Solar photovoltaic (PV) technology has become a major sustainable energy option in response to the increasing need for renewable energy. Nations are aiming for less fossil fuels, and promoting global solar PV capacity that was significantly expand to 710 GW in 2020. Some environmental factors which might render solar energy unusable include mud, trees and buildings. Alongside, hotspots, electrical imbalance and the possibility of damage to the PV modules by thermal reasons which reduces power output, especially partial and total shadowing will yield a power loss of 40–50%. These issues can be resolved through the advanced machine learning (ML) techniques which help to detect the defect on PV panel automatically and reduce downtime, and improve energy production reliability. In this work, an accurate and efficient classifier for PV defects using normal and shading condition based on advanced ML techniques like Random Forest (RF), K-Nearest Neighbors (KNN), Decision Tree (DT), Logistic Regression (LG), eXtreme Gradient Boosting (XGBoost) and Support Vector Machines (SVM) is proposed. A dataset including characteristics of voltage, current, power and irradiance is used to test the classification accuracy and the computational efficiency of these algorithms. The results suggested that, RF is the top algorithm with a classification accuracy of 99.7%. KNN, DT, XGBoost, and SVM are next in line with 99% classification accuracy but LR had the lowest performance of 95%. This study suggested PV monitoring systems with lower maintenance costs and energy losses.
Aafaque Ali, M. A. Raza, M. Altayeb et al.· Discover Sustainability· 0 citations
The production of green hydrogen through water splitting requires highly efficient electrocatalysts, but the current trial-and-error-based synthesis or discovery is time-consuming, costly and resource-intensive. Machine learning (ML) provides a powerful, data-driven alternative that can model complex structure-activity relationships across large chemical spaces at orders-of-magnitude speed. This review systematically overviews the life cycle of the electrocatalyst research and development application of ML. First, the thermodynamic and kinetic principles of the hydrogen and oxygen evolution reactions are summarised, along with some well-adopted and accepted activity descriptors. Then we explore data sources, featurization approaches, and algorithms, and discuss the model space, from a simple interpretable model to a graph neural network to a generative model, in the context of the ML toolkit. Strategic applications are discussed for high-throughput virtual screening of alloys and single-atom catalysts, as well as multifunctional activity prediction for overall water splitting, and stability optimisation under operating conditions. The topic of emerging frontiers is highlighted, including high-entropy alloys, amorphous materials, and linking atomic-scale understanding to device-level performance through integration with density functional theory. Finally, the problems of data scarcity, model interpretability and the discrepancy between computational predictions and industrial implementation are discussed, along with future directions for closed-loop discovery and self-driving laboratories. Incorporating ML into electrocatalyst design and combining it with autonomous experimentation will revolutionise this process, from simulation to energy solution, dramatically speeding it up.
Vamsi Krishna Kudapa, Shoaib Mohd, Vijayakumar Sivasundar et al.· Frontiers in Chemistry· 0 citations
