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.

Keywords

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