NBMLSS: probabilistic forecasting of electricity prices via Neural Basis Models for Location Scale and Shape

Forecasters using flexible neural networks (NN) in multi-horizon distributional regression setups often struggle to gain detailed insights into the underlying mechanisms that lead to the predicted feature-conditioned distribution parameters. In this work, we deploy a Neural Basis Model for Location, Scale and Shape, that blends the principled interpretability of GAMLSS with a computationally scalable shared basis decomposition, combined by linear projections supporting dedicated stepwise and parameter-wise feature shape functions aggregations. Experiments have been conducted on multiple market regions, achieving probabilistic forecasting performance comparable to that of distributional neural networks, while providing more insights into the model behavior through the learned nonlinear feature level maps to the distribution parameters across the prediction steps. Introduction Probabilistic forecasting of hourly electricity prices in day-ahead power markets (PEPF) is a complex problem with a significant impact. These enable informed decision-making in high-stakes scenarios such as trading strategies, resource scheduling, and optimal commitment by factoring in potential fluctuations and associated risks [2]. Moreover, electricity prices are characterized by high volatility and rapid changes driven by intricate factors, including distributed power demand, generation costs, and weather conditions [3].