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

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A comprehensive review on the problem of choosing a suitable activation function for the hidden layer of a feed forward neural network has been widely investigated. Since the nonlinear component of a neural network is the main contributor to the network mapping capabilities, the different choices that may lead to enhanced performances, in terms of training, generalization, or computational costs, are analyzed, both in general-purpose and in embedded computing environments. Finally, a strategy to convert a network configuration between different activation functions without altering the network mapping capabilities will be presented.