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.

A Robust MPC strategy for Heating, Ventilation and Air Conditioning Systems (HVAC) is proposed in this work. The typical control objective of minimizing energy consumption while maintaining user comfort is considered in this work. Robust MPC is naturally suited for HVAC systems with the aforementioned control goal because it is a control strategy that considers process constraints and the optimization of a performance index, and explicitly handles uncertainty. In this system, the uncertainty comes from the ambient temperature and internal loads predictions, which are the main factors driving the thermal dynamics. Thus, effectively predicting their future behaviour and uncertainty aids for the quality of the control system. In this context, the main contribution of this work is the introduction of a new framework that uses fuzzy interval models for predicting bounds of uncertain variables in a Robust MPC formulation. These bounds are constructed so that the future values of the relevant variables are within them with a predefined probability. In this work, fuzzy interval models are trained and used to predict the future system disturbances, and these are in turn used to provide bounds for the predictions of room temperatures of the HVAC system. Simulation results show the effectiveness of the proposed strategy, in terms of yielding higher percentage of constraints satisfaction when compared to a classical MPC method. Additionally, it is shown that an appropriate compromise between the system performance and the rate of constraint satisfaction can be achieved by varying the coverage probability of the fuzzy interval models.

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.

This work aims at designing a novel evolving fuzzy prediction interval able to model nonlinear time-variant systems. To achieve this goal, we integrate a passive mechanism meant to update the model when new data are available with an active mechanism able to trigger ad-hoc adaptation mechanisms when changes are detected in the data-generating process. The base model considered for this proposal was the prediction interval based on fuzzy numbers, which allow handling the characterization of the uncertainty of a system through additional parameters focused on the computation of the interval width. The performance of the proposed solution has been tested on both synthetic and real data. In this work, the synthetic data was generated from a generic nonlinear time-variant system, while the real data correspond to measurements of solar power generation obtained from photovoltaic panels. The simulation results confirm the effectiveness of the proposed evolving fuzzy prediction interval for modeling systems that present changes in their dynamics over time.

This paper analyses and discusses the environmental impact of integrating renewable energies in the operation of seaports in a context in which maritime activities are an important contributor to global greenhouse gas emissions. The analysis performed in this work is structured as follows: first, the implementation of smartport microgrids is discussed as an important strategy to control seaports because its design facilitates the integration of different technologies such as cold ironing and distributed energy resources. In this context, reaching a proper integration of cold ironing and renewable energy technologies is essential in the process of reducing pollution levels. Secondly, the main challenges of implementing these technologies are discussed, covering the economic, technical, and environmental aspects. Here, Chile’s lack of proper normative is an important problem that must be addressed to incentivise projects for sustainable smartport implementations. Finally, this work presents a case study where a smartport with cold ironing and renewable energy facilities is designed for the port in Puerto Montt, Chile. The simulation results show the effectiveness of the smartport strategy for reducing emissions and operational costs. Still, this kind of strategy usually requires higher investments due to the inclusion of renewable energy facilities.