Potential of Explainable Artificial Intelligence in Advancing Renewable Energy: Challenges and Prospects

Energy & Fuels, Journal Year: 2024, Volume and Issue: 38(3), P. 1692 - 1712

Published: Jan. 19, 2024

Modern machine learning (ML) techniques are making inroads in every aspect of renewable energy for optimization and model prediction. The effective utilization ML the development scaling up systems needs a high degree accountability. However, most approaches currently use termed black box since their work is difficult to comprehend. Explainable artificial intelligence (XAI) an attractive option solve issue poor interoperability black-box methods. This review investigates relationship between (RE) XAI. It emphasizes potential advantages XAI improving performance efficacy RE systems. realized that although integration with has enormous alter how produced consumed, possible hazards barriers remain be overcome, particularly concerning transparency, accountability, fairness. Thus, extensive research required address societal ethical implications using create standardized data sets evaluation metrics. In summary, this paper shows potential, perspectives, opportunities, challenges application system management operation aiming target efficient energy-use goals more sustainable trustworthy future.

Language: Английский

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Accurate nowcasting of cloud cover at solar photovoltaic plants using geostationary satellite images

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: Jan. 13, 2024

Abstract Accurate nowcasting for cloud fraction is still intractable challenge stable solar photovoltaic electricity generation. By combining continuous radiance images measured by geostationary satellite and an advanced recurrent neural network, we develop a algorithm predicting at the leading time of 0–4 h plants. Based on this algorithm, cyclically updated prediction system also established tested five plants several stations with observations in China. The results demonstrate that efficient, high quality adaptable. Particularly, it shows excellent forecast performance within first 2-hour time, average correlation coefficient close to 0.8 between predicted clear sky ratio actual power generation Our findings highlight benefits potential technique improve competitiveness energy market.

Language: Английский

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A century-long analysis of global warming and earth temperature using a random walk with drift approach

Decision Analytics Journal, Journal Year: 2023, Volume and Issue: 7, P. 100237 - 100237

Published: April 28, 2023

Climate change poses the most significant threat to humanity today. This study examines global warming trend by analyzing temperature changes over past century, uncovering alarming results. Various models, including Random Walk with Drift approach R programming language, have been used compare different time horizons and scenarios. research demonstrates importance of utilizing advanced analytical techniques better understand climate change's impact. The findings underscore urgency implementing effective policies mitigate effects safeguard our planet's future.

Language: Английский

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Prediction, modelling, and forecasting of PM and AQI using hybrid machine learning

Journal of Cleaner Production, Journal Year: 2023, Volume and Issue: 421, P. 138496 - 138496

Published: Aug. 17, 2023

Language: Английский

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Comparative analysis of single and hybrid machine learning models for daily solar radiation

Energy Reports, Journal Year: 2024, Volume and Issue: 11, P. 3256 - 3266

Published: March 11, 2024

This study investigates the estimation of daily solar radiation (SR) through various machine learning (ML) models, including k-nearest neighbor algorithm (KNN), support vector regression (SVR), and random forest (RF), both individually in combination with wavelet transform (WT). The assessment these models is based on meteorological data spanning three decades (1981–2010) from province Kütahya Türkiye. To address inherent uncertainty data-driven quantile method employed for analysis. Statistical metrics, such as mean absolute error (MAE), root square (RMSE), coefficient determination (R2), prediction interval (MPI), coverage probability (PICP), are utilized to evaluate effectiveness uncertainties models. SVR KNN exhibit comparable performances concerning predictive accuracy levels. However, hybrid KNN-WT, RF-WT, SVR-WT, display enhanced compared individual ML indicated by statistical performance criteria. Notably, SVR-WT model, incorporating inputs sunshine duration, air temperature, wind speed, relative humidity, outperforms other terms RMSE (2.174 MJ/m2), MAE (1.721 R2 (0.923), MPI (28.55), PICP (0.80) testing dataset. In conclusion, integration WT significantly improves providing valuable insights design operation energy systems, where precise SR critical optimal cost-efficient operation.

Language: Английский

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Solar irradiation prediction using empirical and artificial intelligence methods: A comparative review

Heliyon, Journal Year: 2023, Volume and Issue: 9(6), P. e17038 - e17038

Published: June 1, 2023

Solar irradiation data is essential for the feasibility of solar energy projects. Notably, intermittent nature influences use in all forms, whether or agriculture. Accurate prediction only solution to effectively different forms. The estimation most critical factor site selection and sizing projects selecting a suitable crop area. But physical measurement irradiation, due cost technology involved, not possible locations across globe. Numerous techniques have been implemented predict this purpose. two types approaches that are frequently employed empirical artificial intelligence (AI). Both demonstrated good accuracy various places world. To find out best method, thorough review research articles discussing has done compare methods prediction. In paper, predicting using AI published from 2017 2022 reviewed, both compared. showed more accurate than methods. models, modified sunshine-based models (MSSM) highest accuracy, followed by (SSM) non-sunshine-based (NSM). NSM little lower MSSM SSM, but can give results sunshine unavailability. Also, literature confirmed simple could accurately, increasing model's polynomial order cannot improve results. Artificial neural networks (ANN) Hybrid among methods, support vector machine (SVM) adaptive neuro-fuzzy inference system (ANFIS). increase efficiency hybrid minimal, complexity requires very sophisticated programming knowledge. ANN's important input factors maximum minimum temperatures, temperature differential, relative humidity, clearness index precipitation.

Language: Английский

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Integrated AI-driven optimization of Fenton process for the treatment of antibiotic sulfamethoxazole: Insights into mechanistic approach

Chemosphere, Journal Year: 2024, Volume and Issue: 357, P. 141868 - 141868

Published: April 7, 2024

Language: Английский

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Metaheuristic Algorithms for Solar Radiation Prediction: A Systematic Analysis

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 100134 - 100151

Published: Jan. 1, 2024

In the contemporary world, where escalating demand for energy and imperative sustainable sources, notably solar energy, have taken precedence, investigation into radiation (SR) has become indispensable. Characterized by its intermittency volatility, SR may experience considerable fluctuations, exerting a significant influence on supply security. Consequently, precise prediction of imperative, particularly in context potential proliferation photovoltaic panels need optimized management. Several works existing literature review state art prediction, focusing trends identified using machine learning (ML) or deep (DL) techniques. However, there is gap regarding integration optimization algorithms with ML DL techniques prediction. This systematic addresses this studying models that leverage metaheuristic alongside artificial intelligence (AI) techniques, aiming primarily maximum accuracy. Metaheuristic such as Particle Swarm Optimization (PSO) Genetic Algorithm (GA) featured 29% 12.1% analyzed articles, respectively, while intelligent approaches like Convolutional Neural Networks (CNN), Extreme Learning Machine (ELM), Multilayer Perceptron (MLP) emerged predominant choices, collectively accounting 43.9% studies. Analysis encompassed studies examining across hourly, daily, monthly intervals, daily intervals representing 48.7% focus. Noteworthy variables including temperature, humidity, wind speed, atmospheric pressure surfaced, capturing proportions 90%, 68.2%, 56%, 41.4%, within reviewed literature.

Language: Английский

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New models of solar photovoltaic power generation efficiency based on spectrally responsive bands

Applied Energy, Journal Year: 2024, Volume and Issue: 375, P. 123936 - 123936

Published: Aug. 12, 2024

Language: Английский

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Forecasting ground-level ozone and fine particulate matter concentrations at Craiova city using a meta-hybrid deep learning model

Urban Climate, Journal Year: 2024, Volume and Issue: 57, P. 102099 - 102099

Published: Aug. 16, 2024

Language: Английский

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