An NNwC MPPT-Based energy supply solution for sensor nodes in buildings and its feasibility study

Shuhao Chang; Qiancheng Wang; Haihua Hu; Zijian Ding; Hansen Guo

doi:10.3390/en12010101

An NNwC MPPT-Based energy supply solution for sensor nodes in buildings and its feasibility study

Shuhao Chang
, Qiancheng Wang
, Haihua Hu
, Zijian Ding
, Hansen Guo

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

18 Citations (Scopus)

47 Downloads (CityUHK Scholars)

Abstract

Sensors for data collecting are vital in the development of IoT and intelligent systems. High power consuming current and voltage monitors are indispensable in conducting maximum power point tracking (MPPT) in traditional PV energy wireless sensor nodes. This paper presents a sensor node system based on Neural Network MPPT with cloud method (NNwC) which utilizes information sharing process that is specific to sensor networks. NNwC uses a few sample sensor nodes to collect environmental parameter data such as light intensity (L) and temperature (T) to build the MPPT regression model by Neural Network. Then all other functional sensor nodes implement the model with their environmental parametervalues to conduct MPPT. As a result, the new sensor node system reduces energy consumption as well as the size and cost of the harvester. Then, this paper provides a SPICE simulation to estimate the percentage of power consumption reduced in the new sensor node system and also estimates the percentage of loss in neural network MPPT power generation compared with the perfect MPPT. Finally, the study compares the economic and environmental performance of the proposed system and the traditional ones through a case in a real building situation. © 2018 by the authors.

Original language	English
Article number	en12010101
Number of pages	20
Journal	Energies
Volume	12
Issue number	1
DOIs	https://doi.org/10.3390/en12010101
Publication status	Published - 1 Jan 2019
Externally published	Yes

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

Funding

This research received no external funding.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Research Keywords

energy saving
maximum power point tracking (MPPT)
neural network
sensor nodes
solar energy harvester

Publisher's Copyright Statement

This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/

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10.3390/en12010101

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title = "An NNwC MPPT-Based energy supply solution for sensor nodes in buildings and its feasibility study",

abstract = "Sensors for data collecting are vital in the development of IoT and intelligent systems. High power consuming current and voltage monitors are indispensable in conducting maximum power point tracking (MPPT) in traditional PV energy wireless sensor nodes. This paper presents a sensor node system based on Neural Network MPPT with cloud method (NNwC) which utilizes information sharing process that is specific to sensor networks. NNwC uses a few sample sensor nodes to collect environmental parameter data such as light intensity (L) and temperature (T) to build the MPPT regression model by Neural Network. Then all other functional sensor nodes implement the model with their environmental parametervalues to conduct MPPT. As a result, the new sensor node system reduces energy consumption as well as the size and cost of the harvester. Then, this paper provides a SPICE simulation to estimate the percentage of power consumption reduced in the new sensor node system and also estimates the percentage of loss in neural network MPPT power generation compared with the perfect MPPT. Finally, the study compares the economic and environmental performance of the proposed system and the traditional ones through a case in a real building situation. {\textcopyright} 2018 by the authors.",

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TY - JOUR

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AU - Wang, Qiancheng

AU - Hu, Haihua

AU - Ding, Zijian

AU - Guo, Hansen

N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

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N2 - Sensors for data collecting are vital in the development of IoT and intelligent systems. High power consuming current and voltage monitors are indispensable in conducting maximum power point tracking (MPPT) in traditional PV energy wireless sensor nodes. This paper presents a sensor node system based on Neural Network MPPT with cloud method (NNwC) which utilizes information sharing process that is specific to sensor networks. NNwC uses a few sample sensor nodes to collect environmental parameter data such as light intensity (L) and temperature (T) to build the MPPT regression model by Neural Network. Then all other functional sensor nodes implement the model with their environmental parametervalues to conduct MPPT. As a result, the new sensor node system reduces energy consumption as well as the size and cost of the harvester. Then, this paper provides a SPICE simulation to estimate the percentage of power consumption reduced in the new sensor node system and also estimates the percentage of loss in neural network MPPT power generation compared with the perfect MPPT. Finally, the study compares the economic and environmental performance of the proposed system and the traditional ones through a case in a real building situation. © 2018 by the authors.

AB - Sensors for data collecting are vital in the development of IoT and intelligent systems. High power consuming current and voltage monitors are indispensable in conducting maximum power point tracking (MPPT) in traditional PV energy wireless sensor nodes. This paper presents a sensor node system based on Neural Network MPPT with cloud method (NNwC) which utilizes information sharing process that is specific to sensor networks. NNwC uses a few sample sensor nodes to collect environmental parameter data such as light intensity (L) and temperature (T) to build the MPPT regression model by Neural Network. Then all other functional sensor nodes implement the model with their environmental parametervalues to conduct MPPT. As a result, the new sensor node system reduces energy consumption as well as the size and cost of the harvester. Then, this paper provides a SPICE simulation to estimate the percentage of power consumption reduced in the new sensor node system and also estimates the percentage of loss in neural network MPPT power generation compared with the perfect MPPT. Finally, the study compares the economic and environmental performance of the proposed system and the traditional ones through a case in a real building situation. © 2018 by the authors.

KW - energy saving

KW - maximum power point tracking (MPPT)

KW - neural network

KW - sensor nodes

KW - solar energy harvester

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U2 - 10.3390/en12010101

DO - 10.3390/en12010101

M3 - RGC 21 - Publication in refereed journal

SN - 1996-1073

VL - 12

JO - Energies

JF - Energies

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M1 - en12010101

ER -

An NNwC MPPT-Based energy supply solution for sensor nodes in buildings and its feasibility study

Abstract

Bibliographical note

Funding

UN SDGs

Research Keywords

Publisher's Copyright Statement

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