Neural Network Systems for Advanced Energy Harvesting in Microgrids

Abstract

Microgrids require sophisticated techniques for renewable energy management systems because they integrate more renewable sources into their networks. The study investigates neural network methods specifically hybrid CNN-LSTM models which help maximize energy collection in microgrids. The preprocessing methodology incorporates three key steps starting with energy data normalization followed by application of denoising filters for enhancing data quality and final execution of temporal dataset synchronization to improve reliability. The Recursive Feature Elimination method selects features from which RFE identifies key parameters affecting both energy output and utilization metrics. The CNN-LSTM combination uses convolutional layers to extract spatial characteristics while also leveraging long short-term memory units to detect temporal patterns within energy datasets. The developed system produces better forecasting precision alongside optimized system performance which leads to improved energy distribution and diminished energy loss. The developed solution provides scalable interpretation capabilities to manage microgrid energy systems for the advancement of sustainable efficient energy platforms.