Comparison of Three Methods for a Weather Based Day-Ahead Load Forecasting

Mingzhe Zou, Jiachen Gu, Duo Fang, Gareth Harrison, Sasa Djokic, Xinying Wang, Chen Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Day-ahead load forecasting plays an increasingly important role for the operation of networks and generation dispatch. The accuracy of the forecasting depends on many factors, including the quality and size of historical data, selected forecasting model and available information on influential factors (e.g., weather data). This paper compares three state-of-the-art models in terms of their ability to capture complex variations in load profiles and provide accurate day-ahead load forecasting: multilayer perceptron (MLP), gradient boosting regression trees (GBRT) and stacked bidirectional long short-term memory (SB-LSTM). The models are implemented on one dataset from China and another from Scotland. The performance is evaluated using mean absolute error (MAE), mean absolute percentage error (MAPE) and other energy-based indices. The presented results show that GBRT outperforms MLP with the same expert extracted load characteristics as additional inputs, but SB-LSTM provides the most accurate forecasting results, without extracting any artificial data feature from the two considered demand datasets.

Original languageEnglish
Title of host publicationProceedings of 2019 IEEE PES Innovative Smart Grid Technologies Europe, ISGT-Europe 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538682180
DOIs
Publication statusPublished - 11 Nov 2019
Event2019 IEEE PES Innovative Smart Grid Technologies Europe, ISGT-Europe 2019 - Bucharest, Romania
Duration: 29 Sep 20192 Oct 2019

Publication series

NameProceedings of 2019 IEEE PES Innovative Smart Grid Technologies Europe, ISGT-Europe 2019

Conference

Conference2019 IEEE PES Innovative Smart Grid Technologies Europe, ISGT-Europe 2019
Country/TerritoryRomania
CityBucharest
Period29/09/192/10/19

Keywords

  • Day-ahead load forecasting
  • deep learning
  • multilayer perceptron
  • recurrent neural network
  • regression tree ensemble

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