close
R I A

AI in Solar Energy

School of Energy · Intermediate to Advanced

From Solar Resource to AI-Powered PV Systems, Solar Farms & Smart Energy Management

Utility-scale and C&I solar fleets generate continuous inverter / string telemetry, irradiance and weather streams, tracker states, soiling indicators and increasingly high-resolution inspection imagery. Advanced practice combines physics-informed performance models (PVsyst-style yield baselines, PR / CUF diagnostics) with ML forecasting, anomaly detection and Computer Vision — so that underperformance is attributed correctly (weather vs soiling vs clipping vs equipment fault vs curtailment).

This intermediate-to-advanced track retains core solar engineering context — irradiance geometry, module / inverter / BOS behaviour, SLD / DC–AC design awareness, rooftop-to-utility layouts and bankability thinking — while deepening AI methods for multi-horizon generation forecasts, inverter and tracker PdM, thermal / RGB defect detection, tracker and plant optimisation, and EMS co-control of PV with BESS and EV charging.

You will also address distributed vs centralised plant data architectures, labelling and drift in production models, and how AI outputs feed O&M tickets, cleaning schedules, warranty claims and commercial reporting — with clear pathways into AI in Wind and the broader AI in Renewable Energy Systems programme.

Technical focus areas

Irradiance & power ML

GHI/DNI/POA features, satellite + ground fusion, probabilistic PV forecasts.

Plant diagnostics

PR/CUF analytics, string/inverter residuals, clipping, soiling and mismatch losses.

Inspection AI

UAV thermal/RGB pipelines for hotspots, cracks, soiling and tracker faults.

EMS & hybrids

PV–BESS–grid–EV coordination, peak shaving and forecast-aware dispatch.

What you will learn

  • Connect PV / CSP fundamentals and BOS architecture to AI-ready data models
  • Engineer features from weather, satellite, pyranometer, inverter and string SCADA data
  • Build multi-horizon irradiance and AC power forecasts with uncertainty quantification
  • Diagnose performance losses using PR, expected energy baselines and residual analytics
  • Design PdM for inverters, trackers, transformers and DC collection equipment
  • Deploy CV workflows for hotspots, cracks, delamination, soiling and vegetation / shading
  • Separate soiling, weather, clipping, curtailment and equipment failure signatures
  • Optimise tracker strategies and plant set-points with forecast and constraint awareness
  • Co-optimise PV with BESS and EV charging under tariff, demand and grid limits
  • Retain SLD / BOQ / bankability framing when translating AI insights to project decisions
  • Apply MLOps practices: labelling, drift monitoring, false-positive cost and operator ownership
  • Scope AI roadmaps for rooftop, C&I and utility fleets with different telemetry maturity

Curriculum modules

  • 1. Solar Plant Physics, Electrical Architecture & AI Data Foundations

    Irradiance components (GHI/DNI/DHI/POA), temperature and spectral effects; module / string / inverter topology; MPPT and clipping behaviour; tracker types; CSP adjacency; metering and SCADA tag design for analytics; defining prediction targets and loss categories.

    • PR, CUF, specific yield and availability KPIs
    • Data quality: timestamps, calibration, missing inverters, communication gaps
    • Physics baselines vs purely data-driven models — when to blend
  • 2. AI for Solar Resource & Probabilistic Generation Forecasting

    Nowcasting to day-ahead (and longer) PV forecasts using NWP, satellite cloud fields, ground sensors and plant lags. Quantile / ensemble methods; ramp and cloud-edge events; site adaptation; skill evaluation against persistence and numerical baselines.

    • Plant vs regional aggregation for portfolio forecasting
    • Forecast use in scheduling, imbalance risk and EMS set-points
    • Linking PVsyst-style long-term yield to operational forecast systems
  • 3. Physics-Informed PV Performance Analytics & Optimization

    Expected energy models; string/inverter residual heatmaps; DC/AC ratio and clipping diagnostics; soiling ratio estimation; mismatch and shading patterns; temperature derating; identifying underperforming blocks and actionable set-point / O&M responses.

    • Digital twin / digital thread concepts for plant performance
    • Fleet benchmarking across similar sites and technologies
    • From analytics to cleaning, rewiring or inverter service decisions
  • 4. Predictive Maintenance & Fault Detection for Plant Equipment

    Inverter failure precursors, tracker motor / encoder anomalies, transformer and MV collection issues, DC combiner problems. Multivariate anomaly detection, alarm rationalisation, spare-part and SLA implications, and integration with CMMS tickets.

