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Advisor(s)
Abstract(s)
The scheduling of household smart load devices play a key
role in microgrid ecosystems, and particularly in underpowered grids.
The management and sustainability of these microgrids could bene t
from the application of short-term prediction for the energy production
and demand, which have been successfully applied and matured in
larger scale systems, namely national power grids. However, the dynamic
change of energy demand, due to the necessary adjustments aiming to
render the microgrid self-sustainability, makes the forecasting process
harder. This paper analyses some prediction techniques to be embedded
in intelligent and distributed agents responsible to manage electrical
microgrids, and especially increase their self-sustainability. These prediction
techniques are implemented in R language and compared according
to di erent prediction and historical data horizons. The experimental results
shows that none is the optimal solution for all criteria, but allow to
identify the best prediction techniques for each scenario and time scope.
Description
Keywords
Multi-agent systems Prediction models Microgrids sustainability
Citation
Ferreira, Adriano; Leitão, Paulo; Barata, José (2017). Prediction models for short-term load and production forecasting in smart electrical grids. In 8th International Conference on Industrial Applications of Holonic and Multi-Agent Systems, HoloMAS 2017. Lyon. [S.l.]: Springer, p. 186-199. ISBN 978-331964634-3