Fuzzy-based prediction of solar PV and wind power generation for microgrid modeling using particle swarm optimization

Teferra, Demsew Mitiku; Ngoo, Livingstone; Nyakoe, George N.

doi:10.1016/j.heliyon.2023.e12802

Cited by 23 publications

(10 citation statements)

References 18 publications

(19 reference statements)

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“…Lastly, in [23], a blended Fuzzy-PSO smart forecasting method is deployed, and its precision is documented and contrasted with Fuzzy and Fuzzy-GA prediction models. The authors underline the advantages of the Fuzzy-PSO smart forecasting method, which include the ability to combine the strengths of both fuzzy logic and Particle Swarm Optimization (PSO), potentially leading to improved accuracy and robustness in prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Learning and Weather Model Combination for PV Production Forecasting

Buonanno,

Caputo,

Balog

et al. 2024

Energies

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Accurate predictions of photovoltaic generation are essential for effectively managing power system resources, particularly in the face of high variability in solar radiation. This is especially crucial in microgrids and grids, where the proper operation of generation, load, and storage resources is necessary to avoid grid imbalance conditions. Therefore, the availability of reliable prediction models is of utmost importance. Authors address this issue investigating the potential benefits of a machine learning approach in combination with photovoltaic power forecasts generated using weather models. Several machine learning methods have been tested for the combined approach (linear model, Long Short-Term Memory, eXtreme Gradient Boosting, and the Light Gradient Boosting Machine). Among them, the linear models were demonstrated to be the most effective with at least an RMSE improvement of 3.7% in photovoltaic production forecasting, with respect to two numerical weather prediction based baseline methods. The conducted analysis shows how machine learning models can be used to refine the prediction of an already established PV generation forecast model and highlights the efficacy of linear models, even in a low-data regime as in the case of recently established plants.

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Section: Literature Reviewmentioning

confidence: 99%

Machine Learning and Weather Model Combination for PV Production Forecasting

Buonanno,

Caputo,

Balog

et al. 2024

Energies

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“…Computer-based soft computing methods, such as support vector machine (SVM), particle swarm optimization (PSO), fuzzy logic (FL), fuzzy decision tree (FDT), artificial neural networks ( ), wavelet neural network (WNN), genetic algorithm ( ), adaptive neuro-fuzzy inference system (ANFIS), co-active neuro-fuzzy inference system (CANFIS), convolutional neural network (CNN), imperialist competitive algorithm ( ) and, recurrent neural network (RNN) have recently been advanced in research areas of scientific, engineering, technological, and industrial courses [ [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] ]. These state-of-the-art mathematical modeling tools can capture high dimensional complex data, recognize inherent highly complex links from input-output data, find optimum patterns, and forecast target parameters [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of gross calorific value from coal analysis using decision tree-based bagging and boosting techniques

Munshi,

Jahan,

Howladar

et al. 2024

Heliyon

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“…2 Main soft computing methods [102] Fuzzy logic is one of the crucial ML approaches in soft computing. The adaptability of fuzzy logic makes it ideal for applications requiring linguistic variables and fuzzy sets, which include temperature manage systems, choice-making tactics, and professional systems [103], [104]. Genetics and evolutionary approaches are heavily influenced by biological evolution and natural selection theory.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence applications in solar energy

Le,

et al. 2024

JOIV : Int. J. Inform. Visualization

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Renewable energy research has become significant in the modern period owing to escalating prices of fossil fuels and the pressing need to reduce greenhouse gas emissions. Solar energy stands out among these sources due to its abundance and global accessibility. However, its weather-dependent and cyclical nature add inherent risks, making effective planning and management difficult. Soft computing technologies provide attractive solutions for modeling such systems, while machine learning and optimization techniques are gaining popularity in the solar energy industry. The current literature highlights the growing use of soft computing technologies, emphasizing their potential to address difficult challenges in solar energy systems. To effectively reap the benefits, these strategies must be seamlessly connected with emerging technologies like the Internet of Things (IoT), big data analytics, and cloud computing. This integration provides a unique opportunity to improve the scalability, flexibility, and efficiency of solar energy systems. Researchers can use these synergies to create intelligent, linked solar energy ecosystems capable of real-time optimization of energy production, delivery, and consumption. These technologies have the potential to transform the renewable energy environment, allowing for more resilient and sustainable energy infrastructures. Furthermore, as these technologies improve, there is a growing demand for trained experts to address associated cybersecurity problems, assuring the integrity and security of these sophisticated systems. Researchers may pave the road for a more sustainable and energy-efficient future by working collaboratively and using interdisciplinary methodologies.

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Fuzzy-based prediction of solar PV and wind power generation for microgrid modeling using particle swarm optimization

Cited by 23 publications

References 18 publications

Machine Learning and Weather Model Combination for PV Production Forecasting

Machine Learning and Weather Model Combination for PV Production Forecasting

Prediction of gross calorific value from coal analysis using decision tree-based bagging and boosting techniques

Artificial intelligence applications in solar energy

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