    • Failure-mode libraries and labelled event datasets
    • Lead-time vs false-positive trade-offs for field crews
    • Warranty / claim evidence packs from analytics
  • 5. Computer Vision & Intelligent Solar Inspection

    UAV thermal and RGB missions; IR hotspot physics; defect taxonomies (hotspots, cracks, delamination, discoloration, soiling, vegetation, tracker misalignment); detection/segmentation models; georeferencing findings to string / table IDs; human-in-the-loop severity review.

    • Campaign design: flight parameters, irradiance conditions, repeatability
    • Change detection across seasonal inspections
    • Closing the loop: finding → work order → post-repair verification
  • 6. AI for Utility / C&I Farms, Trackers & Smart Energy Management

    Plant-level optimisation under irradiance forecasts and grid limits; tracker strategy analytics; curtailment and export-limit management; EMS architectures for on-site load and peak shaving; SLD / layout awareness when recommending operational changes.

    • Rooftop vs ground-mount vs floating PV differences for AI
    • Portfolio dashboards and multi-site MLOps
    • Cybersecurity and OT/IT data-path considerations
  • 7. AI + Storage, Hybrid Systems & Solar EV Charging

    Forecast-aware BESS charge/discharge; SoC and degradation-aware strategies; hybrid PV–wind–diesel–grid configurations; solar EV charging co-optimisation against tariffs, demand charges and solar availability; linking EMS decisions to commercial KPIs.

    • Rule-based vs optimisation / RL-style EMS concepts
    • Metering, settlement and constraint modelling awareness
    • Capstone-style scenario design for hybrid sites

Skills you build

  • Physics-informed PV performance and loss attribution
  • Multi-horizon probabilistic solar / power forecasting
  • Inverter–string–tracker SCADA feature engineering
  • PdM and anomaly pipelines for plant equipment
  • Thermal / RGB CV inspection and georeferenced triage
  • Tracker and plant optimisation under constraints
  • PV–BESS–EV EMS co-optimisation literacy
  • Bankability / SLD-aware translation of AI insights to actions

Who this is for

  • PV design, EPC, O&M and performance engineers adding AI depth
  • SCADA / monitoring analysts moving into predictive analytics
  • Data / ML practitioners entering solar asset intelligence
  • EMS, storage and EV-charging professionals working with PV
  • Asset owners / managers needing advanced digital O&M literacy

Methods & tooling awareness

Conceptual exposure to PVsyst-oriented yield baselines, time-series ML, gradient boosting / sequence models, anomaly detection, CV detection/segmentation, plant historians and EMS dashboards — oriented to engineering decisions, not software-vendor lock-in.

Key AI use cases (intermediate–advanced)

Probabilistic PV Generation Forecasting

Fuse NWP, satellite and plant data for multi-horizon power forecasts with uncertainty for scheduling and EMS control.

Loss Attribution & PR Diagnostics

Separate weather, soiling, clipping, mismatch, downtime and curtailment contributions to underperformance.

Inverter & Tracker PdM

Detect precursor signatures and raise calibrated work orders before major production loss.

UAV Thermal / RGB Inspection

Automate hotspot and module-defect detection, map findings to plant topology and verify repairs.

Soiling Intelligence

Estimate soiling loss trajectories and recommend data-driven cleaning intervals under local climate.

Tracker & Plant Optimisation

Improve energy yield under forecast irradiance, shading and export constraints.

PV + BESS Dispatch

Optimise charge/discharge against tariffs, peaks and forecast uncertainty while respecting SoC limits.

Solar EV Charging Coordination

Align charging sessions with on-site solar and storage to cut grid import and demand charges.

Industry perspective

Advanced solar operations move from static design models to continuous learning systems: Solar Data → Physics Baselines → AI Models → Attribution → Optimisation → Automated / Assisted Decisions.

Graduates should be able to contribute to intelligent O&M, plant performance engineering, EMS programmes and digital renewable operations — with enough depth to challenge naive ML claims and insist on operationally valid metrics.

Enquire about AI in Solar Energy

Explore other School of Energy tracks

AI in Renewable Energy Systems
AI in Wind Energy
Fuel Cells / PEMFC
Electric Vehicle Engineering
AI Industry Specializations
School of Energy hub

Ready to start this energy track?

Enquire with RIA for batch schedules, mentoring pathways and project support.

ENQUIRE NOW
Go To Top