Layer 2: Prediction Engine

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.

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

Quick Start

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

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")

Architecture

Technology Stack

Core ML
Data Processing
Production

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

Model Performance

Performance metrics from production deployments across 100+ energy assets.

Solar Generation Forecasting

Horizon	MAPE	RMSE	Peak Accuracy
1 hour	8.5%	12.3 kW	94.2%
24 hours	15.2%	28.1 kW	87.6%
7 days	22.8%	41.7 kW	78.3%

Demand Forecasting

Customer Type	MAPE	RMSE	Load Factor
Residential	12.1%	5.8 kW	0.68
Commercial	8.9%	15.2 kW	0.74
Industrial	6.3%	42.1 kW	0.82

Real-World Applications

Utility Grid Operations

15-minute load forecasting for grid balancing
Day-ahead renewable integration planning
Week-ahead maintenance scheduling optimization
Seasonal capacity planning and resource allocation

Solar Farm Management

Intraday generation forecasting for trading
Weather-dependent O&M scheduling
Performance monitoring and anomaly detection
Financial revenue and P&L forecasting

Energy Communities

Peer-to-peer trading optimization
Storage dispatch scheduling
EV charging load coordination
Demand response event planning

Integration with Qubit Stack

Layer 2 seamlessly integrates with other Qubit Foundation components:

Data Input

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

Feature Engineering

Automatically extracts time, weather, calendar, and lag features

Model Training

Trains specialized models for each energy domain and asset type

Prediction Generation

Outputs forecasts in TimeSeries format for Layer 3 optimization

Available Models

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

Domain-Specific Forecasters

Solar Generation
Demand Forecasting

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"
)

from qubit.forecasting.demand import DemandForecaster

forecaster = DemandForecaster(
    customer_type="commercial",
    include_weather=True,
    include_calendar=True
)

forecast = forecaster.predict(
    load_history,
    temperature_forecast=temp_df,
    horizon="24h"
)

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

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"

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.

​Layer 2: Prediction Engine

​Available Features

Solar Generation Forecasting

Demand & Load Forecasting

Feature Engineering

Production APIs

​Quick Start

​Installation

​Generate Your First Forecast

​Architecture

​Technology Stack

​Model Performance

​Solar Generation Forecasting

​Demand Forecasting

​Real-World Applications

​Integration with Qubit Stack

​Available Models

​Time Series Models

ARIMA

Prophet

LSTM

XGBoost

Random Forest

Ensemble

​Domain-Specific Forecasters

​Deployment Options

Docker Container

Kubernetes Cluster

Serverless Functions

Edge Computing

​Example Deployment

​Next Steps

Get Started

Forecasting Models

GitHub Repository

Integration Guide

Layer 2: Prediction Engine

Available Features

Quick Start

Installation

Generate Your First Forecast

Architecture

Technology Stack

Model Performance

Solar Generation Forecasting

Demand Forecasting

Real-World Applications

Integration with Qubit Stack

Available Models

Time Series Models

Domain-Specific Forecasters

Deployment Options

Example Deployment

Next Steps