Distributional neural networks for electricity price forecasting

• URL: http://arxiv.org/abs/2207.02832v1
• Date: Wed, 6 Jul 2022 17:42:52 GMT
• Title: Distributional neural networks for electricity price forecasting
• Authors: Grzegorz Marcjasz, Micha{\l} Narajewski, Rafa{\l} Weron and Florian Ziel
• Abstract summary: We present a novel approach to probabilistic electricity price forecasting (EPF) The novel network structure for EPF is based on a regularized distributional multilayer perceptron (DMLP) Using the framework, the neural network's output is defined to be a distribution, either normal or potentially skewed and heavy-tailed Johnson's SU (JSU)
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a novel approach to probabilistic electricity price forecasting (EPF) which utilizes distributional artificial neural networks. The novel network structure for EPF is based on a regularized distributional multilayer perceptron (DMLP) which contains a probability layer. Using the TensorFlow Probability framework, the neural network's output is defined to be a distribution, either normal or potentially skewed and heavy-tailed Johnson's SU (JSU). The method is compared against state-of-the-art benchmarks in a forecasting study. The study comprises forecasting involving day-ahead electricity prices in the German market. The results show evidence of the importance of higher moments when modeling electricity prices.

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