An Optimization Scheme Based on Fuzzy Logic Control for Efficient Energy Consumption in Hydroponics Environment

Abstract

As the world population is increasing rapidly, food and water demands are the most crucial problem for humanity. In some areas of the world, water or environment is unsuitable for plant growth; hydroponic systems can provide a suitable environment for crop production with effective management of natural resources. Internet of Things paradigm based automated systems has been creating an excellent opportunity for monitoring and controlling agriculture by minimizing the cost and maximizing the profit significantly over the past decade. The reduction of the cost can be achieved by sufficient usage of resources and setting up optimum operational parameters for agricultural devices. This paper presents an optimization scheme with novel objective function for hydroponics environment parameters management with efficient energy consumption. The proposed approach provides optimal energy and resource utilization in the hydroponics system with setting up a working level and operational duration to the actuators. We have developed an optimization scheme with objective function for optimal humidity and water level control based on fuzzy logic, which can support the optimal measurement for crop growth with energy efficiency. Fuzzy logic control is applied for the compromise between actuators working level and operational duration. A real hydroponics environment has been implemented and presented to evaluate the effectiveness of the proposed approach. It can be assessed through the simulation results that the optimization module achieves a signification reduction (18%) in energy consumption as compared to the other scheme.

Double Fed Induction Generator Control Design Based on a Fuzzy Logic Controller for an Oscillating Water Column System

“…In this case, the rules were settled according to Table 1. The last step is the defuzzification where the results previously gathered through the logic of the controller are translated into numerical values [36]; these were uniformly discretized between −1 and 1. In accordance with the established linguistic rules and the output numerical values of the defuzzification, the obtained control surface is displayed in Figure 5.…”

Dynamic User Preference Parameters Selection and Energy Consumption Optimization for Smart Homes Using Deep Extreme Learning Machine and Bat Algorithm

“…The planning and identification of the best suitable locations to install sensors and gateways reduce the energy consumption in smart buildings [38]. The fuzzy logic controller based model has been successful in the hydroponics environment for the optimization of humidity and water level [39].…”

“…Equation (37) can be used to calculate the frequency for Bat (i). The values of xi and vi updates when the iterations proceed further, while for the calculation of new solutions, let's say x t i and velocities v t i at step t we can use (38) and (39) respectively. = + ( − ) (37)…”

Virtual Machine Resource Allocation Optimization in Cloud Computing Based on Multiobjective Genetic Algorithm

“…The status of these physical nodes may reappear in the near future, which will directly trigger a new round of dynamic resource configuration requirements [ 13 ]. To solve the above problems and to put forward a new thinking of physical nodes on the stability of the energy-saving resource scheduling method, how to find a virtual machine distribution that activates the stable state of each physical node and how to reduce the energy consumption of virtual machines from the old state to the new state are urgent and necessary [ 14 ].…”