The existing uncertainties during the operation of processes could strongly affect the performance of forecasting systems, control strategies and fault detection systems when they are not considered in the design. Because of that, the study of uncertainty quantification has gained more attention among the researchers during past decades. From this field of study, the prediction intervals arise as one of the techniques most used in literature to represent the effect of uncertainty over the future process behavior. Thus, researchers have focused on developing prediction intervals based on the use of fuzzy systems and neural networks, thanks to their usefulness for represent a wide range of processes as universal approximators. In this work, a review of the state-of-the-art of methodologies for prediction interval modelling based on fuzzy systems and neural networks is presented. The main characteristics of each method for prediction interval construction are presented and some recommendations are given for selecting the most appropriate method for specific applications. To illustrate the advantages of these methodologies, a comparative analysis of selected methods of prediction intervals is presented, using a benchmark series and real data from solar power generation of a microgrid.

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Rural communities usually settle in territories where crop self-consumption is the main source of sustenance. In this context, climate change has made these environments of crop control susceptible to water shortages, impacting crop yields. The implementation of greenhouses has been proposed to address these problems, together with strategies to optimize water and energy consumption. In this study, an energy–water management system based on a model predictive control strategy is proposed. This control strategy consists of a fuzzy optimizer used to determine the optimal consumption from isolated microgrids considering the local resources available. The proposed controller is implemented on two timescales. First, medium-term optimization over one month is used to estimate the necessary water demand required to support crop growth and a high yield. Second, short-term optimization is used to determine the optimal climate conditions inside the greenhouse for managing crop irrigation, refilling the reserve water tank, and providing ventilation. Experiments were conducted to test this approach using a case study of an isolated community. For such a case, energy consumption was reduced, and the irrigation process was optimized. The results indicated that the proposed controller is a viable alternative for implementing intelligent management systems for greenhouses.