Comparative analysis of MLP and CNN-LSTM models for solar power generation forecasting

• Main Authors: Jannah, Nurul, Abu Hanifah, Mohd Shahrin, Gunawan, Teddy Surya, Ahmad Zabidi, Suriza, Yusoff, Siti Hajar, Mohd Sapihie, Siti Nadiah
• Format: Proceeding Paper
• Language: English; English
• Published: IEEE 2023
• Subjects: TK7885 Computer engineering
• Online Access: http://irep.iium.edu.my/110167/1/110167_Comparative%20analysis%20of%20MLP%20and%20CNN-LSTM.pdf; http://irep.iium.edu.my/110167/7/110167_Comparative%20analysis%20of%20MLP%20and%20CNN-LSTM_SCOPUS.pdf; http://irep.iium.edu.my/110167/; https://ieeexplore.ieee.org/document/10373538

Solar energy, a cornerstone of renewable energy, for optimal grid integration and management, requires precise forecasting. Photovoltaic (PV) forecasting must be accurate to ensure energy stability and maximize resource utilization. This study compares Multi-Layer Perceptron (MLP) and Convolutional...