From Time-Series to Hybrid Models : Advancements in Short-Term Load Forecasting Embracing Smart Grid Paradigm
Ali, Salman (Universitat Politècnica de Catalunya)
Bogarra, Santiago (Universitat Politècnica de Catalunya)
Riaz, Muhammad N. (Universitat Autònoma de Barcelona. Departament de Ciències de la Computació)
Phyo, Phyae Pyae (Eindhoven University of Technology)
Flynn, David (University of Glasgow)
Taha, Ahmad (University of Glasgow)

Data:	2024
Resum:	This review paper is a foundational resource for power distribution and management decisions, thoroughly examining short-term load forecasting (STLF) models within power systems. The study categorizes these models into three groups: statistical approaches, intelligent-computing-based methods, and hybrid models. Performance indicators are compared, revealing the superiority of heuristic search and population-based optimization learning algorithms integrated with artificial neural networks (ANNs) for STLF. However, challenges persist in ANN models, particularly in weight initialization and susceptibility to local minima. The investigation underscores the necessity for sophisticated predictive models to enhance forecasting accuracy, advocating for the efficacy of hybrid models incorporating multiple predictive approaches. Acknowledging the changing landscape, the focus shifts to STLF in smart grids, exploring the transformative potential of advanced power networks. Smart measurement devices and storage systems are pivotal in boosting STLF accuracy, enabling more efficient energy management and resource allocation in evolving smart grid technologies. In summary, this review provides a comprehensive analysis of contemporary predictive models and suggests that ANNs and hybrid models could be the most suitable methods to attain reliable and accurate STLF. However, further research is required, including considerations of network complexity, improved training techniques, convergence rates, and highly correlated inputs to enhance STLF model performance in modern power systems.
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article de revisió ; recerca ; Versió publicada
Publicat a:	Applied sciences (Basel), Vol. 14, Núm. 11 (June 2024) , art. 4442, ISSN 2076-3417

DOI: 10.3390/app14114442

46 p, 2.5 MB

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