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

Jannah, Nurul and Abu Hanifah, Mohd Shahrin and Gunawan, Teddy Surya and Ahmad Zabidi, Suriza and Yusoff, Siti Hajar and Mohd Sapihie, Siti Nadiah (2024) Comparative analysis of MLP and CNN-LSTM models for solar power generation forecasting. In: IEEE 9th International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA 2023), 17th - 18th October 2023, Kuala Lumpur, Malaysia.

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Abstract

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 Neural Network-Long Short-Term Memory (CNN-LSTM) models for forecasting solar power generation. Both models were trained with 13 features using an open-source dataset from 10 PV sites in Hebei Province, China, spanning 300 days (2018-07-01 to 2019-06-13). The CNN-LSTM was configured with 50 epochs and particular hyperparameters. CNN-LSTM demonstrated superior performance, with Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) values of 0.088, 0.051, and 0.227 versus MLP's 0.260, 0.156, and 0.395. The findings demonstrate CNN-LSTM's potential for enhancing solar power forecasting and facilitating the management of renewable energy sources.

Item Type:	Proceeding Paper (Invited Papers)
Additional Information:	The first author is a research assistant. So, Teddy Gunawan (5588) is the corresponding author. External collaboration (industry): Petronas Research Sdn Bhd
Uncontrolled Keywords:	Solar power forecasting, Multi-Layer Perceptron, Convolutional Neural Network, Long Short-Term Memory, Photovoltaic dataset, renewable energy management
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices > TK7885 Computer engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User:	Prof. Dr. Teddy Surya Gunawan
Date Deposited:	15 Jan 2024 11:09
Last Modified:	23 Feb 2024 15:58
URI:	http://irep.iium.edu.my/id/eprint/110167

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