Toptal increases HVAC energy model prediction accuracy by 27% with machine learning.
Toptal partnered with an HVAC industry leader to overhaul its energy usage prediction models, improving accuracy, scalability, and cost-effectiveness through advanced machine learning techniques and open-source technologies.
Client
A global HVAC and refrigeration company delivering intelligent climate and energy solutions for buildings and cold chain.
Employees
Revenue
Industry
IndustrialsDelivered Services
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Schedule a CallChallenge
The company struggled with inaccurate and expensive models for predicting energy usage, compounded by the complexity of processing petabytes of real-time sensor data.
Solution
Scalable Model Implementation
Toptal transitioned the outdated model to a machine learning framework that utilized distributed computing with Kubernetes and Dask, enabling the parallel training of more than 500,000 lightweight, personalized models for enhanced scalability and speed.
Hybrid Modeling Strategy
Toptal combined physics-based modeling with machine learning, allowing for the creation of adaptive and interpretable models tailored to individual users, which improved the usability and actionability of the energy predictions.
Outcome
Accuracy Lifted by 27%, Reaching 85%
The new models increased prediction accuracy from 67% to 85%, making the energy usage predictions more reliable and actionable for the client.
Cost-effective Technology Transition
By moving from Amazon SageMaker to open-source technologies like Kubeflow and Dask, Toptal reduced the costs associated with training and deploying the models, while also improving performance and scalability.
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