John Sipple

Sang woo Ham, Donghun Kim, Michael Rossetti et al.

Accurate building energy simulation is essential for developing advanced control strategies that enable demand flexibility and grid responsiveness. The Smart Buildings Control Suite (sbsim) offers a lightweight, scalable, and data-calibrated simulation environment based on a tensorized finite difference model. Previous work extended sbsim to include interior long-wave radiative heat exchange between indoor surfaces. However, a complete thermal model must also account for exterior radiative processes, including long-wave radiation exchange with the sky and surroundings, as well as short-wave solar radiation incident on building surfaces. This paper presents a comprehensive radiative heat transfer implementation for sbsim that integrates both interior and exterior radiation mechanisms. Our primary contribution is the development and integration of a fully tensorized exterior radiation module that captures sky and ground long-wave exchange as well as solar heat gains through opaque and transparent surfaces. By formulating these processes as tensor operations compatible with the existing framework, we preserve the computational efficiency necessary for reinforcement learning applications. We validate our implementation against established simulation tools and demonstrate improved prediction accuracy for surface temperatures and building thermal loads. This enhancement significantly increases the physical fidelity of sbsim, enabling more realistic training environments for building energy optimization and control.

Judah Goldfeder, John Sipple

Modern commercial Heating, Ventilation, and Air Conditioning (HVAC) devices form a complex and interconnected thermodynamic system with the building and outside weather conditions, and current setpoint control policies are not fully optimized for minimizing energy use and carbon emission. Given a suitable training environment, a Reinforcement Learning (RL) model is able to improve upon these policies, but training such a model, especially in a way that scales to thousands of buildings, presents many real world challenges. We propose a novel simulation-based approach, where a customized simulator is used to train the agent for each building. Our open-source simulator (available online: https://github.com/google/sbsim) is lightweight and calibrated via telemetry from the building to reach a higher level of fidelity. On a two-story, 68,000 square foot building, with 127 devices, we were able to calibrate our simulator to have just over half a degree of drift from the real world over a six-hour interval. This approach is an important step toward having a real-world RL control system that can be scaled to many buildings, allowing for greater efficiency and resulting in reduced energy consumption and carbon emissions.

John Sipple, Abdou Youssef

The need for explainable AI (XAI) is well established but relatively little has been published outside of the supervised learning paradigm. This paper focuses on a principled approach to applying explainability and interpretability to the task of unsupervised anomaly detection. We argue that explainability is principally an algorithmic task and interpretability is principally a cognitive task, and draw on insights from the cognitive sciences to propose a general-purpose method for practical diagnosis using explained anomalies. We define Attribution Error, and demonstrate, using real-world labeled datasets, that our method based on Integrated Gradients (IG) yields significantly lower attribution errors than alternative methods.

John Sipple

Complex devices are connected daily and eagerly generate vast streams of multidimensional state measurements. These devices often operate in distinct modes based on external conditions (day/night, occupied/vacant, etc.), and to prevent complete or partial system outage, we would like to recognize as early as possible when these devices begin to operate outside the normal modes. Unfortunately, it is often impractical or impossible to predict failures using rules or supervised machine learning, because failure modes are too complex, devices are too new to adequately characterize in a specific environment, or environmental change puts the device into an unpredictable condition. We propose an unsupervised anomaly detection method that creates a negative sample from the positive, observed sample, and trains a classifier to distinguish between positive and negative samples. Using the Contraction Principle, we explain why such a classifier ought to establish suitable decision boundaries between normal and anomalous regions, and show how Integrated Gradients can attribute the anomaly to specific variables within the anomalous state vector. We have demonstrated that negative sampling with random forest or neural network classifiers yield significantly higher AUC scores than Isolation Forest, One Class SVM, and Deep SVDD, against (a) a synthetic dataset with dimensionality ranging between 2 and 128, with 1, 2, and 3 modes, and with and without noise dimensions; (b) four standard benchmark datasets; and (c) a multidimensional, multimodal dataset from real climate control devices. Finally, we describe how negative sampling with neural network classifiers have been successfully deployed at large scale to predict failures in real time in over 15,000 climate-control and power meter devices in 145 Google office buildings.