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Found 8 papers, 6 papers with code
Generative Adversarial Network with Soft-Dynamic Time Warping and Parallel Reconstruction for Energy Time Series Anomaly Detection
In this paper, we employ a 1D deep convolutional generative adversarial network (DCGAN) for sequential anomaly detection in energy time series data.
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.
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.
LEAD1.0: A Large-scale Annotated Dataset for Energy Anomaly Detection in Commercial Buildings
Modern buildings are densely equipped with smart energy meters, which periodically generate a massive amount of time-series data yielding few million data points every day.
The ASHRAE Great Energy Predictor III competition: Overview and results
This launch marked the third energy prediction competition from ASHRAE and the first since the mid-1990s.
Computers and Society
The Building Data Genome Project 2: Hourly energy meter data from the ASHRAE Great Energy Predictor III competition
This paper describes an open data set of 3, 053 energy meters from 1, 636 non-residential buildings with a range of two full years (2016 and 2017) at an hourly frequency (17, 544 measurements per meter resulting in approximately 53. 6 million measurements).
Applications
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.
