In this work, we investigate a probabilistic method for electricity price forecasting, which overcomes traditional ones. We start considering statistical methods for point forecast, comparing their performance in terms of efficiency, accuracy, and reliability, and we then exploit Neural Networks approaches to derive a hybrid model for probabilistic type forecasting. We show that our solution reaches the highest standard both in terms of efficiency and precision by testing its output on German electricity prices data.

Energy Markets Forecasting. From Inferential Statistics to Machine Learning: The German Case

Luca Di Persio
;
2021-01-01

Abstract

In this work, we investigate a probabilistic method for electricity price forecasting, which overcomes traditional ones. We start considering statistical methods for point forecast, comparing their performance in terms of efficiency, accuracy, and reliability, and we then exploit Neural Networks approaches to derive a hybrid model for probabilistic type forecasting. We show that our solution reaches the highest standard both in terms of efficiency and precision by testing its output on German electricity prices data.
electricity price
statistical method
autoregressive
probabilistic forecast
neural network
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/1038513
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