2018
DOI: 10.1016/j.compeleceng.2017.07.006
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Smart grid load forecasting using online support vector regression

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Cited by 144 publications
(61 citation statements)
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“…The second benchmarking base model is support vector regression. Since the method of support vector machines(SVR) was proposed in 1995 [33], SVR is a popular machine learning model proposed for forecasting tasks even recently [34][35][36]. It has also been used as a benchmark for comparison in various studies in the survey of related works [14,18].…”
Section: Benchmarking Base Modelmentioning
confidence: 99%
“…The second benchmarking base model is support vector regression. Since the method of support vector machines(SVR) was proposed in 1995 [33], SVR is a popular machine learning model proposed for forecasting tasks even recently [34][35][36]. It has also been used as a benchmark for comparison in various studies in the survey of related works [14,18].…”
Section: Benchmarking Base Modelmentioning
confidence: 99%
“…], [14], [25], [42], [45], [78], [101], [110], [122], [124], [132], [156], [164], [171], [178], [187], [210], [211], [213], [228], [237], [242], [256]), Support Vector Machines (SVM) (21) ( [? ], [36], [53], [57], [65], [78], [79], [106], [115], [117], [122], [157], [159], [166], [187], [193], [203], [227], [240], [253], [256]), autoregressive integrated moving average (ARIMA) (13) ( [6], [19], [32], [42], [53], [78], [90],…”
Section: Sms Resultsmentioning
confidence: 99%
“…Framing can be done either with constant or with variable frame length [35,47]. In the state-based baseline NILM approach, in order to estimate the device consumption on a state level, a regression algorithm instead of a classification algorithm is used [48,49], while classification is used in event-based approaches to detect devices' on/off states [39,45,46].…”
Section: Baseline Nilm Architecturementioning
confidence: 99%