Machine learning and analytical model hybridization to assess the impact of climate change on solar PV energy production

Nwokolo, Samuel Chukwujindu; Obiwulu, Anthony Umunnakwe; Ogbulezie, Julie C.

doi:10.1016/j.pce.2023.103389

Cited by 38 publications

(16 citation statements)

References 51 publications

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“…Furthermore, AI can predict and forecast energy demand, allowing for better solar energy integration into the power grid. 111,112 Nwokolo et al 113 employed six different techniques for performance prediction of several solar PV systems in Australia, using a radial basis function (RBF), MLP-ANN, switched and controlled auto regression integrated moving averages (SARIMA and CARIMA), and boosting and bagging ensemble ML models. The suggested hybrid model, which used only the quantifiable parameter of solar radiation, is most comparable to all systems' recorded PV energy output, with a comprehensive R 2 of 0.9998% and an RMSE of 0.0063 kWh.…”

Section: Applications Of Ai In Renewablementioning

confidence: 99%

“…A study conducted based on AI can also aid in detecting maintenance issues such as faulty panels or connections, thus allowing for quick repairs and preventing energy loss. Furthermore, AI can predict and forecast energy demand, allowing for better solar energy integration into the power grid. , Nwokolo et al . employed six different techniques for performance prediction of several solar PV systems in Australia, using a radial basis function (RBF), MLP-ANN, switched and controlled auto regression integrated moving averages (SARIMA and CARIMA), and boosting and bagging ensemble ML models.…”

Section: Introduction To Ai and Its Applications In Renewable Energymentioning

confidence: 99%

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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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Section: Applications Of Ai In Renewablementioning

confidence: 99%

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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“…Even, if global warming is kept within 2oC relative to the pre-industrial levels, the negative effects will be experienced also due to some other intervening factors, and the world needs to make such strategies and policies to overcome the negative implications on the environment. As studies have Nwokolo, Obiwulu & Ogbulezie (2023); Osman et al (2023); Zahra et al (2023); Mustafa (2011) found that climate change promotes urbanization and increases the population ratio, which leads to environmental challenges in addition to food security, or general security issues in society (Ashraf Hussain et al, 2022). On the other hand, the UN has also predicted that in the year 2025, an increase in the global population is likely from 7.2 billion to 81 billion, a huge burden on the environment and vulnerable population.…”

Section: Environmental Impact Of Climate Changementioning

confidence: 99%

Impact of Climate Change on Pakistan and Proposed Solutions: Evidence from Literature

Sheer,

Fatima,

Sharif

2023

Migrat. Lett.

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The current research examines the impact of climate change on Pakistan (social, economic & environmental) and proposed solutions from existing literature. For this purpose, the qualitative research design was adopted, and literature has been taken from the last ten years' papers, articles, reports, and books. However, for the analysis of impacts, the most recent year-2023 literature has been taken from international journals. The result was divided into a total of four themes: a) social, b) economic, c) environmental impacts, and d) mitigation strategies for climate change in Pakistan. Results and findings showed that climate change has negatively affected the social, economic, and environmental well-being of the people of Pakistan, especially, people who are associated with agriculture and livestock. Frequent droughts, urban flooding, land-sliding, cyclones, earth-quick, etc., not only disturb social life but also have several negative economic implications such as decreases the per capita income, loss of Gross Domestic Products (GDP) and food security and safety. Though, Pakistan, being a developing country, has signed many agreements with international organizations like United Nations (UN), World Bank (WB), etc to overcome the negative effects of climate change. Despite strategies and efforts, still Pakistan has failed to achieve its targets due to a lack of funds, political disinterest, lack of awareness among common people about climate change, and administrative inefficiencies as well. The political and administrative seriousness about climate change can make the possible outcomes to overcome the inverse implications of climate change in Pakistan.

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“…Hassan et al [41] have proposed a new regression and ensemble-learning model for forecasting the performance of PV energy plants operating in desert regions, taking into account the innovative tools of the photovoltaic system and the character traits of the procedure settings and climatic conditions. Nwokolo et al [42] developed and validated 294 physical models from six different PV power technologies using machine learning, Gumbel's probabilistic approach and hybridization of the two to aid in the possible determination of PV electric energy generation in the unique geographical and climatic environment of the experiment site.…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of recent metaheuristic algorithms for maximum power point tracking of solar photovoltaic systems under partial shading conditions

Ravi

Premkumar

Abualigah

2023

IJAPE

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<span lang="EN-US">The photovoltaic (PV) system comprises one or more solar panels, a converter/inverter, controllers, and other mechanical and electrical elements that utilize the generated electrical energy by the PV modules. The PV systems are ranged from small roofs or transportable units to massive electric utility plants. The maximum power point tracking (MPPT) controller has been used in PV systems to get the maximum power available. In addition, the MPPT controller is much essential for PV systems to protect the battery devices or direct loads from the power fluctuations received from solar PV panels. There are several MPPT control mechanisms available right now. The most common and commonly applied approaches under constant irradiance are perturb and observe (P&O) and incremental conductance (INC). But such methods show variations in the maximum power point. In this sense, this paper analyses and utilizes two recent metaheuristic algorithms called artificial rabbit optimization (ARO) and the most valuable player (MVP) algorithm for MPPT applications. The performance comparisons are made with the most preferred traditional algorithms, such as P&O and INC. Based on the result obtained, this study recommends that ARO perform better in standard testing conditions than all the other algorithms, but in partially shaded conditions, the MVP algorithm performs better in terms of efficiency and tracking speed.</span>

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Machine learning and analytical model hybridization to assess the impact of climate change on solar PV energy production

Cited by 38 publications

References 51 publications

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

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

Impact of Climate Change on Pakistan and Proposed Solutions: Evidence from Literature

Comparative analysis of recent metaheuristic algorithms for maximum power point tracking of solar photovoltaic systems under partial shading conditions

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