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Found 7 papers, 2 papers with code
Autonomous Advanced Aerial Mobility -- An End-to-end Autonomy Framework for UAVs and Beyond
The perspective aims to provide a holistic picture of the autonomous advanced aerial mobility field and its future directions.
Decentralization of Energy Systems with Blockchain: Bridging Top-down and Bottom-up Management of the Electricity Grid
In this work, we aim to highlight the need for and outline a credible path toward restructuring the current operational architecture of the electricity grid in view of the ongoing decentralization trends with an emphasis on peer-to-peer energy trading.
Scalable Modular Synthetic Data Generation for Advancing Aerial Autonomy
One major barrier to advancing aerial autonomy has been collecting large-scale aerial datasets for training machine learning models.
Microgrid Resilience: A Holistic and Context-Aware Resilience Metric
Microgrids present an effective solution for the coordinated deployment of various distributed energy resources and furthermore provide myriad additional benefits such as resilience, decreased carbon footprint, and reliability to energy consumers and the energy system as a whole.
Deep-Learning-Based, Multi-Timescale Load Forecasting in Buildings: Opportunities and Challenges from Research to Deployment
Electricity load forecasting for buildings and campuses is becoming increasingly important as the penetration of distributed energy resources (DERs) grows.
An Integrated Multi-Time-Scale Modeling for Solar Irradiance Forecasting Using Deep Learning
In this research work, we propose a unified architecture for multi-time-scale predictions for intra-day solar irradiance forecasting using recurrent neural networks (RNN) and long-short-term memory networks (LSTMs).
Multi-time-horizon Solar Forecasting Using Recurrent Neural Network
The results demonstrate that the proposed method based on the unified architecture is effective for multi-horizon solar forecasting and achieves a lower root-mean-squared prediction error compared to the previous best-performing methods which use one model for each time-horizon.
