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Developing Energy Demand Forecasting Methods

In: Handbook of Smart Energy Systems

Author

Listed:
  • Willian Y. Takano

    (University of São Paulo)

  • Eduardo N. Asada

    (University of São Paulo)

Abstract

This chapter presents the basics of the load forecasting problem. Issues related to the modeling and its resolution, such as defining the scope that involves classification, load series characteristics and factors that affect the resolution, are discussed briefly. The popular models in the context of information technology and techniques are presented accompanied by performance metrics that allows verifying the accuracy between different models. Special highlight is given to the classical artificial neural network approach with its most used methods: feedforward and recurrent networks that present good performance and constant evolution trough time. In the family of recurrent networks, the deep learning methods have gained special attention such as the long short-term memory (LSTM) and the gated recurrent unit (GRU), both with the ability to map the dynamic nature of the load.

Suggested Citation

  • Willian Y. Takano & Eduardo N. Asada, 2023. "Developing Energy Demand Forecasting Methods," Springer Books, in: Michel Fathi & Enrico Zio & Panos M. Pardalos (ed.), Handbook of Smart Energy Systems, pages 1393-1411, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-97940-9_47
    DOI: 10.1007/978-3-030-97940-9_47
    as

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