Improving Solar and PV Power Prediction with Ensemble Methods

Dao, Le Anh; Ferrarini, Luca; Carrubba, D. La

doi:10.1016/j.ifacol.2020.12.1974

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…Ensemble learning methods such as random forest and gradient boosting often have built‐in ways to determine the significance of each trait. Metrics such as the Gini impurity, mean drop impurity, and advance in split information are used to assess the importance of a feature 177–179 The more important a trait, the more strongly it affects how well the model can predict the biochar supply. The key determinants of biochar yield can be easier to understand with the help of feature importance analysis, which can also help with feature selection and dimensionality reduction, thus making the model more efficient and easier to understand 163,180 …”

Section: Feature Importance and Sensitivity Analysismentioning

confidence: 99%

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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Section: Feature Importance and Sensitivity Analysismentioning

confidence: 99%

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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“…Solar irradiation, ambient temperature, wind speed, and relative humidity affect the cell's temperature. Various researchers have made efforts to predict the power generated from a solar PV plant by utilizing artificial intelligence (AI) tools like adaptive neuro-fuzzy inference system (ANFIS) [9], artificial neural networks (ANN) [10], numerical regression [11], support vector machines [3,12], and response surface methodology (RSM) [13] based on weather categorization concepts. Shi et al [14] used a support vector machine for weather categorization to create a unique prediction model for estimating the power production of a 20 MW PV facility.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Gopi

Sharma²,

Sudhakar

et al. 2022

Sustainability

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Forecasting the performance and energy yield of photovoltaic (PV) farms is crucial for establishing the economic sustainability of a newly installed system. The present study aims to develop a prediction model to forecast an installed PV system’s annual power generation yield and performance ratio (PR) using three environmental input parameters: solar irradiance, wind speed, and ambient air temperature. Three data-based artificial intelligence (AI) techniques, namely, adaptive neuro-fuzzy inference system (ANFIS), response surface methodology (RSM), and artificial neural network (ANN), were employed. The models were developed using three years of data from an operational 2MWp Solar PV Project at Kuzhalmannam, Kerala state, India. Statistical indices such as Pearson’s R, coefficient of determination (R2), root-mean-squared error (RMSE), Nash-Sutcliffe efficiency (NSCE), mean absolute-percentage error (MAPE), Kling-Gupta efficiency (KGE), Taylor’s diagram, and correlation matrix were used to determine the most accurate prediction model. The results demonstrate that ANFIS was the most precise performance ratio prediction model, with an R2 value of 0.9830 and an RMSE of 0.6. It is envisaged that the forecast model would be a valuable tool for policymakers, solar energy researchers, and solar farm developers.

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An Integrated EMVO and ARBFN Algorithms for Output Power Forecasting and Fault Prediction in Solar PV Systems

Manoj¹,

Jose²

2023

J. Electr. Eng. Technol.

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Improving Solar and PV Power Prediction with Ensemble Methods

Cited by 5 publications

References 13 publications

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

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

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

An Integrated EMVO and ARBFN Algorithms for Output Power Forecasting and Fault Prediction in Solar PV Systems

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