Developing a Predictive AI Model for Renewable Energy Price Forecasting
A NYC-based AI x SaaS renewable energy startup needed a machine learning model to predict electricity prices, enabling their platform users to optimize when to buy, sell, and store power.
Industry
Renewable Energy & SaaS
Location
New York, NY
Timeline
12 weeks
Challenge
Inaccurate price forecasts preventing actionable trading signals
Strategic Approach
Knight
Knight Labs' approach
King
Core forecasting model
Bishop
Strategic feature engineering
Queen
Multi-market expansion
Client
GridWise Energy
Timeline
12 weeks
Focus Areas
Tech Stack
The Challenge
The startup was building a platform to help renewable energy producers and commercial electricity buyers optimize their trading strategies. The core problem: electricity prices in deregulated markets fluctuate dramatically based on weather, demand patterns, grid capacity, fuel costs, and regulatory events. Their platform users needed accurate 24-72 hour price forecasts to make informed decisions about when to sell excess solar/wind generation, when to purchase from the grid, and when to charge/discharge battery storage systems. Existing forecasting approaches the team had tried were producing error rates too high to generate actionable trading signals.
The Solution
We researched, designed, and developed a deep learning time-series forecasting model that predicts hourly electricity prices across multiple ISO markets (NYISO, PJM, ERCOT). The model architecture combines temporal convolutional networks with attention mechanisms, ingesting historical price data, real-time weather feeds, grid load forecasts, generation mix data, and natural gas futures prices. We built an automated retraining pipeline that updates the model daily with new market data, and implemented a backtesting framework that validates predictions against historical outcomes. The model outputs probabilistic forecasts with confidence intervals, enabling the platform to surface risk-adjusted trading recommendations.
The Result
The model achieved a mean absolute percentage error (MAPE) of 8.2% on 24-hour forecasts, outperforming the client's previous approach by 3x. Platform users saw a 22% average increase in trading profitability within the first quarter. The improved forecasting capability was a key factor in the startup securing their $4.5M Series A round, and they've since expanded from 3 ISO markets to 7.
Forecast Accuracy (MAPE)
Trading Profit Increase
Series A Secured
ISO Markets Covered