From point forecasts to multivariate probabilistic forecasts: The Schaake shuffle for day-ahead electricity price forecasting

Grothe, Oliver; Kächele, Fabian; Krüger, Fabian

doi:10.1016/j.eneco.2023.106602

Cited by 15 publications

(2 citation statements)

References 43 publications

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“…Where the symbol '+' denotes a pseudo-inverse operation. Replacing (7) in ( 6), we have a new criterion, which is only dependent on the nonlinear parameters, v:…”

Section: Parameter Separabilitymentioning

confidence: 99%

“…The parameter λ is a regularization parameter, which enables the search direction to change between the steepest descent and the Gauss-Newton directions. The gradient and Jacobean of criterion ( 8) can be obtained by: a) first computing (7); b) replacing the optimal values in the linear parameters; c) and determining the gradient and Jacobian in the usual way.…”

Section: Training Algorithmmentioning

confidence: 99%

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A Multi-Step Ensemble Approach for Energy Community Day-ahead Net Load Point and Probabilistic Forecasting

Ruano,

Ruano

2024

Preprint

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The incorporation of renewable energy systems in the world energy system has been steadily increasing during the last years. In terms of the building sector, the usual consumers are becoming increasingly prosumers, and the trend is that communities of energy, whose households share produced electricity, will increase in number in the future. Another observed tendency is that the aggregator (the entity that manages the community) trades the net community energy in public energy markets. To accomplish economically good transactions, accurate and reliable forecasts of the day-ahead net energy community must be available. These can be obtained using an ensemble of multi-step shallow artificial neural networks, with prediction intervals obtained by the covariance algorithm. Using real data obtained by a small energy community of four houses located in the South region of Portugal, one can verify that the deterministic and probabilistic performance of the proposed approach is at least similar, typically better than using complex, deep models.

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“…Where the symbol '+' denotes a pseudo-inverse operation. Replacing (7) in ( 6), we have a new criterion, which is only dependent on the nonlinear parameters, v:…”

Section: Parameter Separabilitymentioning

confidence: 99%

Section: Training Algorithmmentioning

confidence: 99%