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Found 8 papers, 3 papers with code
HUGO -- Highlighting Unseen Grid Options: Combining Deep Reinforcement Learning with a Heuristic Target Topology Approach
With the growth of Renewable Energy (RE) generation, the operation of power grids has become increasingly complex.
AudioProtoPNet: An interpretable deep learning model for bird sound classification
Recently, scientists have proposed several deep learning models to monitor the diversity of bird species.
BirdSet: A Multi-Task Benchmark for Classification in Computational Avian Bioacoustics
Data fragmentation and opacity across studies complicate a comprehensive evaluation of model performance.
Active Bird2Vec: Towards End-to-End Bird Sound Monitoring with Transformers
We propose a shift towards end-to-end learning in bird sound monitoring by combining self-supervised (SSL) and deep active learning (DAL).
Managing power grids through topology actions: A comparative study between advanced rule-based and reinforcement learning agents
In this article, we analyse the submitted agent from Binbinchen and provide novel strategies to improve the agent, both for the RL and the rule-based approach.
Targeted Adversarial Attacks on Wind Power Forecasts
In recent years, researchers proposed a variety of deep learning models for wind power forecasting.
A Reinforcement Learning Approach for the Continuous Electricity Market of Germany: Trading from the Perspective of a Wind Park Operator
Through their short trading horizon and continuous nature, the intraday markets offer the ability to adjust trading decisions from the day-ahead market or reduce trading risk in a short-term notice.
Vertical Power Flow Forecast with LSTMs Using Regular Training Update Strategies
The strong growth of renewable energy sources and the high volatility in power generation of these sources, as well as the increasing amount of volatile energy consumption is leading to major challenges in the electrical grid.
