RT info:eu-repo/semantics/article
T1 An insight of deep learning based demand forecasting in smart grids
A1 Aguiar Pérez, Javier Manuel
A1 Pérez Juárez, María Ángeles
K1 Demand forecasting
K1 Load forecasting
K1 Demand response
K1 Forecasting horizon
K1 Smart grid
K1 Smart environment
K1 Deep learning
K1 Long short-term memory networks
K1 Convolutional neural networks
K1 33 Ciencias Tecnológicas
AB Smart grids are able to forecast customers’ consumption patterns, i.e., their energy demand, and consequently electricity can be transmitted after taking into account the expected demand. To face today’s demand forecasting challenges, where the data generated by smart grids is huge, modern data-driven techniques need to be used. In this scenario, Deep Learning models are a good alternative to learn patterns from customer data and then forecast demand for different forecasting horizons. Among the commonly used Artificial Neural Networks, Long Short-Term Memory networks—based on Recurrent Neural Networks—are playing a prominent role. This paper provides an insight into the importance of the demand forecasting issue, and other related factors, in the context of smart grids, and collects some experiences of the use of Deep Learning techniques, for demand forecasting purposes. To have an efficient power system, a balance between supply and demand is necessary. Therefore, industry stakeholders and researchers should make a special effort in load forecasting, especially in the short term, which is critical for demand response.
SN 1424-8220
YR 2023
FD 2023
LK https://uvadoc.uva.es/handle/10324/64799
UL https://uvadoc.uva.es/handle/10324/64799
LA eng
NO Sensors Enero 2023, vol. 23, n. 3. p. 1467
NO Producción Científica
DS UVaDOC
RD 27-dic-2024