Next day load forecast A case study for the city of Lisbon

Svetlana Chemetova, Paulo Santos, Mário Ventim-Neves

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Effective short-term load forecasting plays a crucial role in the operation of both traditional and deregulated power systems. Improving the accuracy of load forecasting can increase the appropriateness of planning and scheduling and reduce operational costs of power systems making them resemble resilient energy systems. In the present paper, we propose the regressive forecast model of the day ahead based on the artificial neural network. The electric load peaks were also calculated by the model. The data used were the time series of active power, recorded by EDP Distribution Telemetry System, collected in Lisbon. The results show that our approach provides a reliable model for forecast daily and hourly energy consumption, as well the load profile with accuracy.

Original languageEnglish
Title of host publicationTechnological Innovation for Resilient Systems - 9th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2018, Proceedings
EditorsLuis M. Camarinha-Matos, Kankam O. Adu-Kankam, Mohammad Julashokri
Place of PublicationCham
PublisherSpringer
Pages62-70
Number of pages9
ISBN (Electronic)978-3-319-78574-5
ISBN (Print)978-3-319-78573-8
DOIs
Publication statusPublished - 1 Jan 2018
Event9th Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2018 - Costa de Caparica, Portugal
Duration: 2 May 20184 May 2018

Publication series

NameIFIP Advances in Information and Communication Technology
PublisherSpringer
Volume521
ISSN (Print)1868-4238

Conference

Conference9th Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2018
CountryPortugal
CityCosta de Caparica
Period2/05/184/05/18

Keywords

  • Electric power system
  • Load forecasting
  • Load pattern
  • Neural networks
  • Peak load

Fingerprint Dive into the research topics of 'Next day load forecast A case study for the city of Lisbon'. Together they form a unique fingerprint.

  • Cite this

    Chemetova, S., Santos, P., & Ventim-Neves, M. (2018). Next day load forecast A case study for the city of Lisbon. In L. M. Camarinha-Matos, K. O. Adu-Kankam, & M. Julashokri (Eds.), Technological Innovation for Resilient Systems - 9th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2018, Proceedings (pp. 62-70). (IFIP Advances in Information and Communication Technology; Vol. 521). Cham: Springer. https://doi.org/10.1007/978-3-319-78574-5_6