Weather Impact on Solar Farm Performance: A Comparative Analysis of Machine Learning Techniques

Gopi, Ajith; Sharma, Prabhakar; Sudhakar, K.; Ngui, Wai Keng; Kirpichnikova, Irina; Cüce, Erdem

doi:10.3390/su15010439

Cited by 57 publications

(22 citation statements)

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“…In this equation, the constant σ of Stefan Boltzmann and T sky represents the sky's temperature, which is obtained according to Equation (18). 43 T T = 0.05532 × sky amb 1.5 (18) After calculating useful heat according to Equation ( 6), the amount of wasted heat is obtained from Equation (19).…”

Section: Symbolmentioning

confidence: 99%

“…Considering the emphasis of the world community on the importance of areas such as sustainable development goals or net zero, the development methods of the above items in the field of renewable energy should be evaluated. One of the tools that can play a significant role in this field is the machine learning method 19 . Therefore, this research is focused on the recent advances in machine learning applied to heat transfer with nanoparticles in renewable energy systems 20 …”

Section: Introductionmentioning

confidence: 99%

“…One of the tools that can play a significant role in this field is the machine learning method. 19 Therefore, this research is focused on the recent advances in machine learning applied to heat transfer with nanoparticles in renewable energy systems. 20 One of the most important areas in the analysis of nanofluids is the stability of the composition and the absence of physical and chemical reactions between the nanoparticles.…”

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confidence: 99%

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Evaluating the effect of using nanofluids on the parabolic trough collector's performance

Moosavian,

Hajinezhad,

Fattahi

et al. 2023

Energy Science & Engineering

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Solar collectors utilize solar energy, a sustainable and renewable source, to cater to both household and industrial needs. This paper investigated the usage of mono and binary nanoparticles in parabolic trough collectors. This paper used multiwalled carbon nanotube/Fe3O4 nanoparticles as the additives to the collector heat transfer fluid (in Syltherm 800 oil) and also evaluated the performance implications of energy systems in six scenarios. A MATLAB‐based computational model is provided to investigate the influence of employing binary nanoparticles on a solar collector's performance. The influence of nanoparticle modifications and volume percentages on energy, exergy, economic, and environmental aspects is studied. Finally, the impact of nanoparticles on each of the four mentioned areas is examined. At the same total energy rate, in the best case, adding 4% by volume of Fe3O4 (Case 2) to the base fluid increases the valuable energy rate absorbed from 7.32 kW in the base state to 7.50 kW (2.5%). The carbon dioxide produced in the system life cycle by using useful energy is called EnergoEnvironmental, and the carbon dioxide produced in the system life cycle by using useful exergy is called ExergoEnvironmental. At last, the calculation of the parameters EnergoEnviroEconomic and ExergoEnviroEconomic are done, the penalty policy is considered, and the calculations are generalized to solar collector power plants due to the carbon dioxide cost policies.

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Section: Symbolmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Evaluating the effect of using nanofluids on the parabolic trough collector's performance

Moosavian,

Hajinezhad,

Fattahi

et al. 2023

Energy Science & Engineering

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“…ML models may also be employed to enhance the efficiency of RE systems, anticipate energy output, and optimize energy storage. For instance, an ML algorithm can be used to analyze data on the efficiency of solar panels and wind turbines to detect areas for improvement. − As a result, more efficient and cost-effective RE solutions may emerge. However, despite the numerous advantages of ML in RE research, several potential downsides must be considered.…”

Section: Introductionmentioning

confidence: 99%

“…Storage of solar energy is essential to supply energy when the sun does not shine. AI models can optimize energy storage by analyzing data from energy usage patterns and weather forecasts to predict when energy storage systems need to be charged and discharged, and can also help in optimizing the sizes of these energy storage systems, thus lowering costs while ensuring adequate energy storage. , Khan et al proposed an enhanced, generally applicable stacked ensemble approach (DSE-XGB) for solar energy forecasting based on two deep learning techniques, long short-term memory (LSTM) and an ANN. The suggested model was tested on four different solar generation data sets to enable a full assessment.…”

Section: Introduction To Ai and Its Applications In Renewable Energymentioning

confidence: 99%

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

Nguyen,

Tarełko,

Sharma

et al. 2024

Energy Fuels

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

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Machine learning for the management of biochar yield and properties of biomass sources for sustainable energy

Nguyen,

Sharma,

Ağbulut

et al. 2024

Biofuels Bioprod Bioref

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Biochar is emerging as a potential solution for biomass conversion to meet the ever increasing demand for sustainable energy. Efficient management systems are needed in order to exploit fully the potential of biochar. Modern machine learning (ML) techniques, and in particular ensemble approaches and explainable AI methods, are valuable for forecasting the properties and efficiency of biochar properly. Machine‐learning‐based forecasts, optimization, and feature selection are critical for improving biomass management techniques. In this research, we explore the influences of these techniques on the accurate forecasting of biochar yield and properties for a range of biomass sources. We emphasize the importance of the interpretability of a model, as this improves human comprehension and trust in ML predictions. Sensitivity analysis is shown to be an effective technique for finding crucial biomass characteristics that influence the synthesis of biochar. Precision prognostics have far‐reaching ramifications, influencing industries such as biomass logistics, conversion technologies, and the successful use of biomass as renewable energy. These advances can make a substantial contribution to a greener future and can encourage the development of a circular biobased economy. This work emphasizes the importance of using sophisticated data‐driven methodologies such as ML in biochar synthesis, to usher in ecologically friendly energy solutions. These breakthroughs hold the key to a more sustainable and environmentally friendly future.

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Weather Impact on Solar Farm Performance: A Comparative Analysis of Machine Learning Techniques

Cited by 57 publications

References 58 publications

Evaluating the effect of using nanofluids on the parabolic trough collector's performance

Evaluating the effect of using nanofluids on the parabolic trough collector's performance

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

Machine learning for the management of biochar yield and properties of biomass sources for sustainable energy

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