Intelligent Modeling and Optimization of Solar Plant Production Integration in the Smart Grid Using Machine Learning Models

Abubakar, Muhammad; Che, Yanbo; Faheem, Muhammad; Bhutta, Muhammad Shoaib; Mudasar, Abdul Qadeer

doi:10.1002/aesr.202300160

Cited by 5 publications

(1 citation statement)

References 46 publications

(45 reference statements)

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“…This poses health risks and intensifies the problem of climate change. To address these challenges, it's crucial to incorporate green energy sources like hydropower, wind, and solar power in the smart grid by using blockchain-based advanced information and communication technologies [ [4] , [5] , [6] ]. Table 1 highlights various characteristics of blockchain technology in the smart grid [ [7] , [8] , [9] ].…”

Section: Introductionmentioning

confidence: 99%

Multilayer cyberattacks identification and classification using machine learning in internet of blockchain (IoBC)-based energy networks

Faheem,

Al-Khasawneh

2024

Data in Brief

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Section: Introductionmentioning

confidence: 99%

Multilayer cyberattacks identification and classification using machine learning in internet of blockchain (IoBC)-based energy networks

Faheem,

Al-Khasawneh

2024

Data in Brief

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Conventional and artificial intelligence based maximum power point tracking techniques for efficient solar power generation

Khan,

Raza,

Faheem

et al. 2024

Engineering Reports

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The increasing global need for renewable energy sources, driven by environmental concerns and the limited availability of traditional energy, highlights the significance of solar energy. However, weather fluctuations challenge the efficiency of solar systems, making maximum power point tracking (MPPT) systems crucial for optimal energy harvesting. This study compares ten MPPT approaches, including both conventional and artificial intelligence (AI)‐based techniques. These controllers were designed and implemented using MATLAB Simulink, and their performance was evaluated under real environmental conditions with fluctuating irradiance and temperature. The results demonstrate that conventional techniques, such as incremental conductance (INC), Perturb and Observe (P&O), Incremental conductance and Particle Swam Optimization (INC‐PSO), Fuzzy Logic Control and Particle Swam Optimization (FLC‐PSO), and Perturb and Observe and Particle Swam Optimization (P&O‐PSO), achieved accuracies of 94%, 97.6%, 98.9%, 98.7%, and 99.3% respectively. In contrast, AI‐based intelligent techniques, including Artificial Neural Network (ANN), Artificial Neural Fuzzy Interference System (ANFIS), Fuzzy Logic Control (FLC), Particle Swam Optimization (PSO), and Artificial Neural Network and Particle Swam Optimization (ANN‐PSO), outperform achieving higher accuracies of 97.8%, 99.9%, 98.9%, 99.2%, and 99%, respectively. Compared to available research, which often reports lower accuracies for conventional techniques, our study highlights the enhanced performance of AI‐based methods. This study provides a comprehensive comparative analysis, delivering critical analysis and practical guidance for engineers and researchers in selecting the most effective MPPT controller optimized to specific environmental conditions. By improving the efficiency and reliability of solar power systems, our research supports the advancement of sustainable energy solutions.

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Optimizing solar power efficiency in smart grids using hybrid machine learning models for accurate energy generation prediction

Bhutta,

Li,

Abubakar

et al. 2024

Sci Rep

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The fourth energy revolution is characterized by the incorporation of renewable energy supplies into intelligent networks. As the world is shifting towards cleaner energy sources, there is a need for efficient and reliable methods to predict the output of renewable energy plants. Hybrid machine learning modified models are emerging as a promising solution for energy generation prediction. Renewable energy generation plants, such as solar, biogas, hydropower plants, wind farms, etc. are becoming increasingly popular due to their environmental benefits. However, their output can be highly variable and dependent on weather conditions, making integrating them into the existing energy grid challenging. Smart grids with artificial intelligent systems have the potential to solve this challenge by using real-time data to optimize energy production and distribution. Although by incorporating sensors, analytics, and automation, these grids can manage energy demand and supply more efficiently, reducing carbon emissions, increase energy security, and improve access to electricity in remote areas. However, this research aims to enhance the efficiency of solar power generation systems in a smart grid context using machine learning hybrid models such as Hybrid Convolutional-Recurrence Net (HCRN), Hybrid Convolutional-LSTM Net (HCLN), and Hybrid Convolutional-GRU Net (HCGRN). For this purpose, this study considers various parameters of a solar plant such as power production (MWh), irradiance or plane of array (POA), and performance ratio (PR). The HCLN model demonstrates superior accuracy with the RMSE values of 0.012027 for MWh, 0.013734 for POA and 0.003055 for PR, along with the lowest MAE values of 0.069523 for MWh, 0.082813 for POA, and 0.042815 for PR. The obtained results suggest that the proposed machine learning models can effectively enhance the efficiency of solar power generation systems by accurately predicting the required measurements.

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Intelligent Modeling and Optimization of Solar Plant Production Integration in the Smart Grid Using Machine Learning Models

Cited by 5 publications

References 46 publications

Multilayer cyberattacks identification and classification using machine learning in internet of blockchain (IoBC)-based energy networks

Multilayer cyberattacks identification and classification using machine learning in internet of blockchain (IoBC)-based energy networks

Conventional and artificial intelligence based maximum power point tracking techniques for efficient solar power generation

Optimizing solar power efficiency in smart grids using hybrid machine learning models for accurate energy generation prediction

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