Deep Learning-Based Multivariate Probabilistic Forecasting for Short-Term Scheduling in Power Markets

By Jean-François Toubeau, Jérémie Bottieau, François Vallée, Z. De Grève. 2019

204 citations recorded by the source corpus.

In the current competition framework governing the electricity sector, complex dependencies exist between electrical and market data, which complicates the decision-making procedure of energy actors. These must indeed operate within a complex, uncertain environment, and consequently need to rely on accurate multivariate, multi-step ahead probabilistic predictions. This paper aims to take advantage of recent breakthroughs in deep learning, while exploiting the structure of the problem to design prediction tools with tailored architectural alterations that improve their performance. The method can provide prediction intervals and densities, but is here extended with the objective to generate predictive scenarios. It is achieved by sampling the predicted multivariate distribution with a copula-based strategy so as to embody both temporal information and cross-variable dependencies. The effectiveness of the proposed methodology is emphasized and compared with several other architectures in terms of both statistical performance and impact on the quality of decisions optimized within a dedicated stochastic optimization tool of an electricity retailer participating in short-term electricity markets.

Subfields: Electrical and Electronic Engineering, Safety, Risk, Reliability and Quality, Mathematical Optimization, Time-Series & Forecasting

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Citations in the corpus

N-BEATS: Neural basis expansion analysis for interpretable time series forecasting (2019, background): “Somewhat independently, the work in the modern deep learning TS forecasting developed based on variations of recurrent neural networks [8, 27, 29, 32] being largely dominated by the electricity load forecasting in the multivariate setup.”
High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processes (2019, background): “The work most closely related to ours is the recent work [32], which also proposes to combine deep autoregressive models with copula to model correlated time series.”
Deep Generative Quantile-Copula Models for Probabilistic Forecasting (2019, methodology): “Upon writing this paper, we found another similar work by Toubeau et al, 2019: they used an LSTM-based forecasting model to predict quantiles, and then a separately estimated and stored empirical copula table on a quantile grid/cube to query scenarios.”