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.
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Integrating the Expected Future in Load Forecasts with Contextually Enhanced Transformer Models
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.