gridsmith 0.1.1

Orchestration and reference app layer for ML4U chapter examples

GridSmith

Orchestration and reference app layer for ML4U (Machine Learning for Utilities) chapter examples.

Overview

GridSmith provides a clean, layered architecture for running ML4U chapter examples. It composes algorithms from the Smith library ecosystem (timesmith, anomsmith, geosmith, ressmith, plotsmith) without implementing new algorithms itself.

Architecture

GridSmith is organized into four layers:

1. Core Layer: Domain objects, configs, and pipelines that compose Smith libraries
2. API Layer: Stable public interface with user-friendly entrypoints
3. CLI Layer: Terminal interface for running pipelines
4. Examples Layer: Runnable chapter examples with documentation

Quick Start

Installation

Basic installation:

pip install gridsmith

With Smith libraries (recommended for full functionality):

pip install gridsmith[smith]
# or
pip install -r requirements-smith.txt

Running a Pipeline

Using the CLI:

gridsmith run ami-anomaly --config configs/ami_anomaly.yaml

Using Python:

from gridsmith import GridSmithClient
from gridsmith.api.config import AMIAnomalyConfig

client = GridSmithClient()
config = AMIAnomalyConfig(
    input_path="data/ami_data.csv",
    output_dir="runs/output",
)

results = client.ami_anomaly(config)

Available Pipelines

GridSmith provides machine learning pipelines for utility operations. Here are the available pipelines:

Core Applications (Part II)

• ami-anomaly: AMI anomaly detection
• temperature-load: Temperature-to-load modeling
• ml-fundamentals: Regression, classification, clustering
• load-forecasting: Load forecasting with ARIMA/LSTM
• predictive-maintenance: Asset health monitoring
• outage-prediction: Storm outage prediction
• grid-optimization: Grid optimization with RL
• der-forecasting: Distributed energy resource forecasting
• demand-response: Customer load profiling

Advanced Techniques (Part III)

• computer-vision: Vegetation detection, PLD
• nlp: Log classification, entity extraction
• ai-utilities: LLM integration
• geospatial: Feeder mapping, asset location

Integration & Scale (Part IV)

• mlops: MLflow integration, model registry
• cybersecurity: Threat detection
• ethics: Fairness auditing
• roi-analysis: Cost-benefit analysis
• realtime-analytics: Streaming analytics
• compliance: SAIDI/SAIFI reporting
• feature-engineering: Temporal, geospatial features
• reliability: Reliability analytics
• market-operations: Price forecasting, bidding

Advanced Research

• causal-inference: Difference-in-differences, synthetic control
• multi-task-learning: MTL models

See Documentation for more details.

Project Structure

gridsmith/
├── src/gridsmith/
│   ├── core/          # Layer 1: Domain objects and pipelines
│   ├── api/           # Layer 2: Public interface
│   └── cli/           # Layer 3: Command-line interface
├── examples/          # Layer 4: Chapter examples
├── configs/           # Configuration files
├── docs/              # Documentation
└── tests/             # Test suite

Development

Setup

git clone <repository>
cd gridsmith
pip install -e ".[dev]"

Running Tests

pytest

Linting

ruff check .

Type Checking

mypy src/gridsmith

Documentation

• Architecture - System design and layer organization
• How to Run - Detailed usage instructions
• Data Formats - Input data requirements
• Chapter Examples - Chapter-specific examples

Design Principles

1. No new algorithms: GridSmith orchestrates existing Smith libraries
2. Pure orchestration: Core layer composes, doesn't compute
3. Stable API: Public interface hides internal structure
4. Runnable examples: Every chapter has a working example
5. Testable: Golden tests validate schema and metrics

Definition of Done

• ✅ Can run one command from a clean venv
• ✅ Get metrics, tables, and figures in a run folder
• ✅ README explains inputs and outputs
• ✅ Test asserts schema and metric keys

Integration Policy

GridSmith integrates with the Smith library ecosystem:

• timesmith: Time series forecasting and analysis
• anomsmith: Anomaly detection
• plotsmith: Data visualization

GridSmith uses graceful fallback - it works with or without Smith libraries installed:

• Attempts to use Smith libraries when available
• Falls back to local implementations if libraries are missing
• Supports multiple API patterns for maximum compatibility

See Integration Guide for details.

Dependencies

GridSmith depends on Smith libs by version:

• Pin minimal versions
• Avoid tight coupling to internal modules
• Import from public APIs only
• Graceful degradation if libraries unavailable

License

MIT License