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Zentus

Optimizing Renewables and Storage Through Intelligent Forecasting

Zentus is a renewable energy technology company focused on building intelligent forecasting systems that maximize revenue from renewable trading and operations. Supported by the Stanford Doerr School of Sustainability Accelerator, we combine deep energy domain expertise with advanced AI to solve critical challenges in high-wind energy markets.

🎯 Our Mission

We develop physics-informed machine learning models that are optimized for market impact, not just accuracy metrics. Our goal is to help renewable energy operators and traders make better decision s during periods of high volatility, ultimately improving profitability and grid stability.

🌍 The Challenge We Address

Energy markets with high renewable penetration (ERCOT, Germany, Netherlands, UK, Nordics, etc.) face increasing intraday price volatility. Traders and operators struggle with:

  • Timing storage dispatches and cycling decisions
  • Avoiding negative price periods
  • Choosing between day-ahead, real-time, and ancillary markets
  • Predicting extreme price hours where true trading value lies

Conventional forecasting models minimize statistical error (RMSE) rather than financial outcomes, missing the true value drivers in energy trading.

💡 Our Solution

We co-develop and validate forecasting models through a four-stage process:

  1. Data Integration: Combine market and operational data with physical insights
  2. AI Modelling: Build adaptive forecasting systems using physics-informed ML
  3. Trading Decisions: Enable optimal operational choices
  4. P&L Improvement: Quantify and capture value

Our pilot-first collaboration approach ensures rapid validation, iterative refinement, and measurable outcomes for partners.

🔬 Technical Expertise

Our team brings world-class experience in:

  • Ultra-short-term probabilistic wind farm modeling
  • Model Predictive Control (MPC)
  • High-performance computing
  • Big data engineering
  • Physics-informed neural networks
  • Energy storage intelligence
  • Remote sensing and instrumentation (LiDAR, acoustic tomography)

🤝 Our Track Record

Team members have contributed to leading R&D projects with:

  • NREL (National Renewable Energy Laboratory)
  • ENGIE
  • Ocean Winds
  • KU Leuven
  • ForWind Center for Wind Energy Research
  • AWAKEN Field Campaign
  • TotalControl (EU H2020)
  • FarmConners (EU H2020)

🚀 Current Focus

We're actively building:

  • ERCOT market analysis infrastructure: Data pipelines and analytics for understanding volatility and price extremes
  • Forecasting prototypes: Models that predict extreme price hours and optimize trading strategies
  • Pilot collaborations: Working with renewable energy operators to validate and scale our solutions

📫 Connect With Us


Interested in a pilot collaboration? Connect with us to explore how intelligent forecasting can transform your trading profitability and operational efficiency in high-wind markets.

Popular repositories Loading

  1. ERCOT-Dashboard ERCOT-Dashboard Public

    Interactive Streamlit dashboard demonstrating battery storage revenue opportunities in ERCOT markets through improved forecasting. Engie Urja AI Challenge 2025

    Python 2

  2. zentus-io.github.io zentus-io.github.io Public

    Placeholder Website

    HTML 1

  3. OpenWindSCADA OpenWindSCADA Public

    Forked from sltzgs/OpenWindSCADA

    A curated list of open wind turbine data sets and corresponding code

    Jupyter Notebook 1

  4. .github .github Public

  5. IEA-15-240-RWT IEA-15-240-RWT Public

    Forked from IEAWindSystems/IEA-15-240-RWT

    15MW reference wind turbine repository developed in conjunction with IEA Wind

    Python

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