Short Term Load Forecasting in Smart Grids: Case Study of the City of Évora

Svetlana Roudolfovna Chemetova 1 * , Paulo Santos 1, Mário Ventim-Neves 2

Journal of Information Systems Engineering & Management, Volume 2, Issue 3, Article No: 18.

https://doi.org/10.20897/jisem.201718

OPEN ACCESS   2297 Views   1484 Downloads

Download Full Text (PDF) Cite this article

Abstract

Currently, load forecasting is a fundamental task for planning, operation and exploration of the electric power systems. The importance of forecasting has become more evident with the restructuring of the national energy sector, thus, promoting projects linked to smart grids, namely in Portugal - InovGrid. This study proposes the computational forecast model of the load diagram based on the Levenberg-Marquardt algorithm of Artificial Neural Networks. The used data are the time series of active power, recorded by EDP Distribution Telemetry System, and the climatic time series of the Portuguese Institute of the Sea and Atmosphere, collected on the city of Évora. The forecast horizon is short term: from one hour to a week. The results showed that main statistical error parameter (mean absolute percentage error) was not exceed 5%.

Keywords

load forecasting, smart-grids, substations, artificial neural networks, time series

Citation

Chemetova, S. R., Santos, P., and Ventim-Neves, M. (2017). Short Term Load Forecasting in Smart Grids: Case Study of the City of Évora. Journal of Information Systems Engineering & Management, 2(3), 18. https://doi.org/10.20897/jisem.201718

Submit a Manuscript