Combination of moment‐matching, Cholesky and clustering methods to approximate discrete probability distribution of multiple wind farms

Li, Jinghua; Zhu, Dunlin

doi:10.1049/iet-rpg.2015.0568

Cited by 13 publications

(1 citation statement)

References 39 publications

(58 reference statements)

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“…The output indicates that better approximation of returns is provided in less computation time. The combination of moment-matching with Cholesky decomposition and clustering is proposed by Li and Zhu (2016) and it is indicated to reflect the information of the original discrete probability distribution of the original scenarios. Finally, decomposition techniques can be applied to MMP improving its computational performance.…”

Section: Simmentioning

confidence: 99%

Data-Driven Scenario Generation for Two-Stage Stochastic Programming

Bounitsis

Papageorgiou

Charitopoulos

2022

Computer Aided Chemical Engineering

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Section: Simmentioning

confidence: 99%

Data-Driven Scenario Generation for Two-Stage Stochastic Programming

Bounitsis

Papageorgiou

Charitopoulos

2022

Computer Aided Chemical Engineering

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Variational Autoencoders and Generative Adversarial Networks for Multivariate Scenario Generation

Carbonera,

Ciavotta,

Messina

2024

Data Sci. Transp.

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When making decisions with lasting implications over a medium to long timeframe, it is essential to consider not only the most probable scenario, possibly obtained through a forecasting model, but also a range of potential outcomes. This approach allows for effective risk mitigation across a spectrum of scenarios, including less probable ones, and enhances the resilience of planning strategies. In this paper, we demonstrate the development of a generative model capable of learning the multivariate joint probability distribution of link speeds on a road network, using real sensor data. To further enhance the performance of our Generative Adversarial Network model, we employed a Variational AutoEncoder for pre-training the generator network. Experimental results, conducted on three distinct benchmark datasets, highlight the potential of the proposed model in generating new scenario samples of multivariate variables. The Wasserstein distance between the generated distribution and the real data, confirms the good performance of our model compared to state-of-the-art models, based on copulae. The proposed model has shown its ability to generate scenarios that preserve correlations among variables, while producing samples that faithfully represent the empirical marginal distributions.

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