Renewable energy planning and analytics
Advanced analytics for strategic renewable energy planning and investment
Energy plant management
Intelligent automation and predictive analytics for modern energy plants
Renewable energy asset inspection
Safer, faster, and more precise inspections for your assets
Automated Powerline Inspections
From drone data capture to AI-powered predictive grid maintenance
Smart grids
Intelligent solutions for a decentralized, secure, and optimized grid
Oil&Gas monitoring and research
Intelligent monitoring and advanced analytics for the Oil&Gas Sector
Renewable energy planning and analytics
- Renewable Energy Resource Assessment (GIS-based Solar, Wind, Biomass, and Geothermal Potential Mapping)
- Weather and Climate Data Analytics
- Grid Integration and Demand Response Planning
- Financial and Economic Analysis for Renewable Projects
- Hybrid Energy System Modeling
- Microgrid and Off-grid Energy Planning
- Environmental Impact and Risk Assessment
- Biomass Plant Performance Monitoring & Emission Control
- Heat Utilization & Direct Use Optimization
Approaches
- GIS-based spatial analysis
- Remote sensing & satellite data applications
- Machine learning models to predict energy yields
- Big data analytics for climate trends
- AI-based weather forecasting models
- Grid optimization algorithms for energy dispatch
- Hybrid Energy System Modeling (solar, wind, biomass, diesel, battery storage).
- Life cycle assessment (LCA) to analyze emissions and energy footprint.
Data Types
- Geospatial Data (Shapefiles, GeoTIFF, KML)
- Satellite Imagery (Landsat, Sentinel, Meteosat)
- Meteorological Data (Solar irradiance, wind speed, Temperature, Humidity, Precipitation)
- Historical Climate Data (NOAA, ECMWF, NASA)
- Topographic & Land Use Data
- Smart Meter Data (Voltage, Current, Power Factor)
Technologies
- GIS & Remote Sensing: GDAL, QGIS, Google Earth Engine, ArcGIS
- Machine Learning: Scikit-Learn, TensorFlow, XGBoost
- Climate Data Processing: Climate Data API (NOAA, NASA)
- Big Data Processing: Apache Spark, Dask, Pandas
- Time Series Forecasting: Prophet, ARIMA, LSTM (TensorFlow/PyTorch)
- Energy Optimization: PyPSA, EnergyPlus, OpenDSS
- Hybrid Energy Simulation: HOMER, OpenModelica
Energy plant management
- Real-time Plant Monitoring & Performance Analytics
- Predictive Maintenance & Asset Management
- Grid Integration & Energy Trading Platforms
- SCADA & Industrial Control System (ICS) Development
- Heat Recovery & Direct Use Optimization
Approaches
- IoT-based real-time monitoring using smart sensors and edge computing.
- Digital Twin simulations for asset lifecycle optimization.
- Automated maintenance scheduling using historical data.
- Smart grid algorithms for energy demand-response management.
- SCADA-based automation for remote control & monitoring.
- AI-driven waste heat recovery modeling
Data Types
- Operational logs: Equipment status, maintenance logs.
- Weather data: Wind speed, solar radiation (for renewables).
- Process control data: Voltage, current, temperature.
- Building heat demand profiles.
Technologies
- IoT & Edge Computing: Node-RED, AWS IoT, Azure IoT Hub.
- Data Analytics & AI: Pandas, TensorFlow, Apache Spark.
- Grid Simulation & Optimization: GridLAB-D, PyPSA.
- SCADA Platforms: Ignition SCADA, OpenPLC, WinCC.
- Heat Simulation & Optimization: EnergyPlus, OpenModelica, TRNSYS.
Renewable energy asset inspection
- Photovoltaic (PV) System Inspection
- Wind Energy (Onshore & Offshore) Inspection
- Small Hydropower Plant Inspection
Approaches
- AI-powered fault detection for panel cracks, dust accumulation, and efficiency loss
- Drone & thermal imaging analysis for hotspot identification.
- Autonomous drone & robotic inspection of turbine blades, towers, and gearboxes.
- AI-based dam & penstock structural health monitoring.
- Ultrasound & LiDAR-based underwater inspections.
Data Types
- High-resolution images from drones & on-ground cameras.
- Thermal imaging data from infrared cameras.
- LiDAR point cloud data for 3D surface inspection
- GPS tracks for drone mission planners
- Vibration & stress sensor data.
- Water level & pressure readings.
- Sonar & ultrasound data.
- Video feeds from underwater drones.
- 3D/CAD/BIM models & GIS data.
Technologies
- Image Processing: Scikit-Image, Keras, YOLOv8.
- Drone SDKs: DJI SDK, MAVSDK, ArduPilot.
- Thermal Image Processing: OpenCV, FLIR Atlas SDK.
- GIS & Mapping: QGIS, Leaflet, ArcGIS.
- LiDAR Processing: PCL (Point Cloud Library), Open3D.
- Structural Analysis: Abaqus, ANSYS.
