mshw, a forecasting library to predict short-term electricity demand based on multiple seasonal Holt-Winters
mshw：基于多重季节 Holt–Winters 模型的短期电力需求预测库AI 解读 ↓
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mshw：基于多重季节 Holt–Winters 模型的短期电力需求预测库
- 研究问题
- 如何以计算结构较简单的模型预测短期电力需求，并处理多重季节性及特殊日期带来的变化，是摘要关注的核心问题。
- 方法与思路
- 作者开发 MATLAB 工具箱，实现多重季节 Holt–Winters 指数平滑及神经网络模型，并引入离散区间移动季节性处理特殊日期。
- 材料陈述的贡献
- 摘要称工具箱已应用于欧洲多个电力系统，并提出将离散、复杂季节性模型推广到其他预测场景的研究方向。
- 阅读关注点
- 应核对特殊日期与移动季节性的定义、模型调参与预测窗口，以及欧洲各系统的评价指标、比较基准和计算成本。
- 阅读边界
- 这是 arXiv 预印本，同行评审及接收未核实；摘要未给出误差或运行时间，不能据此确认其准确率与计算优势。
原文摘要
Transmission system operators have a growing need for more accurate forecasting of electricity demand. Current electricity systems largely require demand forecasting so that the electricity market establishes electricity prices as well as the programming of production units. The companies that are part of the electrical system use exclusive software to obtain predictions, based on the use of time series and prediction tools, whether statistical or artificial intelligence. However, the most common form of prediction is based on hybrid models that use both technologies. In any case, it is software with a complicated structure, with a large number of associated variables and that requires a high computational load to make predictions. The predictions they can offer are not much better than those that simple models can offer. In this paper we present a MATLAB toolbox created for the prediction of electrical demand. The toolbox implements multiple seasonal Holt-Winters exponential smoothing models and neural network models. The models used include the use of discrete interval mobile seasonalities (DIMS) to improve forecasting on special days. Additionally, the results of its application in various electrical systems in Europe are shown, where the results obtained can be seen. The use of this library opens a new avenue of research for the use of models with discrete and complex seasonalities in other fields of application.