Machine learning and the renewable energy revolution: Exploring solar and wind energy solutions for a sustainable future including innovations in energy storage

Bin Abu Sofian, Abu Danish Aiman; Lim, Hooi Ren; Siti Halimatul Munawaroh, Heli; Ma, Zengling; Chew, Kit Wayne; Show, Pau Loke

doi:10.1002/sd.2885

Cited by 14 publications

(6 citation statements)

References 150 publications

(200 reference statements)

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“…In the case of solar energy, using soft computing provides several obstacles, including unpredictability, optimization, integrating, and cybersecurity. However, it also presents several significant potentials for innovation, development, and sustainability [263], [264]. Soft computing methods can play a revolutionary role in realizing the full capability of solar energy as a clean, plentiful, and sustainable energy source for the future.…”

Section: Resultsmentioning

confidence: 99%

Artificial intelligence applications in solar energy

Le,

et al. 2024

JOIV : Int. J. Inform. Visualization

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Renewable energy research has become significant in the modern period owing to escalating prices of fossil fuels and the pressing need to reduce greenhouse gas emissions. Solar energy stands out among these sources due to its abundance and global accessibility. However, its weather-dependent and cyclical nature add inherent risks, making effective planning and management difficult. Soft computing technologies provide attractive solutions for modeling such systems, while machine learning and optimization techniques are gaining popularity in the solar energy industry. The current literature highlights the growing use of soft computing technologies, emphasizing their potential to address difficult challenges in solar energy systems. To effectively reap the benefits, these strategies must be seamlessly connected with emerging technologies like the Internet of Things (IoT), big data analytics, and cloud computing. This integration provides a unique opportunity to improve the scalability, flexibility, and efficiency of solar energy systems. Researchers can use these synergies to create intelligent, linked solar energy ecosystems capable of real-time optimization of energy production, delivery, and consumption. These technologies have the potential to transform the renewable energy environment, allowing for more resilient and sustainable energy infrastructures. Furthermore, as these technologies improve, there is a growing demand for trained experts to address associated cybersecurity problems, assuring the integrity and security of these sophisticated systems. Researchers may pave the road for a more sustainable and energy-efficient future by working collaboratively and using interdisciplinary methodologies.

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Section: Resultsmentioning

confidence: 99%

Artificial intelligence applications in solar energy

Le,

et al. 2024

JOIV : Int. J. Inform. Visualization

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“…As depicted in the flow diagram of Figure 4, the proposed UBO-EREX architecture involves the utilization of both the ELM network architecture and REX. In this work, ELM is trained by generating random input weights and biases (a, b) for the hidden layer H, which is then activated by an activation function σ for specific inputs x as in (1).…”

Section: Ubo-erexmentioning

confidence: 99%

“…Wind turbines play a pivotal role in the renewable energy landscape, offering a sustainable solution to power generation [1,2]. However, ensuring the reliable operation of wind turbines is crucial for maximizing energy output and minimizing maintenance costs [3,4].…”

Section: Introductionmentioning

confidence: 99%

UBO-EREX: Uncertainty Bayesian-Optimized Extreme Recurrent EXpansion for Degradation Assessment of Wind Turbine Bearings

Berghout,

Benbouzid

2024

Electronics

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Maintenance planning is crucial for efficient operation of wind turbines, particularly in harsh conditions where degradation of critical components, such as bearings, can lead to costly downtimes and safety threats. In this context, prognostics of degradation play a vital role, enabling timely interventions to prevent failures and optimize maintenance schedules. Learning systems-based vibration analysis of bearings stands out as one of the primary methods for assessing wind turbine health. However, data complexity and challenging conditions pose significant challenges to accurate degradation assessment. This paper proposes a novel approach, Uncertainty Bayesian-Optimized Extreme Recurrent EXpansion (UBO-EREX), which combines Extreme Learning Machines (ELM), a lightweight neural network, with Recurrent Expansion algorithms, a recently advanced representation learning technique. The UBO-EREX algorithm leverages Bayesian optimization to optimize its parameters, targeting uncertainty as an objective function to be minimized. We conducted a comprehensive study comparing UBO-EREX with basic ELM and a set of time-series adaptive deep learners, all optimized using Bayesian optimization with prediction errors as the main objective. Our results demonstrate the superior performance of UBO-EREX in terms of approximation and generalization. Specifically, UBO-EREX shows improvements of approximately 5.1460 ± 2.1338% in the coefficient of determination of generalization over deep learners and 5.7056% over ELM, respectively. Moreover, the objective search time is significantly reduced with UBO-EREX with 99.7884 ± 0.2404% over deep learners, highlighting its effectiveness in real-time degradation assessment of wind turbine bearings. Overall, our findings underscore the significance of incorporating uncertainty-aware UBO-EREX in predictive maintenance strategies for wind turbines, offering enhanced accuracy, efficiency, and robustness in degradation assessment.

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“…Among them, solar energy is a promising, cost-effective alternative. 8 In this case, numerous solar energy conversion and storage systems have been developed, 9 including supercapacitors 10,11 connected to solar cells 12,13 and rechargeable (secondary) batteries, such as lithium-ion, lithium-oxygen, lithium-sulfur, zinc-ion, and redox-flow batteries. 14,15 Additionally, photovoltaic cells (such as silicon, dyesensitized, and perovskite solar cells) and external electrochemical components are combined, 16 where the latter externally stores electricity in the form of chemical energy, while the former acts as an energy collector.…”

Section: Introductionmentioning

confidence: 99%