Energy Forecasting Models

Layer 2 provides state-of-the-art machine learning models specifically designed for energy domain challenges. Each model is optimized for different forecasting horizons, data patterns, and accuracy requirements.

Model Categories

Time Series Models

Classical and modern approaches for temporal pattern recognition

Weather-Based Models

Renewable generation forecasting with meteorological data

Behavioral Models

Demand forecasting incorporating human behavior patterns

Ensemble Methods

Combining multiple models for optimal accuracy

Available Models

Classical Time Series

ARIMA
Prophet
Exponential Smoothing

Auto-Regressive Integrated Moving AverageBest for: Stable patterns, linear trends, short-term forecasting

from qubit.forecasting.models import ARIMAForecaster

forecaster = ARIMAForecaster(
    order=(2, 1, 2),  # (p, d, q)
    seasonal_order=(1, 1, 1, 24),  # Daily seasonality
    auto_select=True  # Automatic parameter selection
)

forecaster.fit(historical_data)
forecast = forecaster.predict(horizon="24h")

Strengths:

Fast training and inference
Interpretable parameters
Confidence intervals included
Good for stable seasonal patterns

Limitations:

Assumes linear relationships
Limited external feature support
Poor with non-stationary data

Machine Learning Models

Random Forest
XGBoost
Support Vector Regression

Ensemble of Decision TreesBest for: Feature-rich data, non-linear patterns, uncertainty quantification

from qubit.forecasting.models import RandomForestForecaster

forecaster = RandomForestForecaster(
    n_estimators=100,
    max_depth=15,
    min_samples_split=5,
    bootstrap=True
)

# Include weather and calendar features
features = feature_engineer.extract(
    energy_data,
    include_weather=True,
    include_calendar=True,
    include_lag=True
)

forecaster.fit(features, target_values)
forecast = forecaster.predict(future_features)

Feature Importance Analysis:

importance = forecaster.get_feature_importance()
print(importance.head(10))

# Output:
# temperature             0.234
# hour_sin               0.187
# load_lag_24h           0.156
# is_weekend             0.098
# cloud_cover            0.089

Deep Learning Models

LSTM Networks
GRU Networks
Transformer Models

Long Short-Term MemoryBest for: Long-term dependencies, complex temporal patterns

from qubit.forecasting.models import LSTMForecaster

forecaster = LSTMForecaster(
    lstm_units=[128, 64],
    dropout=0.2,
    lookback_window=168,  # 1 week of hourly data
    forecast_horizon=24,
    batch_size=32,
    epochs=100
)

# Multi-step ahead prediction
forecaster.fit(
    X_train, y_train,
    validation_split=0.2,
    early_stopping=True
)

forecast = forecaster.predict(X_test)

Architecture Visualization:

forecaster.plot_model_architecture()
forecaster.plot_training_history()

Sequence-to-Sequence Prediction:

# Input: 7 days of hourly data
# Output: Next 24 hours
forecast = forecaster.predict_sequence(
    input_sequence,
    horizon=24
)

Domain-Specific Forecasters

Solar Generation Forecasting

Physical Solar Model

Combines physics-based solar calculations with ML corrections:

from qubit.forecasting.solar import PhysicalSolarForecaster

forecaster = PhysicalSolarForecaster(
    system_capacity_kw=1000,
    panel_type='monocrystalline',
    tilt_angle=35,
    azimuth=180,
    location={'lat': 37.7749, 'lon': -122.4194}
)

# Uses solar position algorithms + weather data
forecast = forecaster.predict(
    weather_forecast,
    include_physics=True,
    ml_correction=True
)

Cloud Motion Tracking

Satellite imagery analysis for sub-hour forecasting:

from qubit.forecasting.solar import CloudMotionForecaster

forecaster = CloudMotionForecaster(
    satellite_provider='goes_16',
    tracking_method='optical_flow',
    nowcasting_horizon='3h'
)

# Requires satellite imagery API
forecast = forecaster.predict(
    current_irradiance,
    satellite_images=imagery_data
)

PV Performance Model

System-specific performance modeling:

from qubit.forecasting.solar import PVPerformanceForecaster

forecaster = PVPerformanceForecaster(
    pv_model='sandia',
    inverter_model='cec',
    degradation_rate=0.005  # Annual
)

