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Heuristics-Based Energy Demand Forecasting with Scarce Data in the Department of Huila, Colombia
Pronóstico de la Demanda de Energía Basado en Heurística con Datos Escasos en el Departamento del Huila, Colombia
DOI:
https://doi.org/10.15446/ing.investig.109551Keywords:
demand forecasting, energy, heuristics, scarce data (en)pronóstico de la demanda, energía, heurística, datos escasos (es)
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Within the framework of the energy transition, electrical distribution grid operators require effective tools to predict the demand of individual users. These tools are necessary for an adequate planning of future generation resources and infrastructure modernization. However, understanding future electricity needs poses a significant challenge, especially in emerging economies, where historical data are manually collected on a monthly or bi-monthly basis and exhibit a significant amount of missing information. In response to the above, this work proposes a novel heuristics-based method for medium-term energy demand forecasting with scarce data. Qualitative and quantitative information was abstracted into a mathematical model representing the trend and noise components of historical energy consumption observations. In addition, external factors were considered as an additional layer for the mathematical model, in order to account for events that could not be foreseen by merely using the dataset. A train-test data split was proposed to iteratively search for the best parameters to predict electricity demand with respect to different categorical divisions of users (social stratum, rural or urban location, and municipality). For testing and validation, real historical data were used, as provided by the regional distribution system operator (DSO) of the department of Huila, Colomba. The results suggest a trade-off between accuracy and computational intensity, as well as the fact that a non-categorical approach leads to the algorithm with the best performance (average absolute error of 1.61%) at a low computational cost.
En el marco de la transición energética, los operadores de redes de distribución eléctrica requieren herramientas efectivas para predecir la demanda de usuarios individuales. Estas herramientas son necesarias para un planeamiento adecuado de los recursos futuros de generación y la modernización de la infraestructura. Sin embargo, entender las necesidades futuras de electricidad constituye un desafı́o significativo, especialmente en economı́as emergentes donde los datos históricos son recolectados manualmente en perı́odos mensuales o bimensuales y presentan una cantidad significativa de información faltante. En respuesta a esto, se propone un novedoso método basado en heurı́stica para el pronóstico de la demanda de energı́a en el mediano plazo con datos escasos. Se abstrajo información cualitativa y cuantitativa en un modelo matemático que representa las componentes de tendencia y ruido en observaciones históricas de consumo de energı́a. Adicionalmente, se consideraron factores externos como capa adicional para el modelo matemático, en aras de dar cuenta de eventos que no podrı́an ser previstos solamente con el conjunto de datos. Se propuso una división de datos de entrenamiento y prueba con el fin de buscar iterativamente los mejores parámetros para predecir la demanda de electricidad respecto a diferentes divisiones categóricas de usuarios (estrato social, ubicación rural o urbana y municipio). Para realizar pruebas y validaciones, se utilizaron datos históricos reales proporcionados por el operador del sistema de distribución (OSD) regional del departamento del Huila, Colombia. Los resultados sugieren que hay una compensación entre precisión e intensidad computacional, y que un enfoque no categórico resulta en el algoritmo con un mejor desempeño (error absoluto promedio de 1.61 %) y un bajo costo computacional.
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Copyright (c) 2024 Juan Cuenca, Diego Palacios-Castro, Rodolfo García
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