# Planning Strategies in the Energy Sector: Integrating Bayesian Neural Networks and Uncertainty Quantification in Scenario Analysis & Optimization

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/planning-strategies-in-the-energy-sector-integrating-bayesian-neural-networks/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Funda Iseri,Halil Iseri,Harsh Shah,Eleftherios Iakovou,Efstratios N. Pistikopoulos |
| Citations | 0 |
| DOI | 10.2139/ssrn.5088471 |
| Fields | Engineering |
| Open Access | true |
| OA Status | green |
| OA URL | https://doi.org/10.2139/ssrn.5088471 |
| OpenAlex ID | https://openalex.org/W4406161187 |
| Type | preprint |
| Year | 2025 |

## Paper authors

- [Halil Iseri](https://scholariq.org/researchers/halil-iseri/)

## Paper journal

- [SSRN Electronic Journal](https://scholariq.org/journals/ssrn-electronic-journal/)

## Paper primary topic

- [Reservoir Engineering and Simulation Methods](https://scholariq.org/topics/reservoir-engineering-and-simulation-methods/)

## Paper topics

- [Reservoir Engineering and Simulation Methods](https://scholariq.org/topics/reservoir-engineering-and-simulation-methods/)
- [Energy Load and Power Forecasting](https://scholariq.org/topics/energy-load-and-power-forecasting/)
- [Electric Power System Optimization](https://scholariq.org/topics/electric-power-system-optimization/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.