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Predicting Hourly Energy Consumption. Can Regression Modeling Improve on an Autoregressive Baseline?

, , , , and . DARE, volume 9518 of Lecture Notes in Computer Science, page 105-122. Springer, (2015)

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Ontology-driven multilevel sequential pattern mining: mining for gold in event logs of photovoltaic plants., , , and . IEEE Conf. on Intelligent Systems, page 1-7. IEEE, (2018)Event Relevancy Pruning in Support of Energy-Efficient Sequential Pattern Mining., , and . Nemesis/UrbReas/SoGood/IWAISe/GDM@PKDD/ECML, volume 11329 of Lecture Notes in Computer Science, page 227-242. Springer, (2018)Predicting Hourly Energy Consumption. Can Regression Modeling Improve on an Autoregressive Baseline?, , , , and . DARE, volume 9518 of Lecture Notes in Computer Science, page 105-122. Springer, (2015)Data-driven Relevancy Estimation for Event Logs Exploration and Preprocessing., , and . ICAART (2), page 395-404. SciTePress, (2018)Industrial Assets Performance Labelling Based on Numerically Encoded Event Logs., , and . BNAIC/BENELEARN, volume 2491 of CEUR Workshop Proceedings, CEUR-WS.org, (2019)A semantic model of events for integrating photovoltaic monitoring data., , , , , and . INDIN, page 24-30. IEEE, (2015)Annotating the Performance of Industrial Assets via Relevancy Estimation of Event Logs., , and . ICMLA, page 1261-1268. IEEE, (2018)Root causes labelling of industrial assets via relevancy estimation of event logs., , and . ICDM, page 17-32. ibai Publishing, (2020)