Project: HELIOTECH
HelioTech.ai is an AI-powered software platform designed to monitor and predict renewable energy production in photovoltaic parks.
The platform combines AI forecasting models with real-time plant data to predict solar production from minutes to days ahead, helping operators reduce imbalance costs and detect underperformance early.
THE CHALLENGE
Solar output is variable. Grid obligations are not.
Photovoltaic parks must commit production schedules to grid operators and energy markets. Every deviation between forecast and actual output turns into imbalance costs, curtailment, or lost revenue.
Weather variability
Cloud cover, aerosols, and temperature change output within minutes, making static or persistence-based forecasts unreliable.
Imbalance penalties
Deviations from declared schedules are settled at imbalance prices that can erase a significant share of a park’s margin.
Hidden underperformance
Soiling, string faults, and inverter derating reduce yield gradually and often go unnoticed without continuous analytics.
Fragmented data
SCADA, inverter, weather station, and market data sit in separate systems with different formats and time resolutions.
Core Capabilities
AI models built for photovoltaic production.
HelioTech.AI combines plant telemetry, numerical weather prediction, and satellite data in one platform. Each module can run on its own or as part of the full forecasting and monitoring stack.
Data ingestion — M1 / M2 / M3
M1 · Plant data
SCADA & inverters
Real-time telemetry from inverters, strings, meters, and trackers.
M1 · Plant data
Weather stations
On-site irradiance, module temperature, and wind sensors.
M2 · Weather
NWP models
Numerical weather prediction feeds from multiple providers.
M3 · Satellite
Cloud imagery
Satellite-based cloud motion for intraday forecasts and nowcasting.
M3 · Market
Market & grid data
Day-ahead and intraday prices, curtailment signals, and schedules.
Forecasting engine — M4
M4 · Forecast
Gradient boosting · Deep learning · Ensemble blending · Probabilistic quantiles · Nowcasting 0–6h · Day-ahead to 7 days
Forecasts at 15-minute resolution with confidence intervals, retrained continuously on each park’s own production history.
Monitoring & integration — M5 / M6
M5 · Monitoring
Performance analytics
Expected vs. actual yield · Soiling and degradation detection · String and inverter fault alerts · Performance ratio tracking · Curtailment accounting
M6 · Integration
API & reporting
REST API and scheduled exports · Grid operator schedule formats · Trading desk integration · Role-based dashboards · Audit-ready reports
Who uses it
User
Park operators
Daily production schedules and O&M priorities.
User
Asset owners
Yield, losses, and performance across portfolios.
User
Energy traders
Day-ahead and intraday bids from probabilistic forecasts.
User
Grid operators
Reliable injection schedules for balancing.
User
Aggregators
Combined forecasts for virtual power plants.
Forecast Horizons
From minutes to days ahead.
Each horizon uses a different combination of models and data sources, chosen for the decision it supports.
NOWCAST
0–6 hours
0–6 hours
Satellite cloud motion and live telemetry for intraday trading and real-time dispatch.
INTRADAY
6–24 hours
6–24 hours
Updated weather model runs blended with plant data to correct the day-ahead schedule.
DAY-AHEAD
24–48 hours
24–48 hours
Schedules for market bidding and grid operator nominations, with confidence bands.
WEEK-AHEAD
2–7 days
2–7 days
Production outlook for maintenance planning and portfolio hedging.
MONITOR
Real time
Real time
Expected vs. actual comparison flags underperformance as it happens.
REVIEW
Post-event
Post-event
Forecast accuracy, losses, and root-cause reports for every period.
Performance Targets
Measurable KPIs, validated in the field
Accuracy is measured against metered production for every park and every horizon. The targets below apply to parks with at least 12 months of historical data.
<5%
Day-ahead forecast error (nMAE, normalized to capacity)
15 min
Forecast resolution across all horizons
7 days
Maximum forecast horizon
−30%
Imbalance costs vs. baseline forecast
>98%
Data availability from plant telemetry
<24h
Detection time for string and inverter faults
+2–4%
Recovered yield through early fault and soiling detection
100%
Forecasts delivered with confidence intervals
Target Applications
One platform, five use cases.
The same platform runs for a single utility-scale park or a portfolio of distributed rooftop installations.
Utility-scale parks
Production schedules and performance monitoring for ground-mounted PV.
PV portfolios
Aggregated forecasts and benchmarking across many sites.
Energy trading
Probabilistic inputs for day-ahead and intraday bidding.
PV + storage
Charge and discharge planning based on expected production.
Grid operators
Regional solar forecasts for balancing and congestion management.
Policy & Strategic Fit
Aligned with EU energy priorities.
HelioTech.AI supports the integration of variable solar generation into the grid, in line with EU climate and energy market objectives.
RED III
The revised Renewable Energy Directive raises renewable targets, which requires accurate forecasting to integrate variable solar generation into the grid.
Electricity Market Design
Reformed market rules increase the role of intraday trading and the balancing responsibility of renewable producers.
EU Solar Energy Strategy
Targets for rapid solar deployment that depend on reliable production forecasts and plant performance data.
EU AI Act
Documented models, traceable data sources, and accuracy reporting support transparent use of AI in energy systems.