Essays in Econometrics 2001
DOI: 10.1017/cbo9780511753961.027
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Short-Run Forecasts of Electricity Loads and Peaks

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Cited by 63 publications
(89 citation statements)
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“…Furthermore, several data-management and data-mining techniques for time series, such as indexing, clustering and classification [19], provide means for compressed time-series representation (Section 2.2). Another topic, time-series forecasting (Section 2.3), has gained much attention from the research community [1,7,33]. As time-series forecasting is very important in smart electricity grids [8], we use it as one important scenario in our evaluation.…”
Section: Related Workmentioning
confidence: 99%
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“…Furthermore, several data-management and data-mining techniques for time series, such as indexing, clustering and classification [19], provide means for compressed time-series representation (Section 2.2). Another topic, time-series forecasting (Section 2.3), has gained much attention from the research community [1,7,33]. As time-series forecasting is very important in smart electricity grids [8], we use it as one important scenario in our evaluation.…”
Section: Related Workmentioning
confidence: 99%
“…In recent research, an impressive number of forecasting techniques has been developed particularly for energy demand [1,7,33]. The author in [40] shows that the so-called exponential smoothing technique behaves particularly well in the case of short-term energy demand forecasting.…”
Section: Time-series Forecastingmentioning
confidence: 99%
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“…Additionally, the Forecasting component needs to efficiently process new energy measurements to detect changes in the upcoming energy production or consumption and to enable the rescheduling of flex-offers if necessary. [12], [13]. Such models involve a large number of parameters leading to high model creation times due to expensive parameter estimation.…”
Section: Real-time Time Series Forecastingmentioning
confidence: 99%
“…First of all, energy demand models often involve multi-equation models [13] that create a different forecast models for different time intervals (e.g., for each hour of the day). As multi-equation models consists of several independent individual models, we reduce the parameter estimation time by partitioning the time series and parallelizing the estimation process [16].…”
Section: Real-time Model Adaptionmentioning
confidence: 99%