds of different types of energy assets.” Share this post Details Industry : Energy Cloud : Azure Product : Agent Brick…
Headline outcome
…R STORY Digitally optimizing energy assets for decarbonization and cost savings 12x Reduction in time spent on manual forecasting £350K Savings per year due to energy optimization predicted The drive for renew…
Use case type
Predictive operations
…sing different slices of historic data and the application of simple rules, and to create digital twins using historic demand data to simulate and predict future behavior. Furthermore, the use of clustering and classification techniques to simplify t…
