Short-Term Load Forecasting Using an Attended Sequential Encoder-Stacked Decoder Model with Online Training
Source abstract
Abstract
The paper presents a new approach for the prediction of load active power 24 h ahead using an attended sequential encoder and stacked decoder model with Long Short-Term Memory cells. The load data are owned by the New York Independent System Operator (NYISO) and is dated from the years 2014–2017. Due to dynamics in the load patterns, multiple short pieces of training on pre-filtered data are executed in combination with the transfer learning concept. The evaluation is done by direct comparison with the results of the NYISO forecast and additionally under consideration of several benchmark methods. The results in terms of the Mean Absolute Percentage Error range from 1.5% for the highly loaded New York City zone to 3% for the Mohawk Valley zone with rather small load consumption. The execution time of a day ahead forecast including the training on a personal computer without GPU accounts to 10 s on average.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Energy Load and Power Forecasting · Smart Grid Energy Management · Solar Radiation and Photovoltaics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Siemens AG, Humboldtstr. 59, 90459 Nuremberg, Germany
Institute of Medical Informatics, University of Lubeck, 23562 Lubeck, Germany
References (35)
Show source-provided references
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