Enhancing the missing data imputation of primary substation load demand records
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2020-06-23
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Elsevier Ltd
Resumen
The daily analysis of loads is one of the most important activities for power utilities in order to be able to meet the energy demand. This analysis not only includes short-term forecasting but it also encompasses the completion of missing load data, known as imputation. In this work we show that adding information of attached or bordering primary substation helps to improve the prediction accuracy in a single substation, since its neighbours may share common weather-related (e.g. temperature, humidity, wind direction, etc.) and human-related (e.g. work-calendar, holidays, cultural consumption patterns, etc.) data. In order to validate these approaches, we test the forecasting and imputation neighbouring methodology on a wide variety of datasets. Results confirm that, given a primary substation, the addition of information from surrounding substations does improve the forecasting and imputation errors.
Palabras clave
Data imputation
Load consumption
Missing data treatment
Short-term load forecasting
Substation datasets
Load consumption
Missing data treatment
Short-term load forecasting
Substation datasets
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Borges, C. E., Kamara-Esteban, O., Castillo-Calzadilla, T., Andonegui, C. M., & Alonso-Vicario, A. (2020). Enhancing the missing data imputation of primary substation load demand records. Sustainable Energy, Grids and Networks, 23. https://doi.org/10.1016/J.SEGAN.2020.100369