Short-Term Forecasting of Thermostatic and Residential Loads Using Long Short-Term Memory Recurrent Neural Networks
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| Yayımlandı: | arXiv.org (Dec 20, 2024), p. n/a |
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| Yazar: | |
| Diğer Yazarlar: | , , |
| Baskı/Yayın Bilgisi: |
Cornell University Library, arXiv.org
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| Konular: | |
| Online Erişim: | Citation/Abstract Full text outside of ProQuest |
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| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3148682471 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 3148682471 | ||
| 045 | 0 | |b d20241220 | |
| 100 | 1 | |a Nguyen, Bang | |
| 245 | 1 | |a Short-Term Forecasting of Thermostatic and Residential Loads Using Long Short-Term Memory Recurrent Neural Networks | |
| 260 | |b Cornell University Library, arXiv.org |c Dec 20, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a Internet of Things (IoT) devices in smart grids enable intelligent energy management for grid managers and personalized energy services for consumers. Investigating a smart grid with IoT devices requires a simulation framework with IoT devices modeling. However, there lack comprehensive study on the modeling of IoT devices in smart grids. This paper investigates the IoT device modeling of a thermostatic load and implements the recurrent neural networks model for short-term load forecasting in this IoT-based thermostatic load. The recurrent neural network structure is leveraged to build a load forecasting model on temporal correlation. The temporal recurrent neural network layers including long short-term memory cells are employed to learn the data from both the simulation platform and New South Wales residential datasets. The simulation results are provided for demonstration. | |
| 653 | |a Recurrent neural networks | ||
| 653 | |a Energy management | ||
| 653 | |a Memory devices | ||
| 653 | |a Residential energy | ||
| 653 | |a Electrical loads | ||
| 653 | |a Internet of Things | ||
| 653 | |a Smart grid | ||
| 653 | |a Modelling | ||
| 653 | |a Forecasting | ||
| 700 | 1 | |a Panwar, Mayank | |
| 700 | 1 | |a Hovsapian, Rob | |
| 700 | 1 | |a Agalgaonkar, Yashodhan | |
| 773 | 0 | |t arXiv.org |g (Dec 20, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3148682471/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2412.15607 |