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Title: An NNwC MPPT-based energy supply solution for sensor nodes in buildings and its feasibility study
Authors: Chang, S 
Wang, Q
Hu, H 
Ding, Z
Guo, H 
Keywords: Energy saving
Maximum power point tracking (MPPT)
Neural network
Sensor nodes
Solar energy harvester
Issue Date: 2019
Publisher: Molecular Diversity Preservation International (MDPI)
Source: Energies, 2019, v. 12, no. 1, en12010101 How to cite?
Journal: Energies 
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.
EISSN: 1996-1073
DOI: 10.3390/en12010101
Rights: © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (
The following publication Chang S, Wang Q, Hu H, Ding Z, Guo H. An NNwC MPPT-Based Energy Supply Solution for Sensor Nodes in Buildings and Its Feasibility Study. Energies. 2019; 12(1):101 is available at
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