Abstract
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 benefit 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 different 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.
| Original language | English |
|---|---|
| Title of host publication | Industrial Applications of Holonic and Multi-Agent Systems - 8th International Conference, HoloMAS 2017, Proceedings |
| Publisher | Springer Verlag |
| Pages | 186-199 |
| Number of pages | 14 |
| Volume | 10444 LNAI |
| ISBN (Print) | 9783319646343 |
| DOIs | |
| Publication status | Published - 2017 |
| Event | 8th International Conference on Industrial Applications of Holonic and Multi-Agent Systems, HoloMAS 2017 - Lyon, France Duration: 28 Aug 2017 → 30 Aug 2017 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 10444 LNAI |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Conference
| Conference | 8th International Conference on Industrial Applications of Holonic and Multi-Agent Systems, HoloMAS 2017 |
|---|---|
| Country | France |
| City | Lyon |
| Period | 28/08/17 → 30/08/17 |
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Keywords
- Microgrids sustainability
- Multi-agent systems
- Prediction models
Cite this
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Prediction models for short-term load and production forecasting in smart electrical grids. / Ferreira, Adriano; Leitão, Paulo; Barata, José.
Industrial Applications of Holonic and Multi-Agent Systems - 8th International Conference, HoloMAS 2017, Proceedings. Vol. 10444 LNAI Springer Verlag, 2017. p. 186-199 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10444 LNAI).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Prediction models for short-term load and production forecasting in smart electrical grids
AU - Ferreira, Adriano
AU - Leitão, Paulo
AU - Barata, José
N1 - Sem PDF.
PY - 2017
Y1 - 2017
N2 - 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 benefit 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 different 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.
AB - 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 benefit 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 different 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.
KW - Microgrids sustainability
KW - Multi-agent systems
KW - Prediction models
UR - http://www.scopus.com/inward/record.url?scp=85028461402&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-64635-0_14
DO - 10.1007/978-3-319-64635-0_14
M3 - Conference contribution
SN - 9783319646343
VL - 10444 LNAI
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 186
EP - 199
BT - Industrial Applications of Holonic and Multi-Agent Systems - 8th International Conference, HoloMAS 2017, Proceedings
PB - Springer Verlag
ER -