Keeling LabsWe develop ML that optimizes how batteries in the grid store energy

Keeling Labs: Pioneering Clean Energy Solutions

Introduction

At Keeling Labs, we harness the power of machine learning to tackle one of the world's most pressing issues—climate change. Our innovative solutions aim to optimize energy storage and trading, enabling a more sustainable and efficient energy grid.

Key Features

• Self-Optimizing Energy Storage: Our models teach themselves how to optimally charge or discharge batteries to maximize clean energy usage.
• Energy Trading Pilot: Deployed in California (CAISO) to optimize standalone energy trading.
• Real-World Machine Learning: Our models are implemented in real-world scenarios, not just simulations or academic papers.
• Reinforcement Learning: Continuously adapting models to changing energy scenarios.
• Direct Carbon Emission Reduction: Our work directly influences how clean energy displaces carbon emissions in the grid.

Use Cases

• Grid Optimization: Enhancing the efficiency of energy storage systems to support a 100% clean electricity grid.
• Energy Trading: Improving the profitability and sustainability of energy trading operations.
• Carbon Emission Reduction: Directly reducing carbon emissions by optimizing the use of clean energy sources.

Pricing

Our pricing model is tailored to meet the needs of various stakeholders in the energy sector. Contact us for detailed pricing information and customized solutions.

Teams

We are a dedicated team of engineers and researchers committed to solving climate change through technology. Based in Los Angeles, we leverage the region's engineering prowess and vibrant culture to drive innovation in clean energy solutions.

Join us in our mission to create a sustainable future. Explore our job board for exciting career opportunities.