"Probabilistic Deep Learning for Energy Time Series Forecasting: A Comparative Study" explores how advanced probabilistic models like Bayesian Neural Networks, Deep Ensembles, and Concrete Dropout can significantly improve the reliability of day-ahead energy forecasts compared to traditional point estimates. Presented by Ramakrishna Garine , this study highlights the motivation, methodology, calibration challenges, model performance, and key findings, emphasizing the role of recalibration in enhancing prediction accuracy. The research sets the foundation for future work on better uncertainty estimation and faster training techniques for real-world energy applications.

1. Probabilistic Deep
Learning for Energy
Time
Series Forecasting: A
Comparative Study
Ramakrishna Garine, Sunil Pradhan Sharma
University of North Texas, Capital One

2. Motivation
PROBLEM: TRADITIONAL DL →
NO UNCERTAINTY ESTIMATE
CHALLENGE: RENEWABLE
ENERGY INTRODUCES MORE
VOLATILITY
NEED: FORECAST +
CONFIDENCE INTERVAL →
BETTER DECISIONS

3. Objectives & Contributions
Evaluate 5 probabilistic DL models
Compare short-term and day-ahead scenarios
Metrics: Calibration, Sharpness, Accuracy
Benchmark against standard models

4. Research Approach
Evaluation
Calibration
Sharpness (Width of
prediction intervals)
Accuracy Metrics
RMSE, MAPE, CRPS
Scenarios
Short-Term Forecasting Day-Ahead Forecasting
Probabilistic Deep Learning models
Concrete
Dropout
Deep Ensembles
Bayesian Neural
Networks
Deep Gaussian
Processes
Functional
Neural Processes

5. Probabilistic Models Compared
Model Approach Pros Cons
Deep
Ensembles
Avg multiple NNs Simple, robust Costly to train
Concrete
Dropout
Adaptive dropout Fast, lightweight Requires tuning
BNN Weight distributions Good uncertainty High computation
Deep GP Layered GPs Powerful Not scalable
FNP
Implicit function
modeling
Flexible Poor convergence

6. Data & Setup
Data Source: Germany Load Data
(ENTSO-E)
Resolution: 15-min intervals
Train/Test Split: 80/20
Short-term: Predict next step
Day-ahead: Predict 24 hours ahead
BN
N
Concrete
Sim
ple N
N
Q
uantile Regression
D
eep
Ensem
bles
D
eep
G
P
FN
P
0
6
Short-term load forecasting
RMSE MAPE [%] CRPS

7. Key Accomplishments
• All models benefited from recalibration,
improving probabilistic reliability.
• Demonstrated that probabilistic forecasts
outperform traditional point estimates for
energy time series
Achieved best overall accuracy
and sharpness in short-term
forecasts.
Deep Ensembles
Excelled in day-ahead
forecasting.
Bayesian Neural
Networks
Offered strong balance between
performance and simplicity.
Concrete
Dropout

8. Results – Day-Ahead
Forecasting
•Challenge: Less accurate across all models
•Top Models: BNN > Concrete > Deep
Ensemble
•Calibration harder: Under-dispersion seen
•Training times: Similar across tasks

9. Summary Of
Findings
Deep Ensembles: Best overall,
practical
Concrete Dropout: Fast, good
performance
BNN: High-quality uncertainty,
slow to train
Deep GP & FNP: Not production-
ready
Confidence > Accuracy alone

10. Future Work
Enhance Deep GP & FNP
training
Hybrid models (Bayesian +
Ensembles)
Attention/transformers for
forecasting
Explore real-time
deployment

11. Q&A • Let's Connect on LinkedIn
• Search for “Ramakrishna Garine “

12. Thank you !