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Layer 2: Prediction Engine

Production Ready Python License: MIT Layer 2 provides production-ready machine learning models for energy forecasting, demand prediction, and renewable generation analysis. Building on standardized data from Layer 1, this layer delivers sub-second predictions for energy system optimization.

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Available Features

Solar Generation Forecasting

Weather-based solar PV generation predictions with confidence intervals

Demand & Load Forecasting

Electricity demand prediction with behavioral and seasonal patterns

Feature Engineering

Automated feature extraction for energy time series data

Production APIs

Sub-second inference with batch and real-time prediction modes

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Quick Start

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Installation

pip install qubit-energy-forecasting

# With specific ML backends
pip install qubit-energy-forecasting[deep]     # TensorFlow
pip install qubit-energy-forecasting[prophet]  # Facebook Prophet
pip install qubit-energy-forecasting[all]      # All models

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Generate Your First Forecast

from qubit.forecasting import SolarForecaster
import pandas as pd

# Initialize forecaster
forecaster = SolarForecaster(
    system_capacity_kw=1000,
    panel_type="polycrystalline"
)

# Train on historical data
forecaster.fit(X_train, y_train)

# Generate 24-hour forecast
forecast = forecaster.predict(
    weather_data,
    horizon="24h",
    confidence_levels=[0.80, 0.95]
)

print(f"Peak generation: {forecast.peak_value:.2f} kW")
print(f"Total energy: {forecast.total:.2f} kWh")

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Architecture

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Technology Stack

  • Python 3.9+ - Primary language
  • scikit-learn - Classical ML algorithms
  • TensorFlow/Keras - Deep learning models
  • XGBoost/LightGBM - Gradient boosting
  • Facebook Prophet - Time series forecasting

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Model Performance

Performance metrics from production deployments across 100+ energy assets.

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Solar Generation Forecasting

HorizonMAPERMSEPeak Accuracy
1 hour8.5%12.3 kW94.2%
24 hours15.2%28.1 kW87.6%
7 days22.8%41.7 kW78.3%

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Demand Forecasting

Customer TypeMAPERMSELoad Factor
Residential12.1%5.8 kW0.68
Commercial8.9%15.2 kW0.74
Industrial6.3%42.1 kW0.82

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Real-World Applications

  • 15-minute load forecasting for grid balancing
  • Day-ahead renewable integration planning
  • Week-ahead maintenance scheduling optimization
  • Seasonal capacity planning and resource allocation
  • Intraday generation forecasting for trading
  • Weather-dependent O&M scheduling
  • Performance monitoring and anomaly detection
  • Financial revenue and P&L forecasting
  • Peer-to-peer trading optimization
  • Storage dispatch scheduling
  • EV charging load coordination
  • Demand response event planning

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Integration with Qubit Stack

Layer 2 seamlessly integrates with other Qubit Foundation components:
1

Data Input

Consumes standardized TimeSeries from Layer 1 (Schemas, Connectors, Adapters)
2

Feature Engineering

Automatically extracts time, weather, calendar, and lag features
3

Model Training

Trains specialized models for each energy domain and asset type
4

Prediction Generation

Outputs forecasts in TimeSeries format for Layer 3 optimization

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Available Models

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Time Series Models

ARIMA

Classical autoregressive models for stable patterns

Prophet

Handles seasonality and holidays automatically

LSTM

Deep learning for complex temporal dependencies

XGBoost

Gradient boosting for feature-rich predictions

Random Forest

Ensemble methods with uncertainty quantification

Ensemble

Combines multiple models for best accuracy

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Domain-Specific Forecasters

from qubit.forecasting.solar import SolarForecaster

forecaster = SolarForecaster(
    system_capacity_kw=5000,
    panel_type="monocrystalline",
    tilt_angle=35,
    azimuth=180
)

# Integrates weather data automatically
forecast = forecaster.predict(
    historical_data,
    weather_forecast=weather_df,
    horizon="24h"
)

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Deployment Options

Docker Container

Single-node deployment for development and small installations

Kubernetes Cluster

Production deployment with auto-scaling and high availability

Serverless Functions

Event-driven predictions for variable workloads

Edge Computing

Local inference for latency-sensitive applications

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Example Deployment

apiVersion: apps/v1
kind: Deployment
metadata:
  name: energy-forecaster
spec:
  replicas: 3
  template:
    spec:
      containers:
      - name: forecaster
        image: qubit/energy-forecasting:latest
        resources:
          requests:
            cpu: 1000m
            memory: 2Gi
        env:
        - name: MODEL_CACHE_SIZE
          value: "10"

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Next Steps

Get Started

Install and run your first forecast in 5 minutes

Forecasting Models

Deep dive into available prediction models

GitHub Repository

Explore source code and contribute to development

Integration Guide

Connect Layer 2 with your Layer 1 data pipeline
Layer 2 Prediction Engine is production-ready and powering forecasts across renewable energy, utilities, and smart grid applications worldwide.