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A backpropagation neural network-based hybrid energy recognition and management system

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  • Zhu, Xiwen
  • Li, Mingxue
  • Liu, Xiaoqiang
  • Zhang, Yufeng

Abstract

For several decades, small electronic devices like wireless sensor network nodes (WSNs) tend to be powered by ambient energy, and the multi-input energy platform attracts much attention because sensors are usually used in complicated surroundings. However, for multi-input energy platform energy management is complex and the demand of the consumers is stochastic. To solve the problems, this paper presents a backpropagation neural network (BPNN) based hybrid energy recognition and management System (ERMS). The design applies artificial intelligence algorithms to energy forecasting recognition. And it achieves energy-matching management according to recognition results. Besides, we implemented the energy recognition algorithm on an application specific integrated circuit (ASIC) innovatively, which is manufactured in a standard 180 nm CMOS technology. The energy recognition chip area is 1.45mm × 1.45 mm. The experimental data present that the system can identify different types of input energy and control the energy flows automatically. The current consumption of the ASIC is 65μA at 1 MHz and the recognition accuracy can reach 98 %. Moreover, the hybrid energy recognition and management system platform worked effectively. The measurement results show that the power conversion efficiency of the system to photovoltaic energy input is 85 %. Furthermore, when the input is piezoelectric energy, the power management system output power can achieve 7.4 mW.

Suggested Citation

  • Zhu, Xiwen & Li, Mingxue & Liu, Xiaoqiang & Zhang, Yufeng, 2024. "A backpropagation neural network-based hybrid energy recognition and management system," Energy, Elsevier, vol. 297(C).
    • Handle: RePEc:eee:energy:v:297:y:2024:i:c:s0360544224010375
    DOI: 10.1016/j.energy.2024.131264
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    References listed on IDEAS

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    1. Hu, Yusha & Li, Jigeng & Hong, Mengna & Ren, Jingzheng & Lin, Ruojue & Liu, Yue & Liu, Mengru & Man, Yi, 2019. "Short term electric load forecasting model and its verification for process industrial enterprises based on hybrid GA-PSO-BPNN algorithm—A case study of papermaking process," Energy, Elsevier, vol. 170(C), pages 1215-1227.
    2. Ma, Bin & Guo, Xing & Li, Penghui, 2023. "Adaptive energy management strategy based on a model predictive control with real-time tuning weight for hybrid energy storage system," Energy, Elsevier, vol. 283(C).
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    1. Rizk M Rizk-Allah & Lobna M Abouelmagd & Ashraf Darwish & Vaclav Snasel & Aboul Ella Hassanien, 2024. "Explainable AI and optimized solar power generation forecasting model based on environmental conditions," PLOS ONE, Public Library of Science, vol. 19(10), pages 1-33, October.
    2. Chen, Yifan & Yang, Liuquan & Yang, Chao & Wang, Weida & Zha, Mingjun & Gao, Pu & Liu, Hui, 2024. "Real-time analytical solution to energy management for hybrid electric vehicles using intelligent driving cycle recognition," Energy, Elsevier, vol. 307(C).
    3. Sepehrzad, Reza & Langeroudi, Amir Saman Godazi & Al-Durra, Ahmed & Anvari-Moghaddam, Amjad & Sadabadi, Mahdieh S., 2025. "Demand response-based multi-layer peer-to-peer energy trading strategy for renewable-powered microgrids with electric vehicles," Energy, Elsevier, vol. 320(C).

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