Comparison of the Effect of Regularization Techniques and Lookback Window Length on Deep Learning Models in Short Term Load Forecasting

Aysegül Kahraman, Peng Hou, Guangya Yang, Zhile Yang

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Abstract

Management of electric power balance requires accurate forecasting of load and generation, especially in the context of renewable energy adoption. In this context, forecasting electric load requires more attention to decrease the uncertainties in the system operation. There have been many studies under this context, however, the effect of the lookback window for both deep learning and regularization techniques has not been fully investigated in the literature. In this study, we developed a comparative study based on 4 typical deep learning techniques, namely MLP, 1D-CNN, LSTM, and a hybrid model that is a combination of 1D-CNN and LSTM to forecast the electrical load. The effect of both regularization methods and lookback window length has been investigated in detail and found that they improved the forecasting performance based on the complexity and features of the networks. The methods are evaluated in terms of 4 different metrics namely MSE, MAE, MAPE and, R2. The results show LSTM outperformed the other methods in general, and the increase of lookback length improved its performance with the average MAPE less than 2%.
Original languageEnglish
Title of host publicationProceedings of 2021 International Top-Level Forum on Engineering Science and Technology Development Strategy
Number of pages15
PublisherSpringer
ISBN (Print)978-981-16-7155-5
Publication statusSubmitted - 2021
Event2021 International Top-Level Forum on Engineering Science and Technology Development Strategy: 6th PURPLE MOUNTAIN FORUM on Smart Grid Protection and Control - Nanjing, China
Duration: 14 Aug 202122 Aug 2021

Conference

Conference2021 International Top-Level Forum on Engineering Science and Technology Development Strategy
Country/TerritoryChina
CityNanjing
Period14/08/202122/08/2021
SeriesLecture Notes in Electrical Engineering
Volume816
ISSN1876-1100

Keywords

  • Deep Learning
  • Load Forecasting
  • Regularization
  • Lookback window

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