Abstract
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.
Highlights
The application of Machine Learning (ML) has broken the barrier of correctly predicting different physical systems
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 Fine-Tuning Metaheuristic Algorithm (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 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
Summary
The application of Machine Learning (ML) has broken the barrier of correctly predicting different physical systems. Reviewed literature for solar PV systems can be broadly categorized according to the application of the variants of the neural network and hybrid methods. Levenberg-Marquardt (LM), can be found in References [1,2,3,4,5,6,7,8,9] In this category, the application of advanced neural network models, that is, Deep Belief Network (DBN), Autoencoder [10], Long Short-Term Memory (LSTM) [10,11,12], and Adaptive Neuro-Fuzzy Inference System (ANFIS) [13], was found. Authors have proposed a generalized ensemble model integrating Deep Learning (DL) techniques for solar power forecasting in Reference [20]. Regression (LSSVR), M5 Regression Tree (M5RT) [27], and Support Vector Machine (SVM) [37], etc
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