Journal
ENERGIES
Volume 13, Issue 23, Pages -Publisher
MDPI
DOI: 10.3390/en13236405
Keywords
dynamic learning; advanced particle swarm optimization; jaya algorithm; fine-tuning metaheuristic algorithm; renewable energy power forecasting; spearman’ s rank-order correlation; artificial neural network
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Funding
- Korea Electric Power Corporation [R18XA01]
- Energy Cloud R&D Program through the National Research Foundation of Korea (NRF) - Ministry of Science, ICT [2019M3F2A1073]
- National Research Foundation of Korea [5199991014250] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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A reasonable dataset, which is an essential factor of renewable energy forecasting model development, sometimes is not directly available. Waiting for a substantial amount of training data creates a delay for a model to participate in the electricity market. Also, inappropriate selection of dataset size may lead to inaccurate modeling. Besides, in a multivariate environment, the impact of different variables on the output is often neglected or not adequately addressed. Therefore, in this work, a novel Mode Adaptive Artificial Neural Network (MAANN) algorithm has been proposed using Spearman's rank-order correlation, Artificial Neural Network (ANN), and population-based algorithms for the dynamic learning of renewable energy sources power generation forecasting model. The proposed algorithm has been trained and compared with three population-based algorithms: Advanced Particle Swarm Optimization (APSO), Jaya Algorithm, and Fine-Tuning Metaheuristic Algorithm (FTMA). Also, the gradient descent algorithm is considered as a base case for comparing with the population-based algorithms. The proposed algorithm has been applied in predicting the power output of a Solar Photovoltaic (PV) and Wind Turbine Energy System (WTES). Using the proposed methodology with FTMA, the error was reduced by 71.261% and 80.514% compared to the conventional fixed-sized dataset gradient descent-based training approach for Solar PV and WTES, respectively.
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