Automated Powerline Inspections
- AI-powered Fault Detection & Predictive Maintenance
- Drone-based Powerline Inspection
- Digital Twin Creation for Powerline Networks
- LiDAR-based Vegetation Management
Approaches
- AI-based anomaly detection for powerline defects (corrosion, wear, vegetation encroachment).
- Predictive maintenance models using historical inspection data.
- Computer vision-powered defect classification from aerial images.
- SCADA integration for real-time power grid monitoring.
- Autonomous drone navigation along transmission lines.
- High-resolution image & thermal inspection for overheating components.
- LiDAR scanning for 3D powerline mapping.
- Cloud-hosted 3D GIS-based visualization.
- Real-time asset monitoring using IoT sensors.
- VR-based training & simulation environments.
- AI-powered vegetation encroachment detection.
- LiDAR scanning for accurate clearance measurement.
Data Types
- High-resolution images & infrared thermal scans (for detecting hot spots & corrosion).
- LiDAR point clouds for 3D mapping.
- Vibration, load, and temperature sensor data (for predictive failure analysis).
- Multispectral satellite imagery for land cover classification.
- Weather data for predicting vegetation growth trends.
Technologies
- AI & Machine Learning: TensorFlow, PyTorch, Scikit-learn.
- Image Processing: OpenCV, YOLOv8, Detectron2.
- Time-Series Analysis: Prophet, Darts, SciPy.
- IoT Data Processing: Apache Kafka, Node-RED.
- Drone Navigation: PX4, ArduPilot, MAVSDK.
- Digital Twin Frameworks: Azure Digital Twins, Siemens MindSphere.
- GIS & Mapping: QGIS, Google Earth Engine, CesiumJS.
Smart grids
- AI-powered Grid Optimization & Demand Response Management
- Distributed Energy Resource (DER) Management & Grid Integration
- Smart Grid Cybersecurity & SCADA Protection
- Grid Edge Intelligence & IoT-based Asset Monitoring
- AI-driven Energy Market & Grid Trading Platforms
Approaches
- Smart meter integration for real-time consumption tracking.
- AI-driven predictive load balancing algorithms.
- Seamless integration of renewable energy sources (solar, wind, storage) into the grid.
- Microgrid & virtual power plant (VPP) management.
- AI-based optimization of distributed energy resources.
- Secure SCADA & industrial control system (ICS) monitoring.
- IoT-enabled predictive maintenance for power transformers & substations.
- Digital twin technology for real-time grid simulations.
- AI-powered dynamic pricing algorithms.
- Integration with power exchange markets.
Data Types
- Smart meter data (power consumption patterns).
- Grid sensor data (voltage, frequency, power quality).
- SCADA logs & intrusion detection system (IDS) alerts.
- Energy market price fluctuations & historical trading data
Technologies
- AI/ML: TensorFlow, PyTorch, XGBoost, LightGBM.
- Time-series forecasting: Prophet, Darts, SciPy.
- Grid analytics: Pandapower, OpenDSS.
- DER Management: GridLAB-D, PyPSA.
- Security Analysis: Suricata IDS, OSSEC, Wireshark.
- IoT & Edge Computing: Azure IoT Edge, AWS Greengrass.
- Digital Twin Modeling: Siemens MindSphere
Oil & Gas monitoring and research
- Real-time Oil & Gas Asset Monitoring
- Predictive Maintenance & Asset Integrity Management
- AI-powered Seismic Data Processing & Reservoir Modeling
- Digital Twin for Oil & Gas Infrastructure
- Leak Detection & Pipeline Monitoring
- AI-driven Oil Spill Detection & Environmental Impact Analysis
- Carbon Capture & Emission Monitoring
- Automated Drilling Optimization & Wellbore Monitoring
Approaches
- IoT-based sensor monitoring for pipelines, wells, and offshore platforms.
- AI-driven anomaly detection for pressure, temperature, and flow rate variations.
- AI-driven predictive models based on sensor readings & historical failures.
- Ultrasound, infrared, vibration analysis for early fault detection.
- Digital twins for virtual equipment wear simulation.
- AI-assisted seismic image classification for oil reservoir detection.
- 3D subsurface modeling with geophysical data integration.
- Satellite & drone-based infrared imaging for gas leak detection.
Data Types
- IoT sensor data (temperature, pressure, vibration, flow rates).
- Vibration, acoustic emissions, thermographic imaging.
- Maintenance logs, operational history.
- Structural integrity data from ultrasonic testing (UT).
- Seismic reflection & refraction wave data.
- Reservoir pressure & porosity logs.
- Well logs & borehole imaging.
- CO2, CH4 concentration data.
Technologies
- IoT & Cloud: AWS IoT, Azure IoT Hub, Google Cloud IoT.
- AI Analytics: TensorFlow, PyTorch, Scikit-learn.
- SCADA Integration: Ignition SCADA, Wonderware.
- Digital Twin Simulation: Siemens MindSphere, NVIDIA Omniverse.
- Seismic Analysis: ObsPy, Madagascar, SEG-Y libraries.
- GIS & Mapping: ArcGIS, QGIS, GDAL.
- Integration with: Petrel, Schlumberger Eclipse
- Simulation: Delft3D, HYCOM