Publicação
Short-term electricity load forecasting with machine learning
| dc.contributor.author | Aguilar Madrid, Ernesto | |
| dc.contributor.author | António, Nuno | |
| dc.contributor.institution | NOVA Information Management School (NOVA IMS) | |
| dc.contributor.institution | Information Management Research Center (MagIC) - NOVA Information Management School | |
| dc.contributor.pbl | MDPI - Multidisciplinary Digital Publishing Institute | |
| dc.date.accessioned | 2021-02-24T02:07:30Z | |
| dc.date.available | 2021-02-24T02:07:30Z | |
| dc.date.issued | 2021-02-20 | |
| dc.description | Aguilar Madrid, E., & Antonio, N. (2021). Short-term electricity load forecasting with machine learning. Information (Switzerland), 12(2), 1-21. [50]. https://doi.org/10.3390/info12020050 | |
| dc.description.abstract | An accurate short-term load forecasting (STLF) is one of the most critical inputs for power plant units’ planning commitment. STLF reduces the overall planning uncertainty added by the intermittent production of renewable sources; thus, it helps to minimize the hydrothermal electricity production costs in a power grid. Although there is some research in the field and even several research applications, there is a continual need to improve forecasts. This research proposes a set of machine learning (ML) models to improve the accuracy of 168 h forecasts. The developed models employ features from multiple sources, such as historical load, weather, and holidays. Of the five ML models developed and tested in various load profile contexts, the Extreme Gradient Boosting Regressor (XGBoost) algorithm showed the best results, surpassing previous historical weekly predictions based on neural networks. Additionally, because XGBoost models are based on an ensemble of decision trees, it facilitated the model’s interpretation, which provided a relevant additional result, the features’ importance in the forecasting. | en |
| dc.description.version | publishersversion | |
| dc.description.version | published | |
| dc.format.extent | 21 | |
| dc.format.extent | 2606888 | |
| dc.identifier.doi | 10.3390/info12020050 | |
| dc.identifier.issn | 2078-2489 | |
| dc.identifier.other | PURE: 28265368 | |
| dc.identifier.other | PURE UUID: c44844bc-e5e2-4d67-9223-e4c80306609e | |
| dc.identifier.other | Scopus: 85100582142 | |
| dc.identifier.other | WOS: 000622570500001 | |
| dc.identifier.uri | http://hdl.handle.net/10362/112349 | |
| dc.identifier.url | https://www.scopus.com/pages/publications/85100582142 | |
| dc.identifier.url | http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=WOS_CPL&DestLinkType=FullRecord&UT=WOS:000622570500001 | |
| dc.identifier.url | http://doi.org/10.17632/tcmmj4t6f4.1 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.subject | Electricity | |
| dc.subject | Electricity market | |
| dc.subject | Machine learning | |
| dc.subject | Short-term load forecasting | |
| dc.subject | Weekly forecast | |
| dc.subject | Information Systems | |
| dc.subject | SDG 9 - Industry, Innovation, and Infrastructure | |
| dc.title | Short-term electricity load forecasting with machine learning | en |
| dc.type | journal article | |
| degois.publication.firstPage | 1 | |
| degois.publication.issue | 2 | |
| degois.publication.lastPage | 21 | |
| degois.publication.title | Information (Switzerland) | |
| degois.publication.volume | 12 | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess |
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