Reported impact
Business outcome
Reported outcomes include 12x Reduction in time spent on manual forecasting; £350K Savings per year due to energy optimization predicted.
- Productivity Improvement
DatabricksEnergyReal-time AnalyticsAI and Machine LearningData AnalyticsData PlatformProductivity Improvement2023 DatabricksDB#ff3621EnergyUnited KingdomBut the use of virtual machines to optimize the machine learning (ML) workflow was not sustainable. SSE PLC is a net-zero focused energy company that generates, transmits, distributes and supplies gas and electricity…12x Reduction in time spent on manual forecastingDec 26, 20236Real-time AnalyticsAzure Databricks · Databricks Data + AI Platform0 2023-12-26 6
SSE Energy Solutions
Real-time Analytics
But the use of virtual machines to optimize the machine learning (ML) workflow was not sustainable. SSE PLC is a net-zero focused energy company that generates, transmits, distributes and supplies gas and electricity across the UK and Ireland.
12x Reduction in time spent on manual forecasting
£350K Savings per year due to energy optimization predicted
Solution
SSE Energy Solutions recognizes the importance of the application of data and analytics in managing this mix, harnessing energy data for a range of purposes from assessing usage and forecasting future energy demand to measuring the output of renewable generation and benchmarking the consumption of green energy. But the use of virtual machines to optimize the machine learning (ML) workflow was not sustainable. With Databricks now part of the solution, SSE Energy Solutions can regularly run their optimizations, scaling the solution and helping to ensure their combined heat and power (CHP) systems run more effectively, with considerably reduced time, effort and associated costs. 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,” said Rachel Hassall, Head of Data and Analytics at SSE Digital Services. “It was a hugely inefficient use of our data scientists’ time.
Data flow
Operational, customer, and transactional data lands in Azure Databricks, is transformed in the lakehouse.
Solution architecture
- Compute
- Azure Databricks Provides the Azure-based lakehouse workspace for ingesting, transforming, and serving governed data.
- Databricks Data + AI Platform Provides the shared lakehouse environment for ingestion, engineering, analytics, and AI workloads.
Architecture clues
- Runs on Azure.
Evidence from the source
12x Reduction in time spent on manual forecasting
But the use of virtual machines to optimize the machine learning (ML) workflow was not sustainable.
SSE Energy Solutions recognizes the importance of the application of data and analytics in managing this mix, harnessing energy data for a range of purposes from assessing usage and forecasting future energy demand to measuring the output of renewable generation and benchmarking the consumption of green energy.
With Databricks now part of the solution, SSE Energy Solutions can regularly run their optimizations, scaling the solution and helping to ensure their combined heat and power (CHP) systems run more effectively, with considerably reduced time, effort and associated costs.
£350K Savings per year due to energy optimization predicted
Transparency Evidence & methodology
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
How scores are calculated
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Customer reference value85/100
- Problem clarity20/20Measures whether the customer's operational problem is specific enough to compare.
- Implementation depth20/25Rewards named components, data flow, integrations, and architecture detail.
- Outcome specificity20/20Measures whether the reported benefit is concrete rather than a generic claim.
- Quantified impact25/252 metrics describe measurable impact.
- Deployment scale0/10Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
- Source authority20/25The main evidence is an official vendor-published customer story.
- Claim traceability21/2510 of 12 key fields link to a named source.
- Direct evidence20/205 direct source quotes retained.
- Date provenance10/10The publication date comes from the source page or publisher.
- Source transparency8/101 source reference document publisher, URL, authority, retrieval, and supported fields.
- Implementation specificity10/10Rewards source-backed components, data flow, quantified impact, and a specific outcome.
Traceability
Sources & provenance
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
- https://www.databricks.com/customers/sseDatabricksFirst-party
SSE Energy Solutions customer story
Supports- Customer
- Business problem
- Solution
- Products
- Solution components
- Data flow
- Integrations
- technical area
- use case category
- Business outcome
- outcome category
- Metrics
- Architecture
- evidence quotes
- Publication date
Date evidence
- Published on source page Dec 26, 2023 · Databricks · First-partyOpen date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.
Related
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