A hybrid ANN-LSTM based model for indoor temperature prediction

Jiang, Liangxiao; Wang, Xinli; Wang, Lei; Shao, Mingjun; Zhuang, Liping

doi:10.1109/iciea51954.2021.9516151

Cited by 4 publications

(1 citation statement)

References 14 publications

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“…Machine learning implementations in different stages of the occupancy prediction workflow were evaluated. One of the most popular algorithms in building occupancy prediction is the neural-networkbased algorithm, particularly ANN -LSTM, which was utilised by more than 10 papers after 2018 [173]. LSTM is a special RNN which has a good effect in dealing with long time sequence problems, with the combination of ANN, it can quantify the impact of features from the sensors and reflect them into the network together with the current time input to participate in training.…”

Section: Discussion and Recommendation For Future Workmentioning

confidence: 99%

A review on occupancy prediction through machine learning for enhancing energy efficiency, air quality and thermal comfort in the built environment

Zhang

Calautit

2022

Renewable and Sustainable Energy Reviews

121

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Section: Discussion and Recommendation For Future Workmentioning

confidence: 99%

A review on occupancy prediction through machine learning for enhancing energy efficiency, air quality and thermal comfort in the built environment

Zhang

Calautit

2022

Renewable and Sustainable Energy Reviews

121

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Design and control of LSTM-ANN controllers for an efficient energy management system in a smart grid based on hybrid renewable energy sources

Wesley,

Babu,

Kumar

2024

Eng. Res. Express

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Many isolated locations, including hilly areas, remote sites, and military camps, lack feasible access to the main power grid. In these conditions, locally established Microgrids can provide the necessary power supply. However, to meet the demands of these isolated areas, numerous individual Microgrids are required. Although some of these Microgrids might be integratable, geographical constraints may preclude full integration. Under these circumstances, the establishment of a smart grid – integrating multiple Microgrids with a battery management system – can potentially solve many power supply issues. A smart grid control system paired with a centralized battery management system is proposed in this paper. The system considers the use of multiple renewable energy sources at various locations for stable and reliable power generation. The proposed method incorporates a long short-term memory (LSTM)-based artificial neural network (ANN) to ensure a stable, high-quality power supply at different load buses. Additionally, this work introduces an artificial intelligence-based operating system designed to maintain energy management under various conditions. To enhance voltage quality, a 7-level aligned multilevel inverter is incorporated into the system. As compared with PI and Fuzzy controllers, the proposed method with LSTM-ANN controller is improved the power quality under sudden changes in the system which are also presented in results. The effectiveness of the proposed energy management system (EMS) is verified by using a hardware-in the-loop approach with OPAL-RT modules, yielding realistic results.

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ANN based Novel Energy Management System of Hybrid Renewable Energy Sources based DC Microgrid

Bhagyalakshmi,

Karanamdeepak,

Nayakaashok

et al. 2023

2023 First International Conference on Advances in Electrical, Electronics and Computational Intelligence (ICAEECI)

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A hybrid ANN-LSTM based model for indoor temperature prediction

Cited by 4 publications

References 14 publications

A review on occupancy prediction through machine learning for enhancing energy efficiency, air quality and thermal comfort in the built environment

A review on occupancy prediction through machine learning for enhancing energy efficiency, air quality and thermal comfort in the built environment

Design and control of LSTM-ANN controllers for an efficient energy management system in a smart grid based on hybrid renewable energy sources

ANN based Novel Energy Management System of Hybrid Renewable Energy Sources based DC Microgrid

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