BibTeX_23B

@Article{Caicedo2023,

  author         = {Caicedo-Vivas, Joan Sebastian and Alfonso-Morales, Wilfredo},

  journal        = {Energies},

  title          = {{Short-Term Load Forecasting Using an LSTM Neural Network for a Grid Operator}},

  year           = {2023},

  issn           = {1996-1073},

  number         = {23},

  volume         = {16},

  abstract       = {Electricity is crucial for daily life due to the number of activities that depend on it. To forecast future electric load, which changes over time and depends on various factors, grid operators (GOs) must create forecasting models for various time horizons with a high degree of accuracy because the results have a huge impact on their decision-making regarding, for example, the scheduling of power units to supply user consumption in the short or long term or the installation of new power plants. This has led to the exploration of multiple techniques like statistical models and Artificial Intelligence (AI), with Machine-Learning and Deep-Learning algorithms being the most popular in this latter field. This paper proposes a neural network-based model to forecast short-term load for a Colombian grid operator, considering a seven-day time horizon and using an LSTM recurrent neural network with historical load values from a region in Colombia and calendar features such as holidays and the current month corresponding to the target week. Unlike other LSTM implementations found in the literature, in this work, the LSTM cells read multiple load measurements at once, and the additional information (holidays and current month) is concatenated to the output of the LSTM. The result is used to feed a fully connected neural network to obtain the desired forecast. Due to social problems in the country, the load data presents a strange behavior, which, in principle, affects the prediction capacity of the model. Still, it is eventually able to adjust its forecasts accordingly. The regression metric MAPE measures the model performance, with the best predicted week having an error of 1.65% and the worst week having an error of 26.22%. Additionally, prediction intervals are estimated using bootstrapping.},

  article-number = {7878},

  doi            = {10.3390/en16237878},

  url            = {https://www.mdpi.com/1996-1073/16/23/7878},

}