ES-dRNN with Dynamic Attention for Short-Term Load Forecasting

• URL: http://arxiv.org/abs/2203.00937v1
• Date: Wed, 2 Mar 2022 08:39:33 GMT
• Title: ES-dRNN with Dynamic Attention for Short-Term Load Forecasting
• Authors: Slawek Smyl, Grzegorz Dudek, Pawe{\l} Pe{\l}ka
• Abstract summary: Short-term load forecasting (STLF) is a challenging problem due to the complex nature of the time series expressing multiple seasonality and varying variance. This paper proposes an extension of a hybrid forecasting model combining exponential smoothing and dilated recurrent neural network (ES-dRNN) with a mechanism for dynamic attention.
• Score: 1.1602089225841632
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Short-term load forecasting (STLF) is a challenging problem due to the complex nature of the time series expressing multiple seasonality and varying variance. This paper proposes an extension of a hybrid forecasting model combining exponential smoothing and dilated recurrent neural network (ES-dRNN) with a mechanism for dynamic attention. We propose a new gated recurrent cell -- attentive dilated recurrent cell, which implements an attention mechanism for dynamic weighting of input vector components. The most relevant components are assigned greater weights, which are subsequently dynamically fine-tuned. This attention mechanism helps the model to select input information and, along with other mechanisms implemented in ES-dRNN, such as adaptive time series processing, cross-learning, and multiple dilation, leads to a significant improvement in accuracy when compared to well-established statistical and state-of-the-art machine learning forecasting models. This was confirmed in the extensive experimental study concerning STLF for 35 European countries.

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