发表机构
Euskal Herriko Unibertsitatea (EHU)（巴斯克地区大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文应用GAMLSS框架，基于2020-2024年西班牙小时数据预测日前电价，发现灵活分布规格（尤其是JSU）优于基准和正态模型，显著提升概率预测性能，强调了时变形状参数的重要性。
AI 中文摘要
可再生能源并网、市场冲击和监管变化导致的电价波动性日益加剧，这强化了对超越点预测并能准确描述完整条件价格分布的预测方法的需求。本文应用广义可加模型（GAMLSS）框架，利用2020年至2024年的小时数据预测西班牙日前电价。考虑了基于正态分布、Johnson's SU（JSU）和Sinh-Arcsinh（SHASH）分布的替代规格，允许位置、尺度和形状参数随市场基本面变化，包括电力需求、可再生能源发电、季节性效应以及监管和地缘政治风险因素。预测采用滚动窗口方法生成，并通过平均绝对误差（MAE）、分位数损失（pinball loss）和Diebold-Mariano检验进行评估。结果表明，灵活的分布规格相对于朴素基准和标准正态规格提高了预测性能。虽然SHASH和JSU规格提供了最低的点预测误差，但小时分析揭示了不同规格在相对性能上的显著日内变化。所有四个参数均由协变量驱动的JSU规格实现了最佳的概率预测性能，尤其是在分布的尾部。Diebold-Mariano检验证实了这些改进的统计显著性。这些发现强调了建模时变形状分布参数的重要性，并展示了GAMLSS模型在日益波动的电力市场中进行预测和风险管理的价值。
英文摘要
The increasing volatility of electricity prices driven by renewable energy integration, market shocks, and regulatory changes has reinforced the need for forecasting methods that go beyond point predictions and accurately describe the full conditional price distribution. This paper applies the Generalised Additive Models for Location, Scale and Shape (GAMLSS) framework to forecast Spanish day-ahead electricity prices using hourly data from 2020 to 2024. Alternative specifications based on Normal, Johnson's SU (JSU), and Sinh-Arcsinh (SHASH) distributions are considered, allowing the location, scale, and shape parameters to vary with market fundamentals, including electricity demand, renewable generation, seasonal effects, and regulatory and geopolitical risk factors. Forecasts are generated using a rolling-window approach and evaluated through the mean absolute error (MAE), pinball loss, and Diebold-Mariano tests. The results show that flexible distributional specifications improve forecasting performance relative to a naive benchmark and the standard normal specification. While SHASH and JSU specifications provide the lowest point forecasting errors, the hourly analysis reveals substantial intraday variation in relative performance across specifications. JSU specification with all four parameters driven by covariates achieves the best probabilistic forecasting performance, particularly in the tails of the distribution. Diebold-Mariano tests confirm the statistical significance of these improvements. These findings highlight the importance of modelling time-varying shape distributional parameters and demonstrate the value of GAMLSS models for forecasting and risk management in increasingly volatile electricity markets.
Comments25 pages, 2 figures, under review
