Unlocking renewable energy potential: Harnessing machine learning and intelligent algorithms

International Journal of Renewable Energy Development, Journal Year: 2024, Volume and Issue: 13(4), P. 783 - 813

Published: June 7, 2024

This review article examines the revolutionary possibilities of machine learning (ML) and intelligent algorithms for enabling renewable energy, with an emphasis on energy domains solar, wind, biofuel, biomass. Critical problems such as data variability, system inefficiencies, predictive maintenance are addressed by integration ML in systems. Machine improves solar irradiance prediction accuracy maximizes photovoltaic performance sector. help to generate electricity more reliably enhancing wind speed forecasts turbine efficiency. efficiency biofuel production optimizing feedstock selection, process parameters, yield forecasts. Similarly, models biomass provide effective thermal conversion procedures real-time management, guaranteeing increased operational stability. Even enormous advantages, quality, interpretability models, computing requirements, current systems still remain. Resolving these issues calls interdisciplinary cooperation, developments computer technology, encouraging legislative frameworks. study emphasizes vital role promoting sustainable efficient giving a thorough present applications highlighting continuing problems, outlining future prospects

Language: Английский

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Long Short-Term Memory and Bidirectional Long Short-Term Memory Modeling and Prediction of Hexavalent and Total Chromium Removal Capacity Kinetics of Cupressus lusitanica Bark

Sustainability, Journal Year: 2024, Volume and Issue: 16(7), P. 2874 - 2874

Published: March 29, 2024

Hexavalent chromium [Cr(VI)] is a high-priority environmental pollutant because of its toxicity and potential to contaminate water sources. Biosorption, using low-cost biomaterials, an emerging technology for removing pollutants from water. In this study, Long Short-Term Memory (LSTM) bidirectional LSTM (Bi-LSTM) neural networks were used model predict the kinetics removal capacity Cr(VI) total [Cr(T)] Cupressus lusitanica bark (CLB) particles. The models developed 34 experimental datasets under various temperature, pH, particle size, initial concentration conditions. Data preprocessing via interpolation was implemented augment sparse time-series data. Early stopping regularization prevented overfitting, dropout techniques enhanced robustness. Bi-LSTM demonstrated superior performance compared models. inherent complexities process data limitations resulted in heavy-tailed left-skewed residual distribution, indicating occasional deviations predictions capacities obtained extreme K-fold cross-validation stability 38 43, while response surfaces validation with unseen assessed their predictive accuracy generalization capabilities. Shapley additive explanations analysis (SHAP) identified time as most influential input features This study highlights capabilities deep recurrent comprehending predicting complex kinetic phenomena applications.

Language: Английский

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Sustainability assessment of the agriculture sector using best worst method: Case study of Baltic states

Sustainable Development, Journal Year: 2024, Volume and Issue: 32(5), P. 5611 - 5626

Published: April 3, 2024

Abstract Sustainable agriculture development holds significant global and regional importance, particularly within the Baltic countries. On a scale, it is critical strategy for meeting escalating demand food while simultaneously mitigating adverse environmental social consequences associated with agricultural practices. In context of nations, where constitutes substantial portion economy, adoption sustainable farming practices imperative ensuring sector's long‐term viability, safeguarding integrity region's distinct ecosystems, guaranteeing security their populations. A comprehensive understanding opportunities challenges facing impeded by notable research deficiency concerning intricate problems these nations. The use indicators to assess economic plays pivotal role in guiding By taking variables into account, metrics quantify viability farming. Consequently, empower policymakers farmers alike make well‐informed decisions, striking balance between profitability resource conservation, thereby contributing enduring sustainability countries beyond. Notably, assessment identified 31 indicators, which were refined 9 through expert consensus using Delphi method. Subsequently, best worst method was applied rank indicators. results indicate that investment intensity, diversification income, labor productivity, market access emerge as most crucial agriculture.

Language: Английский

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Machine learning applications in forest and biomass supply chain management: a review

International Journal of Forest Engineering, Journal Year: 2024, Volume and Issue: 35(3), P. 371 - 380

Published: July 21, 2024

Language: Английский

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Model Forecasting of Hydrogen Yield and Lower Heating Value in Waste Mahua Wood Gasification with Machine Learning

International Journal of Energy Research, Journal Year: 2024, Volume and Issue: 2024(1)

Published: Jan. 1, 2024

Biomass is an excellent source of green energy with numerous benefits such as abundant availability, net carbon zero, and renewable nature. However, the conventional methods biomass combustion are polluting poor efficiency processes. gasification overcomes these challenges provides a sustainable method for supply greener fuel in form producer gas. The gas can be employed gaseous compression ignition engines dual‐fuel systems. process complex well nonlinear that highly dependent on ambient environment, type biomass, composition medium. This makes modeling systems quite difficult time‐consuming. Modern machine learning (ML) techniques offer use experimental data convenient approach to forecasting In present study, two modern efficient ML techniques, random forest (RF) AdaBoost, were this purpose. outcomes results baseline method, i.e., linear regression. RF could forecast hydrogen yield R 2 0.978 during model training 0.998 test phase. AdaBoost was close behind at 0.948 0.842 mean squared error low 0.17 0.181 testing, respectively. case heating value model, 0.971 respectively, Both provided compared regression, but RFt best among all three.

Language: Английский

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