Document Type

Article

Source of Publication

Results in Engineering

Publication Date

9-1-2023

Abstract

There has been a significant increase in the attention paid to resource management in smart grids, and several energy forecasting models have been published in the literature. It is well known that energy forecasting plays a crucial role in several applications in smart grids, including demand-side management, optimum dispatch, and load shedding. A significant challenge in smart grid models is managing forecasts efficiently while ensuring the slightest feasible prediction error. A type of artificial neural networks such as recurrent neural networks, are frequently used to forecast time series data. However, due to certain limitations like vanishing gradients and lack of memory retention of recurrent neural networks, sequential data should be modeled using convolutional networks. The reason is that they have strong capabilities to solve complex problems better than recurrent neural networks. In this research, a temporal convolutional network is proposed to handle seasonal short-term energy forecasting. The proposed temporal convolutional network computes outputs in parallel, reducing the computation time compared to the recurrent neural networks. Further performance comparison with the traditional long short-term memory in terms of MAD and sMAPE has proved that the proposed model has outperformed the recurrent neural network.

ISSN

2590-1230

Publisher

Elsevier BV

Volume

19

Disciplines

Computer Sciences

Keywords

Energy forecasting, Seasonal energy, Smart grids, Temporal convolutional networks

Scopus ID

85165632095

Indexed in Scopus

yes

Open Access

yes

Open Access Type

Hybrid: This publication is openly available in a subscription-based journal/series

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