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Found 9 papers, 2 papers with code
Postprocessing of point predictions for probabilistic forecasting of electricity prices: Diversity matters
Operational decisions relying on predictive distributions of electricity prices can result in significantly higher profits compared to those based solely on point forecasts.
Combining predictive distributions of electricity prices: Does minimizing the CRPS lead to optimal decisions in day-ahead bidding?
At the same time, the higher computational cost of using CRPS learning compared to an equal-weighted aggregation of distributions is not offset by higher profits, despite significantly more accurate predictions.
Distributional neural networks for electricity price forecasting
We present a novel approach to probabilistic electricity price forecasting which utilizes distributional neural networks.
Forecasting Electricity Prices
Secondly, there is a clear shift from the relatively parsimonious econometric (or statistical) models towards more complex and harder to comprehend, but more versatile and eventually more accurate statistical/machine learning approaches.
Calibration window selection based on change-point detection for forecasting electricity prices
We employ a recently proposed change-point detection algorithm, the Narrowest-Over-Threshold (NOT) method, to select subperiods of past observations that are similar to the currently recorded values.
Electricity Price Forecasting: The Dawn of Machine Learning
Electricity price forecasting (EPF) is a branch of forecasting on the interface of electrical engineering, statistics, computer science, and finance, which focuses on predicting prices in wholesale electricity markets for a whole spectrum of horizons.
Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx
We extend the neural basis expansion analysis (NBEATS) to incorporate exogenous factors.
Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark
While the field of electricity price forecasting has benefited from plenty of contributions in the last two decades, it arguably lacks a rigorous approach to evaluating new predictive algorithms.
Neural networks in day-ahead electricity price forecasting: Single vs. multiple outputs
One of such factors is the network's structure.
