Energy Management Prediction in Hybrid PV-Battery Systems Using Deep Learning Architecture

Abstract

On-grid predictive energy management using machine learning is presented in this paper. A photovoltaic array considered in this study is one of the kinds of a renewable sources of energy, where the battery bank acts as a technology for energy storage, in order to optimise energy exchange with the utility grid using logistic regression. The model of prediction can accurately estimate photovoltaic energy output and load one step ahead using a training technique. The optimization problem is constrained by the maximum amount of CO2 produced and the maximum amount of charge stored in a battery bank. The proposed model is tested on dynamic electricity costs. Compared with existing energy systems, the proposed strategy and prediction model can handle more than half of the annual load need.

An Improved Inverse Kinematics Solution for a Robot Arm Trajectory Using Multiple Adaptive Neuro-Fuzzy Inference Systems

“…Soft computing finds solutions to actual problems while Artificial intelligence aims to create intelligent systems. AI is well suited for use in resolving problems in robotic systems [8]. Adaptive Neuro-Fuzzy Inference Systems (ANFIS) are a widely used technique in self-computing because they combine the information processing capabilities of Fuzzy Inference Systems (FIS) with the learning capabilities of neural networks to solve systems and it considers an efficient method to model, predict, and control complex engineering systems.…”

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“…In Ref. [7], Refaai et al (2022) discussed grids based on LR to predict distributed energy resource powers. An RES with solar arrays, based on their presented design, had battery banks working as energy storage devices.…”

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