A Neural Network-Based Low-Cost Solar Irradiance Sensor

Abstract

Measuring solar irradiance allows for direct maximization of the efficiency in photovoltaic power plants. However, devices for solar irradiance sensing, such as pyranometers and pyrheliometers, are expensive and difficult to calibrate and thus seldom utilized in photovoltaic power plants. Indirect methods are instead implemented in order to maximize efficiency. This paper proposes a novel approach for solar irradiance measurement based on neural networks, which may, in turn, be used to maximize efficiency directly. An initial estimate suggests the cost of the sensor proposed herein may be price competitive with other inexpensive solutions available in the market, making the device a good candidate for large deployment in photovoltaic power plants. The proposed sensor is implemented through a photovoltaic cell, a temperature sensor, and a low-cost microcontroller. The use of a microcontroller allows for easy calibration, updates, and enhancement by simply adding code libraries. Furthermore, it can be interfaced via standard communication means with other control devices, integrated into control schemes, and remote-controlled through its embedded web server. The proposed approach is validated through experimental prototyping and compared against a commercial device.