An ab initio issues on renewable energy system integration to grid

Sarkar, Dipu; Odyuo, Yanrenthung

doi:10.5278/ijsepm.2802

Cited by 5 publications

(1 citation statement)

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“…Renewable energies, with solar power at the forefront, are essential to meeting the challenge of a sustainable energy supply [1,2]. However, the unpredictable nature of sunlight prevents the full potential of solar energy from being exploited [3,4]. To overcome this problem, Maximum Power Point Tracking (MPPT) technology and reliable weather forecasts optimize the PV system efficiency and energy production [5,6].…”

Section: Introductionmentioning

confidence: 99%

ANN for Temperature and Irradiation Prediction and Maximum Power Point Tracking Using MRP-SMC

Jlidi,

Barambones,

Hamidi

et al. 2024

Energies

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Currently, artificial intelligence (AI) is emerging as a dominant force in various technologies, owing to its unparalleled efficiency. Among the plethora of AI techniques available, neural networks (NNs) have garnered significant attention due to their adeptness in addressing diverse challenges, particularly for prediction tasks. This study offers a comprehensive review of predominant AI-based approaches to photovoltaic (PV) energy forecasting, with a particular emphasis on artificial neural networks (ANNs). We introduce a revolutionary methodology that amalgamates the predictive capabilities of ANN with the precision control afforded by the minimum-risk problem and sliding mode control (MRP-SMC), thereby revolutionizing the PV panel performance enhancement. Building upon this methodology, our hybrid approach utilizes the ANN as a proficient weather forecaster, accurately predicting the temperature and solar radiation levels impacting the panels. These forecasts serve as guiding principles for the MRP-SMC algorithm, enabling the proactive determination of the Maximum Power Point (MPP). Unlike conventional methods that grapple with weather unpredictability, the MRP-SMC algorithm transforms stochastic optimization challenges into controllable deterministic risk problems. Our method regulates the boost converter’s work cycle dynamically. This dynamic adaptation, guided by environmental predictions from ANNs, unlocks the full potential of PV panels, maximizing energy recovery efficiency. To train the model, we utilized a large dataset comprising 60,538 temperature and solar radiation readings from the Department of Systems Engineering and Automation at the Faculty of Engineering in Vitoria (University of the Basque Country). Our approach demonstrates a high regression coefficient (R = 0.99) and low mean square error (MSE = 0.0044), underscoring its exceptional ability to predict real energy values. In essence, this study proposes a potent fusion of artificial intelligence and control mechanisms that unleash the untapped potential of photovoltaic panels. By utilizing forecasts to guide the converter, we are paving the way for a future where solar energy shines brighter than ever.

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