Predictive Maintenance
Temperature & vibration modelling of generators, turbines, pumps and transformers to flag anomalies weeks before failure.
Applied AI · Energy & Industry
Turn industrial data into
Machine Learning, the power of data
Turn industrial data into
decisions that perform
Machine Learning, the power of data
DSC Energy Analytics turns industrial data into accurate predictions, automated visual inspection and measurable gains — across wind, solar and water assets.
Since 2018
Seven-figure analytics portfolio
Projects across 4 continents
Wind · Solar · Water
70+Projects delivered
140+Wind & solar plants
3,500+Equipment models
8 yrsSince 2018
Trusted by leading wind, solar & water operators — from utility-scale multinationals to specialist plants.
8 yrsdelivering analytics
70+projects completed
4continents
30+ yrsenergy & water
Who we are
Applied AI built on three decades across energy and water
DSC Energy Analytics is a consulting company specialised in advanced analytics, machine learning, computer vision and digital twins for organisations that put data at the centre of their decisions.
Founded in 2018 on a team that has worked across the energy and water sectors since 1994 — with multinationals and SMEs across four continents — we bring artificial intelligence and big data to renewable generation, water and industry.
- Renewable-energy focus. Deep domain expertise in wind, solar and water operations.
- End-to-end delivery. From 1-second SCADA data to models deployed in the cloud or on-premise.
- Computer vision & digital twins. Visual inspection and living, data-driven plant replicas.
- Own HPC capacity. 175+ Tflops, 9 GPUs and 768 GB RAM, plus public cloud and edge.
4continents
30+ yrsenergy & water
What we do
A full applied-AI toolkit for energy assets
From predictive maintenance and visual inspection to digital twins and automated reporting — we cover the analytics lifecycle end to end.
Performance & Improvement Evaluation
Power-generation models that monitor wind & solar performance and quantify the real impact of upgrades, farm- and turbine-level.
Automated Visual Inspection
Computer-vision models that detect defects and misalignment in solar collectors and PV plants from video, drone and thermal imagery.
Computer Vision
Digital Twins & Efficiency
Dynamic baselines and energy-efficiency indices that grow into living, data-driven replicas of your plant.
Automated Reporting & Dashboards
Report & KPI pipelines (Airflow + Streamlit) that turn raw operations into automated, decision-ready dashboards.
Big Data & Data Engineering
Data extraction, processing and pipelines integrated with SCADA, deployed on Azure, AWS, Google Cloud or on-premise.
AI Enablement
Helping industrial companies and SMEs adopt AI: use-case diagnosis, guided implementation and ongoing support.
Where we work
Industries we serve
Energy and beyond — wherever data can drive better operational and business decisions.
Wind EnergyFarm & turbine analytics
Photovoltaic SolarPV plants & portfolios
Solar Thermal (CSP)Collectors & storage
Water & DesalinationEfficiency & digital twins
Industrial & ManufacturingSCADA · calibration · process
Insurance & RiskChurn & segmentation
Cross-industry AnalyticsForecasting & optimisation
R&D & InnovationApplied research projects
End to end
From raw SCADA to models in production
We handle the full lifecycle — high-frequency data, predictive models, computer vision and digital twins — deployed in the cloud, at the edge or on your own facilities.
How we work
A proven Machine Learning process
From problem definition to a deployed product that feeds back into operations.
01
Problem definition
Frame the business question and value at stake.
02
Data extraction
Ingest raw SCADA and operational data.
03
Processing
Transform and validate into reliable inputs.
04
Exploratory analysis
Surface patterns, drivers and anomalies.
05
ML modelling
Train, tune and validate predictive models.
06
Deployment
Visualise, deploy and integrate into the workflow.
PythonRLightGBMXGBoostCatBoost
TensorFlowPyTorchYOLOSAMStreamlit
AirflowMLflowDockerAzureAWSGoogle Cloud
175+Tflops of compute
9GPUs · 127 GB
768 GBRAM · 68 cores
Cloud + Edgeor on-premise
How we engage
Start small, scale with confidence
Most engagements begin with a low-risk data diagnostic and grow, step by step, into full digital-twin capability — at the pace that works for you.
STEP 01
Data diagnostic
We explore your data and deliver a clear diagnosis of the opportunities — fast, low-commitment.
Start smallSTEP 02
Predictive models
Monitoring, anomaly-detection and forecasting models tailored to your assets.
STEP 03
Automation
Automated reports, dashboards and alerts embedded in your daily workflow.
STEP 04
Digital twin
A living, data-driven replica that scales with your operation and decisions.
Selected work
Results that scale
A selection of real, anonymised projects across wind, solar, water and industry.
0
Projects delivered
0
Wind & solar plants
0
Equipment models
0
Delivering since 2018
Seven-figure analytics portfolio · delivered since 2018
Quantifying turbine upgrades, site-wide
Modified-turbine ML models, benchmarked against neighbouring turbines on 15-second high-frequency data, confirm and quantify power improvements across every wind sector.
3,000+models · 120+ wind farms
Early anomaly detection in rotating equipment
A dual DL-ML model predicts multi-component temperatures of generators, turbines, pumps and transformers, raising early anomaly alerts. Deployed on Azure with Python.
500+models · 23 plants · 1,500 MW
Automated visual inspection of solar collectors
Computer-vision models flag collector defects and misalignment from video and imagery, integrated with operations to prioritise maintenance — in production at a global CSP operator.
In productioncomputer-vision pipeline
Drone & thermal-imaging defect detection
Detection of PV module and string anomalies from aerial RGB and thermal imagery, turning inspection flights into prioritised, geolocated defect maps.
Aerial + thermalfield-validated imagery
Energy-efficiency baselines & digital twin
Dynamic baselines and energy-efficiency indices with automated reporting, growing step by step into a digital twin of the plant — a multi-year, expanding engagement.
Multi-yearscaling to a digital twin
Cleaning-fleet GPS optimisation
Spatial-data analytics integrated with SCADA track the cleaning history and performance of every collector and truck, tailored to each plant.
14+CSP plants · 1,200 MW · 3 countries
An automated plant-reporting factory
Airflow + Streamlit pipelines deliver automated plant reports and KPI dashboards, replacing manual reporting with decision-ready outputs.
In productionacross multiple clients
Customer churn & demand prediction
ML classification anticipates non-renewals and regression models track daily sales — improving retention targeting and forecast accuracy.
+20% AUCchurn · −50% forecast error
Get in touch
Let's put your data to work
Tell us about your assets and the problem you'd like to solve. We'll get back to you shortly.