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Notes on energy
6 notes
LLMs and Agentic AI Systems for Smart Grids: A Tutorial on Architectures and Applications
This paper discusses how large language models (LLMs) and agentic AI systems are being used in smart grids for tasks like forecasting, optimization, and control. The authors present a design principle called the…
Improving Wind and Solar Power Prediction with Efficient Wrapper-based Feature Selection: An Empirical Study
Researchers propose a new method for feature selection in renewable energy prediction pipelines, called Cluster-based Sequential Feature Selection (CSFS). This method aims to address the gap in limited or unsystematic…
A Multimodal Dataset for Large Language Model Applications in the Energy Domain
The paper introduces a new open-access dataset called mAIEnergy which is a multimodal corpus designed to support Large Language Model (LLM) applications in the energy sector. It contains various types of data including…
SolarChain-Eval: A Physics-Constrained Benchmark for Trustworthy Economic Agents in Decentralized Energy Markets
The paper proposes SolarChain-Eval, a benchmark to evaluate the trustworthiness of economic agents in decentralized energy markets using a physics-constrained Markov Decision Process. The benchmark evaluates agents'…
State Representation Matters in Deep Reinforcement Learning: Application to Energy Trading
Researchers studied the impact of state representation in deep reinforcement learning (DRL) for energy trading decisions, using a fixed Double DQN agent in the HydroDam environment. They found that combining absolute…
State Representation Matters in Deep Reinforcement Learning: Application to Energy Trading
Researchers compared different methods for representing market conditions in reinforcement learning (RL) models for energy trading. They found that combining multiple types of features led to stronger performance…
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