@inproceedings{8103c2eae5224184881e5b6b658f1562,
title = "Next day load forecast A case study for the city of Lisbon",
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.",
keywords = "Electric power system, Load forecasting, Load pattern, Neural networks, Peak load",
author = "Svetlana Chemetova and Paulo Santos and M{\'a}rio Ventim-Neves",
year = "2018",
month = jan,
day = "1",
doi = "10.1007/978-3-319-78574-5_6",
language = "English",
isbn = "978-3-319-78573-8",
series = "IFIP Advances in Information and Communication Technology",
publisher = "Springer",
pages = "62--70",
editor = "Camarinha-Matos, {Luis M.} and Adu-Kankam, {Kankam O.} and Mohammad Julashokri",
booktitle = "Technological Innovation for Resilient Systems - 9th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2018, Proceedings",
note = "9th Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2018 ; Conference date: 02-05-2018 Through 04-05-2018",
}
