Developing Energy Demand Forecasting Methods

Willian Y. Takano and Eduardo N. Asada ()
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Willian Y. Takano: University of São Paulo
Eduardo N. Asada: University of São Paulo

A chapter in Handbook of Smart Energy Systems, 2023, pp 1393-1411 from Springer

Abstract: 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.

Keywords: Load forecasting; Feedforward neural network; Recurrent neural network; Deep learning (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-97940-9_47

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DOI: 10.1007/978-3-030-97940-9_47

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