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Electricity Forecasting Improvement in a Destination Using Tourism Indicators

Author

Listed:
  • Oscar Trull

    (Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, E46022 Valencia, Spain)

  • Angel Peiró-Signes

    (Departamento de Dirección y Organización de Empresas, Universitat Politècnica de València, E46022 Valencia, Spain)

  • J. Carlos García-Díaz

    (Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, E46022 Valencia, Spain)

Abstract

The forecast of electricity consumption plays a fundamental role in the environmental impact of a tourist destination. Poor forecasting, under certain circumstances, can lead to huge economic losses and air pollution, as prediction errors usually have a large impact on the utilisation of fossil fuel-generation plants. Due to the seasonality of tourism, consumption in areas where the industry represents a big part of the economic activity follows a different pattern than in areas with a more regular economic distribution. The high economic impact and seasonality of the tourist activity suggests the use of variables specific to it to improve the electricity demand forecast. This article presents a Holt–Winters model with a tourism indicator to improve the effectiveness on the electricity demand forecast in the Balearic Islands (Spain). Results indicate that the presented model improves the accuracy of the prediction by 0.3%. We recommend the use of this type of model and indicator in tourist destinations where tourism accounts for a substantial amount of the Gross Domestic Product (GDP), we can control a significant amount of the flow of tourists and the electrical balance is controlled mainly by fossil fuel power plants.

Suggested Citation

  • Oscar Trull & Angel Peiró-Signes & J. Carlos García-Díaz, 2019. "Electricity Forecasting Improvement in a Destination Using Tourism Indicators," Sustainability, MDPI, vol. 11(13), pages 1-16, July.
  • Handle: RePEc:gam:jsusta:v:11:y:2019:i:13:p:3656-:d:245253
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