A Hybrid Deep Learning‐Based Power Management Strategy for PV‐Assisted Desalination Plant

Abstract

The increasing global need for freshwater has led to the widespread implementation of photovoltaic (PV) assisted desalination facilities as a viable and environmentally friendly remedy. The necessity for these plants resides in executing an effective power management strategy to provide dependable and economically feasible water generation. This paper utilizes a mixed architecture consisting of a convolutional neural network (CNN) and a deep Q‐learning Network (DQN) to implement a hybrid deep learning‐based power management strategy. The presented approach is modeled and executed in matrix laboratory (MATLAB) language, and the experimental findings validated that this algorithm achieved a computational time of 0.2 s and an energy loss of 0.01 megawatts, which is lower than the conventional models. Furthermore, the proposed strategy achieved a remarkable fit with an accuracy rate of 0.99, demonstrating its effectiveness in handling diverse load power and solar profiles in power management. Implementing this hybrid approach holds promise for a substantial reduction in operational expenditures and the advancement of sustainability in freshwater generation, making a valuable contribution toward a more environmentally friendly and optimized future for the desalination sector.