Sensitivity Analysis With Artificial Neural Networks for Operation of Photovoltaic Systems

Abstract One of the main disadvantages of artificial neural networks is their inability to provide a physical representation of what happens inside their hidden layers. This has categorized them, in most of the occasions, as black-box models, limiting their use exclusively to the establishment of relationships between input and output variables, ignoring the internal process. In the present chapter, a perspective of the use of sensitivity analysis of artificial neural networks is offered. Sensitivity analysis represents a powerful tool that allows us to solve this problem, granting new skills to artificial neural network models that can be exploited in various areas, ranging from decision making in operating processes to optimization. In this chapter, the sensitivity analysis will be described, its relevance, and the procedure to implement it in artificial neural networks, starting from the case study of temperature estimation in photovoltaic modules.