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Found 10 papers, 2 papers with code
Global Estimation of Building-Integrated Facade and Rooftop Photovoltaic Potential by Integrating 3D Building Footprint and Spatio-Temporal Datasets
This research tackles the challenges of estimating Building-Integrated Photovoltaics (BIPV) potential across various temporal and spatial scales, accounting for different geographical climates and urban morphology.
AutoPV: Automatically Design Your Photovoltaic Power Forecasting Model
We develop a brand new NAS search space that incorporates various data processing techniques from state-of-the-art (SOTA) TSF models and typical PVPF deep learning models.
GLOBUS: Global building renovation potential by 2070
Surpassing the two large emission sectors of transportation and industry, the building sector accounted for 34% and 37% of global energy consumption and carbon emissions in 2021, respectively.
Estimation of Global Building Stocks by 2070: Unlocking Renovation Potential
Buildings produce one-third of carbon emissions globally, however, data absence regarding global floorspace poses challenges in advancing building carbon neutrality.
A Phone-based Distributed Ambient Temperature Measurement System with An Efficient Label-free Automated Training Strategy
In this study, we propose a distributed phone-based ambient temperature estimation system which enables collaboration among multiple phones to accurately measure the ambient temperature in different areas of an indoor space.
Survey on Large Language Model-Enhanced Reinforcement Learning: Concept, Taxonomy, and Methods
For each role, we summarize the methodologies, analyze the specific RL challenges that are mitigated, and provide insights into future directions.
Multi-spatial Multi-temporal Air Quality Forecasting with Integrated Monitoring and Reanalysis Data
To address these limitations, we present a novel Multi-spatial Multi-temporal air quality forecasting method based on Graph Convolutional Networks and Gated Recurrent Units (M2G2), bridging the gap in air quality forecasting across spatial and temporal scales.
Enhancing Building Semantic Segmentation Accuracy with Super Resolution and Deep Learning: Investigating the Impact of Spatial Resolution on Various Datasets
The development of remote sensing and deep learning techniques has enabled building semantic segmentation with high accuracy and efficiency.
Real-World Video for Zoom Enhancement based on Spatio-Temporal Coupling
In recent years, single-frame image super-resolution (SR) has become more realistic by considering the zooming effect and using real-world short- and long-focus image pairs.
Theory-guided hard constraint projection (HCP): a knowledge-based data-driven scientific machine learning method
Machine learning models have been successfully used in many scientific and engineering fields.
