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 7 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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Forecasting Shifts in Europe's Renewable and Fossil Fuel Markets Using Deep Learning Methods

Liu,

Saleem,

Rashid

et al. 2024

Energy Science & Engineering

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Accurate forecasts of renewable and nonrenewable energy output are essential for meeting global energy needs and resolving environmental issues. Energy sources like the sun and wind are variable, making forecasting difficult. Changes in weather, demand, and energy policy exacerbate this unpredictability. These challenges will be addressed by the bidirectional gated recurrent unit (Bi‐GRU) model, which forecasts power‐generating outcomes more efficiently. The investigation is done over a health data set from 2000 to 2023, including the energy states of the United Kingdom, Finland, Germany, and Switzerland. The comparison of our model (Bi‐GRU) performance with other popular models, including bidirectional long short‐term memory (Bi‐LSTM), ensemble techniques combining convolutional neural networks (CNN) and Bi‐LSTM, and CNNs, make the study more interesting. The performance remains better with a mean absolute percentage error (MAPE) of 2.75%, root mean square error (RMSE) of 0.0414, mean squared error (MSE) of 0.0017, and authentify that Bi‐GRU performs much better than others. This model's superior prediction accuracy significantly enhances our ability to forecast renewable and nonrenewable energy outputs in European states, contributing to more effective energy management strategies.

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

Cited by 7 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

Forecasting Shifts in Europe's Renewable and Fossil Fuel Markets Using Deep Learning Methods

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