Output Power Forecasting of Solar Photovoltaic System Using Meta-cognitive Neuro Fuzzy Inference System
Forecasting of solar power is in general significant for planning the operations of power plants which convert renewable energies into electricity. Prediction of solar power shows some uncertainties depending on atmospheric parameter such as solar irradiance, atmospheric temperature and relative humidity. This article illustrates Artificial Neural Network (ANN) strategy that is, Meta-cognitive Neuro Fuzzy Inference System (McNFIS) which is used to forecast photovoltaic power from the historical data set collected from different photovoltaic panel located in different location. ANN have some interesting properties that made machine learning very appealing when confronting difficult pattern discovery tasks. ANN has the ability of learning, it can learn from the historic data set and it can predict the future samples. McNFIS self-regulatory learning mechanism that controls the learning process of the cognitive component, by deciding what-to-learn, when-to-learn and how-to-learn from sequential training data, forms the meta-cognitive component. McNFIS realizes the above decision by employing sample deletion, sample reserve and sample learning strategy, respectively. Thus, in this article training and testing are performed by ANN technique- McNFIS. The accuracy of prediction can be described by using various error measurement criteria like Normalised Mean Absolute Error, Weighted Mean Absolute Error, normalised Root Mean Square Error. Finally the performance of neural network is t
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