# Temperature coefficient correction
forecast = forecaster.predict(
    irradiance_forecast,
    temperature_forecast,
    wind_forecast
)

Demand & Load Forecasting

Customer Segmentation Models

Different models for different customer types:

from qubit.forecasting.demand import SegmentedDemandForecaster

forecaster = SegmentedDemandForecaster(
    segments={
        'residential': ResidentialModel(),
        'commercial': CommercialModel(),
        'industrial': IndustrialModel()
    }
)

# Automatic customer classification
forecast = forecaster.predict(
    customer_data,
    auto_segment=True
)

Price-Responsive Demand

Incorporates electricity pricing effects:

from qubit.forecasting.demand import PriceElasticDemandForecaster

forecaster = PriceElasticDemandForecaster(
    price_elasticity=-0.1,  # 10% demand reduction per 100% price increase
    income_elasticity=0.3
)

forecast = forecaster.predict(
    historical_load,
    price_forecast=prices_df
)

Weather-Sensitive Load

Temperature-dependent demand modeling:

from qubit.forecasting.demand import WeatherSensitiveForecaster

forecaster = WeatherSensitiveForecaster(
    cooling_threshold=22,  # Celsius
    heating_threshold=18,
    weather_lag=1  # 1-hour temperature lag
)

Wind Generation Forecasting

Wind Power Curve Model

Physics-based wind-to-power conversion:

from qubit.forecasting.wind import WindPowerForecaster

forecaster = WindPowerForecaster(
    turbine_type='vestas_v90',
    hub_height=80,  # meters
    rated_power=2000,  # kW
    cut_in_speed=3,   # m/s
    cut_out_speed=25  # m/s
)

# Wind speed to power conversion
forecast = forecaster.predict(
    wind_speed_forecast,
    wind_direction_forecast
)

Wake Effect Modeling

Turbine interaction effects in wind farms:

from qubit.forecasting.wind import WindFarmForecaster

forecaster = WindFarmForecaster(
    turbine_layout=turbine_coordinates,
    wake_model='jensen',
    terrain_roughness=0.1
)

Ensemble Methods

Model Combination Strategies

Weighted Average
Stacking
Dynamic Ensemble

from qubit.forecasting.ensemble import WeightedEnsemble

ensemble = WeightedEnsemble([
    ('prophet', ProphetForecaster()),
    ('xgboost', XGBoostForecaster()),
    ('lstm', LSTMForecaster())
], weights=[0.3, 0.4, 0.3])

# Automatic weight optimization
ensemble.optimize_weights(
    X_train, y_train,
    method='minimize_mse'
)

Model Selection Guide

Choose models based on your specific use case, data characteristics, and performance requirements.

Decision Matrix

Use Case	Recommended Model	Alternative	Training Time	Inference Speed
Short-term load (1-4h)	XGBoost	ARIMA	Medium	Fast
Day-ahead solar	Ensemble	Random Forest	Slow	Medium
Week-ahead demand	Prophet	LSTM	Fast	Fast
Real-time pricing	ARIMA	SVR	Fast	Very Fast
Seasonal planning	Prophet	Exponential Smoothing	Fast	Fast
Complex patterns	LSTM	Transformer	Very Slow	Slow

Data Requirements

Small Dataset (<1000 samples)

ARIMA
Exponential Smoothing
SVR
Simple ensemble

Medium Dataset (1k-10k samples)

Random Forest
XGBoost
Prophet
Weighted ensemble

Large Dataset (>10k samples)

LSTM/GRU
Transformer
Deep ensemble
Stacking ensemble

Very Large Dataset (>100k samples)

Distributed XGBoost
Multi-GPU LSTM
Transformer with attention
Neural ensemble

Advanced Features

Uncertainty Quantification

All models support multiple uncertainty estimation methods:

# Prediction intervals
forecast = forecaster.predict(
    X_test,
    confidence_levels=[0.5, 0.8, 0.95],
    method='quantile_regression'
)

# Monte Carlo dropout (for neural networks)
forecast = lstm_forecaster.predict(
    X_test,
    uncertainty_method='mc_dropout',
    mc_samples=100
)

