Covariate-informed zero-shot time-series foundation models beat task-specifically tuned XGBoost and random forests in aggregate on a 54-dataset energy forecasting benchmark.
Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms
5 Pith papers cite this work, alongside 608 external citations. Polarity classification is still indexing.
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For Spanish day-ahead electricity prices, a Johnson's SU model with all four distribution parameters driven by market fundamentals gives the most accurate forecasts of extreme price quantiles.
DNNs plus SHAP/SSHAP applied to 39 European bidding zones identify solar and gas as key price drivers and simulate a single-price EU market.
FairMarket-RL injects LLM-computed fairness scores into PPO rewards and reports 30-90 day P2P market simulations with roughly 54% local trade share, 27-30% consumer bill savings, and a profitable grid.
SHAP and gradient explanations of five day-ahead electricity price forecasting DNNs reveal that the most recent price dominates forecasts, and new SSHAP aggregations help visualize these patterns.
citing papers explorer
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FETS Benchmark: Foundation Models Enable Scalable and Generalizable Energy Time Series Forecasting
Covariate-informed zero-shot time-series foundation models beat task-specifically tuned XGBoost and random forests in aggregate on a 54-dataset energy forecasting benchmark.
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A distributional modelling approach with application to electricity price forecasting
For Spanish day-ahead electricity prices, a Johnson's SU model with all four distribution parameters driven by market fundamentals gives the most accurate forecasts of extreme price quantiles.
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Analysing drivers and interdependencies in European electricity markets using XAI
DNNs plus SHAP/SSHAP applied to 39 European bidding zones identify solar and gas as key price drivers and simulate a single-price EU market.
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Scalable Fairness Shaping with LLM-Guided Multi-Agent Reinforcement Learning for Peer-to-Peer Electricity Markets
FairMarket-RL injects LLM-computed fairness scores into PPO rewards and reports 30-90 day P2P market simulations with roughly 54% local trade share, 27-30% consumer bill savings, and a profitable grid.
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Explaining deep neural network models for electricity price forecasting with XAI
SHAP and gradient explanations of five day-ahead electricity price forecasting DNNs reveal that the most recent price dominates forecasts, and new SSHAP aggregations help visualize these patterns.