Informatica

Nowadays, there is a lack of smart marine monitoring systems, which have possibilities to integrate multi-dimensional components for monitoring and predicting marine water quality and making decisions for their optimal operations with minimal human intervention. This research aims to extend the smart coastal marine monitoring by proposing a solar energy planning and control component. The proposed approach involves the adaptive neuro-fuzzy inference system (ANFIS) for the wireless buoys, working online during the whole year in the Baltic Sea near the Lithuanian coast. The usage of our proposed fuzzy solar energy planning and control components allows us to prolong the lifespan of batteries in buoys, so it has a positive impact on sustainable development. The novelty and advantage of the proposed approach lie in establishing the ANFIS-based model to predict and control solar energy in a buoy for different lighting and temperature conditions depending on the four year seasons and to make a decision to transfer the collected data. The energy planning and consumption system for the wireless sensor network of buoys is carefully evaluated, and its prototype is developed. The proposed approach can be practically used for environmental monitoring, providing stakeholders with relevant and timely information for sound decision-making about hydro-meteorological situations in coastal marine water.

Most of the Takagi–Sugeno Fuzzy (TSF) systems found in the literature have only used linear functions of input variables as rule consequent and can be called as TSF Models with Fixed Coefficient (TSFMFC). This paper presents TSF model with variable coefficient (TSFMVC) which can more closely approximate a class of nonlinear systems, nonlinear dynamic systems, and nonlinear control systems. It is also shown that TSFMFC is a special case of TSFMVC. Moreover Variable Gain TSF Controller (VGTSFC) is defined and it performs better, as shown by the simulation results, when compared with Fixed Gain TSF Controller (FGTSFC).