Deep learning algorithms were used to generate photovoltaic renewable energy in saline water analysis via an oxidation process

Abstract

The amount of particles and organic matter in wash-waters and effluent from the processing of fruits and vegetables determines whether they need to be treated to fulfil regulatory standards for their intended use. This research proposes a novel technique in photovoltaic cell-based renewable energy in saline water analysis using the oxidation process and deep learning techniques. Here, the saline water oxidation is carried out based on photovoltaic cell-based renewable and saline water analysis is done using Markov fuzzy-based Q-radial function neural networks (MFQRFNN). The plan is entirely web-oriented to enable better control and effective monitoring of water consumption. This monitoring makes use of a communication system that collects data in the form of irregularly spaced time series. Experimental analysis has been carried out based on water salinity data in terms of accuracy, precision, recall, specificity, computational cost, and kappa coefficient. HIGHLIGHTS This research proposes a novel technique in photovoltaic cell-based renewable energy in saline water analysis using the oxidation process and deep learning techniques.; Forecasting energy demand is an essential component of PV that aids in the planning of power generation as well as energy trading with a commercial grid.; Deep learning-based models hold great promise for forecasting consumer demands and RES energy generation.;

Keywords

• deep learning
• oxidation process
• photovoltaic cell
• renewable energy
• saline water analysis