2025-01-162025-01-162024Delgado, J., Aldarraji, M., Vega Márquez, B. y Pontes Balanza, B. (2024). Time Series Analysis of Iraq’s Energy Sector: Production, Consumption, and Trends. En The 19th International Conference on Soft Computing Models in Industrial and Environmental Applications SOCO 2024. Volum. 1 Lecture Notes in Networks and Systems (172-181), Springer.978-3-031-75012-0978-3-031-75013-72367-33702367-3389https://hdl.handle.net/11441/166799This study is motivated by Iraq’s dependence on external energy sources despite having diverse energy options. The main aim is to predict energy demand based on past data, also including unique events like local festivals or critical periods of time. LSTM networks are chosen for their memory feature, enabling predictions based on past time periods. Data from 2019 to 2022 on energy production and demand in each province of Iraq is utilized, where the dataset has been studied by hours, days, and months in search of better predictions. Different ways of training the model have been explored, including different parametrization, groupings of the demand data and additional characteristics, culminating in the interpretation of the best prediction. The incorporation of additional external information to the model has been proved to enhance its precision, and the obtained results align with Iraq’s macroeconomic landscape, confirming the the appropriateness of incorporating various additional timestamp features, such as seasons, critical periods and holidays to the prediction model.application/pdf10 p.engAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Electricity demand forecastingLSTM time series PredictionIraq energy analysisinfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/openAccesshttps://doi.org/10.1007/978-3-031-75013-7_26