The most widely deployed self-optimizing system for buildings
Our system issues almost 2 million daily commands to individual HVAC equipment components. This allows it to make accurate, granular adjustments autonomously and in real time, rapidly reducing operational inefficiencies and wasted energy.
Our solution conducts over 280 control strategy assessments per day for each HVAC component—achieving a level of optimization no human could match. This minimizes cycling and runtime, lengthening equipment life and cutting down on maintenance costs.
Measured in seconds, our system's rapid data retrieval rate enables our algorithms to continuously analyze, adjust, and react to changing conditions with unparalleled speed. For our customers, this agility means consistently optimal energy savings and occupant comfort.
Making virtual autonomous agents your reality
ARIA is on a constant journey of self-improvement, evolving in tandem with customer needs and the latest advancements in large language models. Automatically updated to meet the highest global standards in accuracy and performance, ARIA is always a step ahead.
ARIA follows the most advanced data security measures, ensuring your data is never used to train other models. This level of individualized data control also helps maintain consistently precise, reliable output, uniquely tailored to your building and needs.
Continuous contextual grounding checks enable consistently accurate responses based on relevant source information. This process enhances the reliability of ARIA's outputs, providing customers with trustworthy data and insights that support optimal building management and a higher return on investment.
Our standardized guardrails work to ensure unbiased and appropriate output, demonstrating our drive to champion responsible AI practices and safeguard customer data integrity.
The power of 20/20 foresight
Our models' exceptional space temperature predictive accuracy rate of 96% up to 6 hours in advance facilitates more informed real-time control strategies - executed entirely autonomously. This predictive power means we can develop control strategies for shorter cycle air side systems and longer cycle water side systems.
Our technology's ability to elicit a vast number of daily predictions allows us to deliver granular, real-time insights with high frequency actions. Our autonomous platform seizes every savings opportunity, large or small - no pareto analysis needed.
Unlocking AI's value with a comprehensive data management and workflow infrastructure
Hundreds of our models are automatically retrained every day, ensuring any drifts or errors are quickly corrected. This continuous process helps maintain optimal system performance while cutting down on equipment wear and maintenance costs.
We collect a large volume of data points daily, allowing for an extremely granular understanding of each building's operations. This enables more precise control over each piece of equipment, leading to more effective and efficient operations.
Our drive to be on the frontier of research means we have thousands of models in production and even more in development. We're constantly adopting, shaping, and integrating the most advanced deep learning techniques to ensure our system is ever evolving, delivering unmatched performance, and setting new industry standards.
Our solution integrates multiple layers of operational reliability verifications to meet peak performance criteria:
Our system processes an astounding 134 gigabytes of data per minute—the equivalent of streaming around 134 hours of Netflix every minute. This immense processing power enables us to handle vast amounts of data in real-time, ensuring rapid equipment analysis and timely interventions.
Our robust control layer enables precision control and automation of building systems, enhancing operational efficiency and reliability.
Our data handling engine processes vast amounts of data in real-time, providing quick and accurate insights.
Our tech stack seamlessly integrates data from multiple sources, including external APIs, ensuring a comprehensive and cohesive data ecosystem.
Our profound data repository engine (Data Lake) enables us to store and manage even the largest datasets with effortless efficiency.
Our multi-layered system empowers our AI to deliver consistent, reliable performance, ensuring optimal performance across every building.
Neural ordinary differential equations: A breakthrough in deep learning accuracy and efficiency
By integrating physics-based principles with black box deep learning techniques, our hybrid approach leverages the strengths of both methodologies, enhancing the interpretability and reliability of our models. This results in more accurate, robust, and trustworthy predictions supported by empirical data.
Traditional deep learning methods often require extensive training periods. With neural ODEs, we've sped up our model training time by 90%, reducing our training cycle from weeks to days - while maintaining or exceeding our accuracy rates.
With Neural ODEs, our model training requires 10 times less data than traditional deep learning models. Yet, even with a fraction of the data, we're able to achieve greater levels of accuracy.
Neural ODEs enable continuous-time modeling, enhancing the accuracy of our predictions. Unlike traditional models that operate on discrete time steps, neural ODEs provide a seamless representation of dynamic systems, capturing nuances and changes more precisely. This approach results in highly reliable models that adapt and improve over time.
By leveraging Neural ODEs, our models can efficiently run on central processing units (CPUs) instead of more energy-intensive graphics processing units (GPUs). This reduces energy consumption and CO2e by up to 50%, contributing to more sustainable and cost-effective AI training processes amidst rising computing demands.
Spearheading emissions data integration for greater GHG reductions
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We integrate real-time and forecasted emissions signals via a cloud-to-cloud API. By leveraging these data-driven insights, our unique AER algorithm automatically adjusts consumption patterns based on the carbon intensity of the grid, prioritizing cleaner energy sources for greater emissions reductions.
Together with our solution, our AER algorithm is proven to reduce HVAC emissions by up to 15% during curtailment events and for the following four hours. This is done by timing energy consumption with periods of lower emissions, aligning energy usage with times when renewable energy is more abundant and fossil fuel generation is lower.
We use a robust, randomized, switchback design inspired by medical randomized controlled trials to ensure accurate and reliable energy savings. This involves alternating between different operational strategies (such as baseline operation, energy optimization, and load shifting) in a controlled and randomized way.
By meticulously measuring and comparing the performance of these strategies, we can reliably estimate the impact of new control strategies in a building. This approach eliminates carryover effects and ensures results aren't influenced by external variables, leading to more precise and actionable insights.
"AI is already contributing to a range of efforts that can help us tackle some of the most urgent problems of today, climate change among them. We have systems that allow us to use energy more efficiently, whether that’s by controlling the temperature in buildings, managing the grid or predicting both the demand and availability of energy."
