Multiobjective Optimization of Solar Powered Irrigation System with Fuzzy Type-2 Noise Modelling
This is an incremental domain-specific approach for engineers designing sustainable energy systems, addressing noise factors like solar insolation fluctuations.
The authors tackled the noisy, multiobjective optimization of a solar-powered irrigation system by modeling environmental noise with Fuzzy Type-2 and using the Bacterial Foraging Algorithm to find Pareto-optimal solutions, but no concrete performance numbers were provided.
Optimization is becoming a crucial element in industrial applications involving sustainable alternative energy systems. During the design of such systems, the engineer/decision maker would often encounter noise factors (e.g. solar insolation and ambient temperature fluctuations) when their system interacts with the environment. In this chapter, the sizing and design optimization of the solar powered irrigation system was considered. This problem is multivariate, noisy, nonlinear and multiobjective. This design problem was tackled by first using the Fuzzy Type II approach to model the noise factors. Consequently, the Bacterial Foraging Algorithm (BFA) (in the context of a weighted sum framework) was employed to solve this multiobjective fuzzy design problem. This method was then used to construct the approximate Pareto frontier as well as to identify the best solution option in a fuzzy setting. Comprehensive analyses and discussions were performed on the generated numerical results with respect to the implemented solution methods.