Performance Monitoring
Power-generation models for wind-farm performance monitoring, at both farm level and individual-turbine level.
Advanced Analytics · Machine Learning · Energy
Machine Learning,
Machine Learning,
the power of data
DSC Energy Analytics turns industrial energy data into accurate predictions, predictive maintenance and measurable performance gains for wind and solar assets.
60+Projects delivered
140+Wind & solar plants
3,500+Equipment models
20+Countries
Who we are
Advanced analytics built on 25 years in energy
DSC Energy Analytics is a consulting company specialised in advanced analytics and predictive modelling with Machine Learning, serving companies that put data at the centre of their decision-making.
Founded on the accumulated experience of a team that has worked in the energy sector for more than 25 years — with both multinationals and SMEs across over 20 countries — DSC Energy was created to bring the technologies of the digital transformation, artificial intelligence and big data, to renewable generation.
- Renewable-energy focus. Deep domain expertise in wind and solar operations.
- End-to-end delivery. From raw SCADA data to models deployed in the cloud or on-premise.
- Own HPC capacity. 175+ Tflops, 9 GPUs and 768 GB RAM, plus public cloud and edge.
20+countries
25 yrsindustry experience
What we do
Data science services for energy assets
From performance monitoring to predictive maintenance, we cover the full analytics lifecycle for renewable generation and industrial equipment.
Predictive Modelling
Advanced ML models — LightGBM, neural networks — that forecast equipment behaviour from historical and high-frequency data.
Predictive Maintenance
Temperature and vibration modelling of generators, turbines, pumps and transformers to detect anomalies before they cause failures.
Improvement Evaluation
Quantify and confirm the real impact of upgrades and power improvements across every wind sector, using high-frequency data.
Image & Geospatial Analytics
Video and image analytics, anomaly detection and GPS/spatial analytics — such as cleaning-truck monitoring in solar-thermal plants.
Big Data & Data Engineering
Data extraction, processing and pipelines integrated with SCADA, deployed on Azure, AWS, Google Cloud or at the client's facilities.
How we work
A proven Machine Learning process
Every project follows a rigorous, repeatable path — from problem definition to a deployed product that feeds back into operations.
01
Problem definition
Frame the business question and the value at stake.
02
Data extraction
Ingest raw SCADA and operational data sources.
03
Processing & cleaning
Transform and validate data into reliable inputs.
04
Exploratory analysis
EDA to surface patterns, drivers and anomalies.
05
ML modelling
Train, tune and validate predictive models.
06
Reporting & deployment
Visualise, deploy and integrate into the workflow.
PythonRLightGBMXGBoostCatBoost
TensorFlowPyTorchStreamlitShinyPySide
AzureAWSGoogle Cloud
175+Tflops of compute
9GPUs · 127 GB
768 GBRAM · 68 cores
Cloud + Edgeor on-premise
Where we work
Sectors we serve
Energy and beyond — wherever data can drive better operational and business decisions.
Wind EnergyFarm & turbine analytics
Photovoltaic & Solar ThermalPV & CSP plants
Biomass & AgroenergyBio & agro resources
Carbon Emission MarketCO₂ & trading data
Smartgrid & SmartcitiesGrid & urban data
Construction & O&MOperation & maintenance
Water TreatmentDrinking & waste water
Cross-industry AnalyticsSales, churn & forecasting
Track record
Results that scale
A selection of real projects delivered across wind, solar and industrial operations.
0
Projects delivered
0
Wind & solar plants
0
Equipment models
0
Countries
Wind-turbine upgrade evaluation
Modified turbine ML models based on the behaviour of neighbouring turbines, using high-frequency data in R and Python to confirm and quantify power improvements across all wind sectors.
3,000models across 120 wind farms
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.
500models · 23 plants · 1,500 MW
Cleaning-truck monitoring in CSP plants
Custom spatial-data analytics integrated with SCADA to track the cleaning history and performance of every collector and truck, tailored to each plant.
14CSP plants · 1,200 MW · 3 countries
Customer churn & sales prediction
ML classification to anticipate non-renewals and regression models for daily sales tracking — improving retention targeting and forecast accuracy.
+20% AUCchurn · −50% sales-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.