AI & Data in Solar

Updated: March 2026

How artificial intelligence is transforming the sector

MAJOR TREND

1 AI is transforming solar

Why now?

The convergence of three factors is accelerating AI adoption in the solar sector:

Data explosion

Real-time monitoring, smart meters, weather data, satellites → terabytes of data available

Algorithm maturity

Deep learning, transformers, time series models → unprecedented accuracy

Growing financial stakes

Larger parks, volatile markets, imbalance penalties → every % counts

Key market figures

Global AI + Energy market $10 Mds

2024 → $30 Mds en 2030 (CAGR 20%)

OPEX reduction with AI 10-25%

Predictive maintenance, O&M optimization

Forecast improvement 30-50%

vs traditional statistical methods (NREL)

Optimized trading gains +15-30%

Merchant revenues with smart dispatch

Sources : McKinsey, NREL, BloombergNEF, MarketsandMarkets

2 Production Forecasting

Production forecasting is the most mature AI application in solar. It enables forecasting production at different horizons (D-1, H-1, sub-hourly) to optimize trading and reduce imbalance penalties on markets.

J-1

Day-ahead

Forecast for the next day, basis for market nominations.

AI Accuracy 90-95%

vs Persistence +20-30%

H-1

Intraday

Hourly adjustment to refine positions on the intraday market.

AI Accuracy 95-98%

Gap reduction 40-60%

15'

Nowcasting

Ultra short term for storage control and grid services.

Horizon 15-60 min

Usage Dispatch BESS

Models and techniques used

LSTM / GRU

Recurrent networks for time series

Transformers

Attention mechanism, state of the art

Ensemble ML

XGBoost, Random Forest, stacking

NWP + ML

Weather models + ML correction

💰 Financial impact of forecasting

For a park of 10 MWc in France :

Annual production ~12 000 MWh

Imbalance penalties (without AI) ~50-80 k€/an

Imbalance penalties (with AI) ~20-35 k€/an

Savings and ROI :

Annual savings 30-45 k€/an

Forecasting solution cost 5-15 k€/an

ROI 3-6x

3 Predictive Maintenance

Real-time anomaly detection

ML algorithms continuously analyze monitoring data (current, voltage, temperature, PR) to detect anomalies before they become critical.

Hotspots (abnormal heating)

Inverter degradation (failure patterns)

Soiling (progressive PR decrease)

Unexpected shading (vegetation, construction)

Cabling / connector faults

Image Analysis with Computer Vision

Drones equipped with thermal and RGB cameras fly over solar parks. AI automatically analyzes images to identify defects.

IR Thermography

Hotspot detection, faulty cells, bypass diodes

Detection accuracy : >95%

Electroluminescence (EL)

Microcracks, PID, cell degradation

Early detection before production impact

RGB Visual Inspection

Glass breakage, delamination, snail trails, soiling

Automated counting and classification

Benefits of predictive maintenance

-30%

Maintenance costs

(preventive vs corrective)

+2-3%

Recovered production

(fast detection)

>99%

Availability

(vs 97-98% without AI)

-50%

Inspection time

(drone + AI vs manual)

4 Trading & Optimisation

AI is revolutionizing solar electricity trading, especially for assets in merchant (exposed to market prices) or with storage.

Smart spot arbitrage

Spot price prediction

ML on price history, weather, demand, imports/exports

Nomination optimization

Day-ahead vs intraday vs imbalance arbitrage

Hedging strategies

Dynamic hedging, price risk management

Storage optimization (BESS)

Optimal dispatch

When to charge/discharge to maximize revenues

Multi-market co-optimization

Arbitrage + FCR + capacity simultaneously

Degradation management

Optimize cycles vs revenues vs lifetime

📈 Algorithmic trading gains

PV merchant

+5-15%

revenues vs baseline

PV + BESS optimisé

+15-30%

revenus vs simple rules

Imbalance reduction

-40-60%

imbalance costs

5 Players & Solutions du marché

Forecasting

• Reuniwatt (FR)

• Steadysun (FR)

• Solcast (AUS)

• Meteomatics (CH)

Monitoring & AM

• Also Energy (US)

• Bazefield (NO)

• 3E (BE)

• Greenbyte (SE)

AI Drone Inspection

• Raptor Maps (US)

• Above Surveying (UK)

• Sitemark (BE)

• DroneBase (US)

Trading / Optimization

• Fluence (US/DE)

• Stem (US)

• Habitat Energy (UK)

• Modo Energy (UK)

Digital Twin

• Akselos (CH)

• DNV (NO)

• GE Digital (US)

• Siemens (DE)

VPP Aggregators

• Next Kraftwerke (DE)

• Flexitricity (UK)

• Energy Pool (FR)

• Centrica (UK)

6 Ma vision : Finance + Data

Bridging two worlds

My 20-year career in market finance (trading, options, risk management) gives me a unique perspective on solar's digital transformation:

→ PPA pricing models resemble energy derivatives

→ Storage optimization = dynamic allocation problem

→ Resource risk management = VaR and stress testing

→ Forecasting = time series and stochastic models

Unique positioning

Profiles combining finance expertise, solar technical knowledge, and data/AI tool proficiency are rare and sought after.

Target positions

• Asset Manager - Digital / Data-driven

• Quantitative Analyst - Renewables

• Head of Trading - Solar/Storage

• Director Business Intelligence - Energy