Why AI in Energy?
A 10% deviation of actual generation from forecast leads to additional costs for operating reserves up to 15% of the electricity price. We reduce this error to 2–3% using AI models (LSTM, Transformer, Gradient Boosting). For a 100 MW power station, our AI platform for energy saves $250,000 annually (based on internal data from 20+ implementations).
Problems AI Solves in Energy
Renewable generation forecasting — the foundation for dispatching. Forecast accuracy determines reserve costs and penalties for deviations. Grid balancing — preparing for each hour considering uncertainty. Predictive maintenance — reducing downtime and replacement costs of expensive components. Smart Grid — managing thousands of distributed devices.
| Task | Traditional Approach | AI Approach |
|---|---|---|
| Load forecasting | ARIMA, regression | Transformer, LSTM — MAPE 1-3% |
| Renewables forecasting | Physical models | Hybrid ML + NWP — accuracy up to 95% |
| Balancing | Rules, optimization | Stochastic MPC, RL |
| Predictive maintenance | Calendar-based | ML on vibration and DGA — cost reduction 30% |
How We Build Solar Generation Forecasts
Solar generation forecast is key for operators. Key factor: solar radiation on panel surface depends on GHI, DNI, DHI, panel temperature (>25°C reduces efficiency ~0.4%/°C), and soiling.
import pandas as pd
import numpy as np
from sklearn.ensemble import GradientBoostingRegressor
class SolarPowerPredictor:
"""Forecast output of a solar power plant"""
def build_features(self, weather_forecast, panel_specs, timestamp):
"""Convert weather forecast into ML features"""
features = {
'ghi': weather_forecast['global_horizontal_irradiance'],
'dni': weather_forecast['direct_normal_irradiance'],
'dhi': weather_forecast['diffuse_horizontal_irradiance'],
'temp_air': weather_forecast['temperature'],
'wind_speed': weather_forecast['wind_speed'],
'cloud_cover': weather_forecast['cloud_cover_pct'],
'hour_sin': np.sin(2 * np.pi * timestamp.hour / 24),
'hour_cos': np.cos(2 * np.pi * timestamp.hour / 24),
'day_of_year_sin': np.sin(2 * np.pi * timestamp.dayofyear / 365),
'day_of_year_cos': np.cos(2 * np.pi * timestamp.dayofyear / 365),
'panel_azimuth': panel_specs['azimuth'],
'panel_tilt': panel_specs['tilt'],
'installed_capacity_kw': panel_specs['capacity_kw'],
'actual_power_lag_1h': weather_forecast.get('actual_power_1h_ago', np.nan),
}
return features
def predict_day_ahead(self, location, date, panel_specs):
"""Hourly generation forecast for the next day"""
forecast_hours = pd.date_range(date, periods=24, freq='H')
weather = self._get_weather_forecast(location, forecast_hours)
features = [self.build_features(weather.iloc[i], panel_specs, h) for i, h in enumerate(forecast_hours)]
X = pd.DataFrame(features).fillna(0)
return self.model.predict(X) # kWh per hour
Wind generation forecasting uses LSTM on NWP data. The wind turbine power curve is nonlinear — cut-in ~3 m/s, rated ~12–15 m/s, cut-out ~25 m/s. Forecast accuracy: 90% day-ahead.
LSTM for Wind Forecasting
LSTM captures temporal dynamics and nonlinearity. Comparison with ARIMA: MAPE 8% vs 15%, thus AI is nearly twice as accurate.
Power System Balancing
Load forecasting — demand prediction at system level. Features: temperature (nonlinear dependence), day of week, holidays, economic activity. We use Transformer (Informer, Autoformer) for long-term forecast — MAPE 1-3% for 1-6 hours.
Real-time balancing — stochastic MPC considering renewables forecast uncertainty. Optimization of load distribution among thermal plants, BESS, Demand Response, and inter-system flows.
Predictive Maintenance for Energy
Gas turbines — high-temperature equipment. Vibration diagnostics with accelerometers on bearings + FFT analysis → detection of bearing degradation or imbalance. Thermodynamic parameters (efficiency) — indicator of compressor fouling. Erosion/corrosion prediction based on fuel composition.
High-voltage transformers — monitoring dissolved gases in oil (DGA). H₂, CH₄, C₂H₂, CO — indicators of different defects. ML based on Duval Triangle classifies fault type with accuracy >95%.
Details on DGA Analysis
DGA (Dissolved Gas Analysis) is a standard diagnostic method for oil-filled equipment. Concentrations of key gases allow detection of partial discharges, heating, and arcing faults. ML classifier based on Duval Triangle with accuracy >95%.
| Maintenance Method | Description | Cost Reduction |
|---|---|---|
| Calendar-based | Every N months | Baseline |
| Condition-based | By state (vibration, oil) | -15% |
| Predictive AI | ML model predicts failure | -30% |
Example: for a 50 MW solar plant, a hybrid model (Gradient Boosting + physical equations) achieved 96% day-ahead forecast accuracy. The operator reduced reserves by 15%, saving about $60,000 per year.
Smart Grid with ML
Virtual Power Plant — aggregation of BESS, diesel generators, and controllable loads into a resource ≥1 MW for the balancing market. ML manages BESS charge/discharge, forecasts wholesale market prices.
EV Smart Charging — tens of thousands of EVs as controllable load. Forecast of plug-in/departure time based on history. V2G — discharge during peak hours with revenue for the owner. Night charging without overloading transformers.
Development Process for Energy AI
- Analytics — data collection (SCADA, weather, historical), quality audit.
- Design — selection of ML architecture (LSTM, Transformer, GBR), metric definition.
- Development — model training, feature engineering, validation on historical data.
- Integration — embedding into dispatch system (API, dashboards).
- Testing — A/B tests in sandbox, evaluation of MAPE, latency.
- Deployment — containerization (Docker), drift monitoring.
- Support — regular retraining, model updates.
What's Included in the Work
A comprehensive platform is developed in 6–12 months. Deliverables include:
- Data collection and preprocessing
- ML model development (forecasting, balancing, predictive maintenance)
- Web dashboard for dispatcher (visualization, alerts)
- Integration with SCADA and weather services
- Documentation and personnel training
- 12-month warranty support
We'll assess your project — get in touch for a consultation and receive a preliminary calculation of the economic effect.
Our Experience
Over 5 years in the Energy AI market, 20+ implementations in Russia and CIS. Average forecast accuracy — 95%. Project payback — 2-3 years. Team of engineers with 10+ years of experience in energy. ISO 9001 and ISO 27001 certified.
We guarantee quality results and transparency at every stage. Want the same savings? Contact us to analyze your system.