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20242026
most citedAnalyzing Uncertainty Quantification in Statistical and Deep Learning Models for Probabilistic Electricity Price Forecasting

6 citations · 8 across the 4 of their papers we have counts for

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cs.LG2025

Bayesian Neural Networks with Monte Carlo Dropout for Probabilistic Electricity Price Forecasting

Abhinav Das, Stephan Schlüter

Accurate electricity price forecasting is critical for strategic decision-making in deregulated electricity markets, where volatility stems from complex supply-demand dynamics and…

cs.LG2025★ 6 cited

Analyzing Uncertainty Quantification in Statistical and Deep Learning Models for Probabilistic Electricity Price Forecasting

Andreas Lebedev, Abhinav Das, Sven Pappert +1

Precise probabilistic forecasts are fundamental for energy risk management, and there is a wide range of both statistical and machine learning models for this purpose. Inherent to…

cs.LG2025★ 1 cited

Regime-Aware Conditional Neural Processes with Multi-Criteria Decision Support for Operational Electricity Price Forecasting

Abhinav Das, Stephan Schlüter

This work integrates Bayesian regime detection with conditional neural processes for 24-hour electricity price prediction in the German market. Our methodology integrates regime de…