Electricity Load Forecasting Using LSTM for Household Usage
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-1488-9_3
Reference12 articles.
1. Zheng J, Xu C, Zhang Z, Li X (2017) Electric load forecasting in smart grids using Long-Short-Term-Memory based Recurrent Neural Network. In: 2017 51st Annual conference on information sciences and systems (CISS), Baltimore, MD, USA, pp 1–6. https://doi.org/10.1109/CISS.2017.7926112
2. Yu Y, Fan J, Wang Z, Zhu Z, Xu Y (2023) A dynamic ensemble method for residential short-term load forecasting. Alexandria Eng J 63:75–88. ISSN 1110-0168, https://doi.org/10.1016/j.aej.2022.07.050
3. Yildiz B, Bilbao JI, Sproul AB (2017) A review and analysis of regression and machine learning models on commercial building electricity load forecasting. Renew Sustain Energ Rev 73:1104–1122. ISSN 1364-0321, https://doi.org/10.1016/j.rser.2017.02.023
4. Chen Z, Zhang D, Jiang H et al (2021) Load forecasting based on LSTM neural network and applicable to loads of “replacement of coal with electricity.” J Electr Eng Technol 16:2333–2342. https://doi.org/10.1007/s42835-021-00768-8
5. Hossain MS, Mahmood H (2020) Short-term load forecasting using an LSTM neural network. In: 2020 IEEE power and energy conference at Illinois (PECI), Champaign, IL, USA, pp 1–6. https://doi.org/10.1109/PECI48348.2020.9064654
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