# Ensemble variance
forecast = ensemble.predict(
    X_test,
    return_std=True,
    return_individual_predictions=True
)

Online Learning

Models that adapt to new data automatically:

from qubit.forecasting.online import OnlineForecaster

online_model = OnlineForecaster(
    base_model=XGBoostForecaster(),
    update_frequency='1h',
    window_size=8760,  # 1 year rolling window
    adaptation_rate=0.01
)

# Continuous learning
for new_data_point in data_stream:
    prediction = online_model.predict(new_data_point.features)
    online_model.update(new_data_point.features, new_data_point.target)

Multi-Horizon Forecasting

Generate predictions for multiple time horizons simultaneously:

from qubit.forecasting.multi import MultiHorizonForecaster

forecaster = MultiHorizonForecaster(
    horizons=['1h', '6h', '24h', '7d'],
    models={
        '1h': ARIMAForecaster(),
        '6h': XGBoostForecaster(),
        '24h': LSTMForecaster(),
        '7d': ProphetForecaster()
    }
)

multi_forecast = forecaster.predict(X_test)
print(f"1-hour: {multi_forecast['1h'].peak_value:.2f} kW")
print(f"24-hour: {multi_forecast['24h'].total:.2f} kWh")

Model Evaluation

Comprehensive Metrics

from qubit.forecasting.evaluation import ForecastEvaluator

evaluator = ForecastEvaluator(
    metrics=['mape', 'rmse', 'mae', 'peak_accuracy', 'energy_score']
)

results = evaluator.evaluate(
    y_true=test_targets,
    forecasts=model_predictions,
    timestamps=test_timestamps
)

# Energy-specific metrics
print(f"MAPE: {results.mape:.2%}")
print(f"Peak timing accuracy: {results.peak_accuracy:.2%}")
print(f"Energy score: {results.energy_score:.3f}")

Cross-Validation

Time series aware cross-validation:

from qubit.forecasting.validation import TimeSeriesCrossValidator

cv = TimeSeriesCrossValidator(
    n_splits=5,
    gap=24,  # 24-hour gap between train/test
    horizon=24  # 24-hour forecast horizon
)

cv_scores = cv.cross_validate(
    forecaster=XGBoostForecaster(),
    X=features,
    y=targets,
    metrics=['mape', 'rmse']
)

print(f"CV MAPE: {cv_scores['mape'].mean():.2%} ± {cv_scores['mape'].std():.2%}")

Next Steps

Getting Started

Build your first forecast in 5 minutes

GitHub Repository

Explore the full source code and examples

The forecasting models are continuously improved based on real-world deployment feedback and cutting-edge ML research.

Get Started

Layer 1: Data

Layer 2: Prediction

Layer 3: Optimization

Layer 4: Coordination

Layer 5: Settlement

Layer 6: Interface

​Energy Forecasting Models

​Model Categories

Time Series Models

Weather-Based Models

Behavioral Models

Ensemble Methods

​Available Models

​Classical Time Series

​Machine Learning Models

​Deep Learning Models

​Domain-Specific Forecasters

​Solar Generation Forecasting

​Demand & Load Forecasting

​Wind Generation Forecasting

​Ensemble Methods

​Model Combination Strategies

​Model Selection Guide

​Decision Matrix

​Data Requirements

Small Dataset (<1000 samples)

Medium Dataset (1k-10k samples)

Large Dataset (>10k samples)

Very Large Dataset (>100k samples)

​Advanced Features

​Uncertainty Quantification

​Online Learning

​Multi-Horizon Forecasting

​Model Evaluation

​Comprehensive Metrics

​Cross-Validation

​Next Steps

Getting Started

GitHub Repository

Energy Forecasting Models

Model Categories

Available Models

Classical Time Series

Machine Learning Models

Deep Learning Models

Domain-Specific Forecasters

Solar Generation Forecasting

Demand & Load Forecasting

Wind Generation Forecasting

Ensemble Methods

Model Combination Strategies

Model Selection Guide

Decision Matrix

Data Requirements

Advanced Features

Uncertainty Quantification

Online Learning

Multi-Horizon Forecasting

Model Evaluation

Comprehensive Metrics

Cross-Validation

Next Steps