Artificial neural network coupled building-integrated photovoltaic thermal system for indian montane climate

Barthwal, Mohit; Rakshit, Dibakar

doi:10.1016/j.enconman.2021.114488

Cited by 22 publications

(3 citation statements)

References 35 publications

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“…ANN prioritizes flexibility and dynamic adaptability over exact or highly accurate results in model implementations. The common practice in ANN modeling is to offer various study kinds and scopes, analyze prediction factors, and exhibit multiple parts of training techniques, algorithms, and data demands [40].…”

Section: Peer 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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Section: Peer 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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“…In the study of Barthwal and Rakshit (2021) [34], an artificial neural network (ANN) was trained to forecast the annual energy and exergy output of a modeled system. The ANN incorporates six input parameters, encompassing design, operational, and application factors.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Nanofluid Hybrid Solar Collectors through Artificial Intelligence Models

Margoum,

Hajji,

Aneli

et al. 2024

Energies

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This study systematically explores and compares the performance of various artificial-intelligence (AI)-based models to predict the electrical and thermal efficiency of photovoltaic–thermal systems (PVTs) cooled by nanofluids. Employing extreme gradient boosting (XGB), extra tree regression (ETR), and k-nearest-neighbor (KNN) regression models, their accuracy is quantitatively evaluated, and their effectiveness measured. The results demonstrate that both XGB and ETR models consistently outperform KNN in accurately predicting both electrical and thermal efficiency. Specifically, the XGB model achieves remarkable correlation coefficient (R2) values of approximately 0.99999, signifying its superior predictive capabilities. Notably, the XGB model exhibits a slightly superior performance compared to ETR in estimating electrical efficiency. Furthermore, when predicting thermal efficiency, both XGB and ETR models demonstrate excellence, with the XGB model showing a slight edge based on R2 values. Validation against new data points reveals outstanding predictive performance, with the XGB model attaining R2 values of 0.99997 for electrical efficiency and 0.99995 for thermal efficiency. These quantitative findings underscore the accuracy and reliability of the XGB and ETR models in predicting the electrical and thermal efficiency of PVT systems when cooled by nanofluids. The study’s implications are significant for PVT system designers and industry professionals, as the incorporation of AI-based models offers improved accuracy, faster prediction times, and the ability to handle large datasets. The models presented in this study contribute to system optimization, performance evaluation, and decision-making in the field. Additionally, robust validation against new data enhances the credibility of these models, advancing the overall understanding and applicability of AI in PVT systems.

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“…Of the various ANN-based algorithms, DNN, in particular, has the advantage of enabling nonlinear combinations between input variables. Additionally, it is a powerful tool for modeling complex systems [39,40]. Furthermore, to address the data imbalance in the data of cyanobacteria cell counts in the study area, a model combining DNN with SMOTE was proposed, and its applicability was evaluated.…”

Section: Introductionmentioning

confidence: 99%

Research on the Development and Application of a Deep Learning Model for Effective Management and Response to Harmful Algal Blooms

Kim

et al. 2023

Water

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Harmful algal blooms (HABs) caused by harmful cyanobacteria adversely impact the water quality in aquatic ecosystems and burden socioecological systems that are based on water utilization. Currently, Korea uses the Environmental Fluid Dynamics Code-National Institute of Environmental Research (EFDC-NIER) model to predict algae conditions and respond to algal blooms through the HAB alert system. This study aimed to establish an additional deep learning model to effectively respond to algal blooms. The prediction model is based on a deep neural network (DNN), which is a type of artificial neural network widely used for HAB prediction. By applying the synthetic minority over-sampling technique (SMOTE) to resolve the imbalance in the data, the DNN model showed improved performance during validation for predicting the number of cyanobacteria cells. The R-squared increased from 0.7 to 0.78, MAE decreased from 0.7 to 0.6, and RMSE decreased from 0.9 to 0.7, indicating an enhancement in the model’s performance. Furthermore, regarding the HAB alert levels, the R-squared increased from 0.18 to 0.79, MAE decreased from 0.2 to 0.1, and RMSE decreased from 0.3 to 0.2, indicating improved performance as well. According to the results, the constructed data-based model reasonably predicted algae conditions in the summer when algal bloom-induced damage occurs and accurately predicted the HAB alert levels for immediate decision-making. The main objective of this study was to develop a new technology for predicting and managing HABs in river environments, aiming for a sustainable future for the aquatic ecosystem.

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Artificial neural network coupled building-integrated photovoltaic thermal system for indian montane climate

Cited by 22 publications

References 35 publications

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

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

Optimizing Nanofluid Hybrid Solar Collectors through Artificial Intelligence Models

Research on the Development and Application of a Deep Learning Model for Effective Management and Response to Harmful Algal Blooms

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