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%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Energy Information Administration (EIA), https://www.eia.gov/outlooks/ieo/. Last accessed 14 Apr 2021
Notton G et al (2018) Intermittent and stochastic character of renewable energy sources: consequences, cost of intermittence and benefit of forecasting. Renew sustain Energy Rev 87:96–105
He W (2017) Load forecasting via deep neural networks. Procedia Comput Sci 122:308–314
Alpaydin E (2020) Introduction to machine learning. MIT press
Masum S, Liu Y, Chiverton J (2018) Multi-step time series forecasting of electric load using machine learning models. In: International conference on artificial intelligence and soft computing. Springer, Cham
Eskandari H, Imani M, Moghaddam MP (2021) Convolutional and recurrent neural network based model for short-term load forecasting. Electric Power Syst Res 195:107173
Hosein S, Hosein P (2017) Load forecasting using deep neural networks. 2017 IEEE power & energy society innovative smart grid technologies conference (ISGT). IEEE
Tian C, et al. (2018) A deep neural network model for short-term load forecast based on long short-term memory network and convolutional neural network. Energies 11(12): 3493
Srivastava N et al (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958
Zhu D et al. (2018) A machine learning approach for air quality prediction: Model regularization and optimization. Big Data Cognit Comput 2(1):5
Bouktif S, et al. (2018) Optimal deep learning lstm model for electric load forecasting using feature selection and genetic algorithm: comparison with machine learning approaches. Energies 11(7):1636
Ghiasi G, Lin T-Y, Le QV (2018) Dropblock: a regularization method for convolutional networks. arXiv preprint arXiv:1810.12890
Goodfellow I, et al. (2016) Deep learning, vol 1, no 2. Cambridge, MIT press
Understanding LSTM Networks, http://colah.github.io/posts/2015-08-Understanding-LSTMs/. Last accessed 18 Apr 2021
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Kahraman, A., Hou, P., Yang, G., Yang, Z. (2022). Comparison of the Effect of Regularization Techniques and Lookback Window Length on Deep Learning Models in Short Term Load Forecasting. In: Xue, Y., Zheng, Y., Novosel, D. (eds) Proceedings of 2021 International Top-Level Forum on Engineering Science and Technology Development Strategy . PMF PMF 2019 2021. Lecture Notes in Electrical Engineering, vol 816. Springer, Singapore. https://doi.org/10.1007/978-981-16-7156-2_45
Download citation
DOI: https://doi.org/10.1007/978-981-16-7156-2_45
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-7155-5
Online ISBN: 978-981-16-7156-2
eBook Packages: EnergyEnergy (R0)