In this paper, we propose a new short-term load forecasting (STLF) model based on contextually enhanced hybrid and hierarchical architecture combining exponential smoothing (ES) and a recurrent neural network (RNN). The model is composed of two simultaneously trained tracks: the context track and the main track. The context track introduces additional information to the main track. It is extracted from representative series and dynamically modulated to adjust to the individual series forecasted by the main track. The RNN architecture consists of multiple recurrent layers stacked with hierarchical dilations and equipped with recently proposed attentive dilated recurrent cells. These cells enable the model to capture short-term, long-term and seasonal dependencies across time series as well as to weight dynamically the input information. The model produces both point forecasts and predictive intervals. The experimental part of the work performed on 35 forecasting problems shows that the proposed model outperforms in terms of accuracy its predecessor as well as standard statistical models and state-of-the-art machine learning models.
翻译:在本文中,我们提出一个新的短期负载预测模型,该模型基于环境强化的混合和等级结构,结合指数平滑和经常神经网络(RNN),由两个同时培训的轨道组成:上下文轨道和主轨。上下文轨道向主轨提供补充信息,从具有代表性的系列中提取,动态调整,以适应主轨预测的单个序列。RNN结构由多个重复式层组成,叠叠叠着等级变相,并配有最近提议的细微扩展的经常性细胞。这些细胞使模型能够捕捉到不同时间序列的短期、长期和季节性依赖性,并动态地加权输入信息。该模型产生点预报和预测间隔。在35个预测问题上开展的工作的实验部分表明,拟议的模型在准确性方面超过了其前身,以及标准统计模型和最先进的机器学习模型。