- Energy
Boosting energy demand forecast accuracy by 24% for thousands of NYC buildings
Overview
Manual energy forecasting process limited accuracy and scalability
An energy management firm needed to forecast electricity demand across approximately 1,000 buildings in New York to support demand-response and energy-efficiency programs such as NYISO’s Special Case Response.
The existing process was slow, manual, and error-prone—relying on spreadsheets and rough estimations to determine seasonal commitments. The forecasts had to be both granular (hourly) and long-range (up to six months), while accounting for complex, interacting seasonal patterns across a wide variety of building types.
Our Solution
AI-powered forecasting engine enables smarter, scalable planning
OneSix developed a forecasting and simulation engine powered by deep learning. The solution included:
- A hybrid model that combines classical state-space forecasting with a neural network architecture, allowing the system to capture nuanced seasonal patterns and their interactions.
- Fourier-transformed inputs to effectively encode cyclical time-based features such as hour-of-day, day-of-week, and season-of-year.
- A shared model architecture fine-tuned to each building, enabling scalability across diverse asset types—from subway stations and K–12 schools to wastewater treatment facilities.
- Simulated future weather and load conditions to estimate global, correlated risk and support smarter demand-response planning.
The system was built using PyTorch and integrated with Torchcast to manage temporal data modeling and training workflows.
Results
Improved accuracy, automation, and program impact
- 24% improvement in forecast accuracy during high-demand scenarios.
- Automated, reliable seasonal projections across 1,000+ buildings, reducing manual effort and minimizing risk of human error.
- Now serves as the foundation for setting program commitments, replacing the legacy manual process with a scalable, data-driven forecasting approach.
Ready to unlock the full potential of data and AI?
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