Short-Term Electricity Demand Forecast Using Deep RNN and Stacked LSTM-Reference-Cited by-同舟云学术

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Short-Term Electricity Demand Forecast Using Deep RNN and Stacked LSTM

Published:2022-11-11 Issue: Volume: Page:578-588
ISSN:2195-4356
Container-title:Lecture Notes in Mechanical Engineering
language:
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Author:

Singh Surbhi,Tripathi Madan Mohan

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-16-9523-0_64

Reference17 articles.

1. Zhou K et al “A review of electric load classification in smart grid environment”. Renew Sustain Energy Rev 201 24:103–110

2. Hammad et al (2020) Methods and models for electric load forecasting: a comprehensive review. Logistics Sustain Transport 11:51–76

3. González-Briones et al (2019) “Machine learning models for electricity consumption forecasting: a review”. In 2nd international conference on computer app and IS, Saudi Arabia, pp 1–6

4. Tzafestas S, Tzafestas E (2001) Computational intelligence techniques for short term electric load forecasting. J Intell Robotic Syst 31:7–68

5. Rahman S, Hazim O (1993) A generalized knowledge based short term load forecasting technique. IEEE Trans Power Syst 8(2):508–514

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Novel Approach for Electricity Load Forecasting using Hybrid Deep Learning Models;2024 IEEE International Conference for Women in Innovation, Technology & Entrepreneurship (ICWITE);2024-02-16

2. Predictive Analysis of Energy Consumption and Electricity Demand Using Machine Learning Techniques;2023 International Conference on Smart Systems for applications in Electrical Sciences (ICSSES);2023-07-07

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