An empirical study evaluating tool-augmented LLM agents on 243 real-world energy analytics problems across data retrieval, knowledge interpretation, and quantitative modeling using domain-specific tools and multi-dimensional scoring.
Electricity price forecasting: A review of the state-of-the-art with a look into the future
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A Group Lasso multivariate model improves day-ahead electricity price forecasts on CAISO data by modeling temporal group effects and ranks second in an international challenge with limited inputs.
Foundation models outperform dataset-specific machine learning in energy time series forecasting across 54 datasets in 9 categories.
NSTM maps European bidding zones into a network via metric graph and neighborhood measure, outperforming independent local models in day-ahead price forecasting across 39 zones.
Matrix-HAR model with multi-horizon lags and renewable generation inputs improves one-week forecasts of realized covariation and spread risk premia versus standard backward-looking volatility methods in electricity markets.
Contextually-enhanced transformers integrating timetable and occupancy data achieve 26.6% and 56.3% average MAE reductions in railway and building energy forecasting respectively, outperforming prior methods.
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.
LightGBM outperforms other models for electricity price forecasting across Norway's bidding zones, with lagged prices and calendar features often sufficient but external features key in stressed regimes.
citing papers explorer
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How Do Tool-Augmented LLM Agents Perform on Real-World Energy Analytics Tasks?
An empirical study evaluating tool-augmented LLM agents on 243 real-world energy analytics problems across data retrieval, knowledge interpretation, and quantitative modeling using domain-specific tools and multi-dimensional scoring.
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Day-Ahead Electricity Price Forecasting Using a Multivariate Group Lasso Method
A Group Lasso multivariate model improves day-ahead electricity price forecasts on CAISO data by modeling temporal group effects and ranks second in an international challenge with limited inputs.
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FETS Benchmark: Foundation Models Outperform Dataset-specific Machine Learning in Energy Time Series Forecasting
Foundation models outperform dataset-specific machine learning in energy time series forecasting across 54 datasets in 9 categories.
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Networked Spatial Effects in European Electricity Price Forecasting
NSTM maps European bidding zones into a network via metric graph and neighborhood measure, outperforming independent local models in day-ahead price forecasting across 39 zones.
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Forecasting of volatility and risk premia in electricity markets
Matrix-HAR model with multi-horizon lags and renewable generation inputs improves one-week forecasts of realized covariation and spread risk premia versus standard backward-looking volatility methods in electricity markets.
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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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Electricity price forecasting across Norway's five bidding zones in the post-crisis era
LightGBM outperforms other models for electricity price forecasting across Norway's bidding zones, with lagged prices and calendar features often sufficient but external features key in stressed regimes.