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Found 14 papers, 3 papers with code
Semantic segmentation of longitudinal thermal images for identification of hot and cool spots in urban areas
The masks generated using the segmentation models were then used to extract the temperature from thermal images and correct for differences in the emissivity of various urban features.
District-scale surface temperatures generated from high-resolution longitudinal thermal infrared images
The rooftop infrared thermography observatory with a multi-modal platform that is capable of assessing a wide range of dynamic processes in urban systems was deployed in Singapore.
Mobile Energy Storage in Power Network: Marginal Value and Optimal Operation
This paper examines the marginal value of mobile energy storage, i. e., energy storage units that can be efficiently relocated to other locations in the power network.
Longitudinal thermal imaging for scalable non-residential HVAC and occupant behaviour characterization
It is observed that for the water-cooled system, the difference between the rate of change of the window and wall can be used to extract the operational pattern.
Electric Vehicle Battery Sharing Game for Mobile Energy Storage Provision in Power Networks
In this paper, we consider two types of EV drivers who may be willing to provide mobile storage service using their EVs: commuters taking a fixed route, and on-demand EV drivers who receive incentives from a transportation network company (TNC) and are willing to take any route.
Asset Participation and Aggregation in Incentive-Based Demand Response Programs
We develop a probabilistic model for the curtailment capability of these assets, and use it to derive analytic expressions for the optimal participation (i. e., promised curtailment) and profitability from the DR asset perspective.
Are Ensemble Classifiers Powerful Enough for the Detection and Diagnosis of Intermediate-Severity Faults?
Intermediate-Severity (IS) faults present milder symptoms compared to severe faults, and are more difficult to detect and diagnose due to their close resemblance to normal operating conditions.
Impact of Congestion Charge and Minimum Wage on TNCs: A Case Study for San Francisco
Both charges reduce the number of TNC trips, but this reduction is limited by the wage floor, and the number of TNC vehicles reduced is not significant.
Learning Robustness with Bounded Failure: An Iterative MPC Approach
We propose an approach to design a Model Predictive Controller (MPC) for constrained Linear Time Invariant systems performing an iterative task.
EnergyStar++: Towards more accurate and explanatory building energy benchmarking
Even more importantly, a set of techniques is developed to help determine which factors most influence the score using SHAP values.
A Queuing Approach to Parking: Modeling, Verification, and Prediction
Based on the queuing model, we propose two prediction methods, a microscopic method and a macroscopic method, that provide a real-time probabilistic forecast of parking occupancy for an arbitrary forecast horizon.
Detecting and Diagnosing Incipient Building Faults Using Uncertainty Information from Deep Neural Networks
Early detection of incipient faults is of vital importance to reducing maintenance costs, saving energy, and enhancing occupant comfort in buildings.
A One-Class Support Vector Machine Calibration Method for Time Series Change Point Detection
The One-Class Support Vector Machine (OC-SVM) is a popular machine learning model for anomaly detection and hence could be used for identifying change points; however, it is sometimes difficult to obtain a good OC-SVM model that can be used on sensor measurement time series to identify the change points in system health status.
Low-dimensional Models in Spatio-Temporal Wind Speed Forecasting
Integrating wind power into the grid is challenging because of its random nature.
