{"ID":424651,"CreatedAt":"2026-03-04T20:58:37Z","UpdatedAt":"2026-03-04T20:58:37Z","DeletedAt":null,"paper_url":"https://paperswithcode.com/paper/es-drnn-with-dynamic-attention-for-short-term","arxiv_id":"2203.00937","title":"ES-dRNN with Dynamic Attention for Short-Term Load Forecasting","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.","short_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.","url_abs":"https://arxiv.org/abs/2203.00937v1","url_pdf":"https://arxiv.org/pdf/2203.00937v1.pdf","authors":"[\"Slawek Smyl\", \"Grzegorz Dudek\", \"Pawe\\u0142 Pe\\u0142ka\"]","published":"2022-03-02T00:00:00Z","tasks":"[\"Load Forecasting\", \"Time Series\", \"Time Series Analysis\"]","methods":"[]","has_code":false,"code_links":[{"ID":564155,"CreatedAt":"2026-03-04T21:00:12Z","UpdatedAt":"2026-03-04T21:00:12Z","DeletedAt":null,"paper_id":424651,"paper_url":"https://paperswithcode.com/paper/es-drnn-with-dynamic-attention-for-short-term","paper_title":"ES-dRNN with Dynamic Attention for Short-Term Load Forecasting","repo_url":"https://github.com/slaweks17/es-adrnn","is_official":true,"mentioned_in_paper":true,"mentioned_in_github":false,"framework":"pytorch","github_stars":0}]}