Energy Storage Benefits Assessment Using Multiple-Choice Criteria: The Case of Drini River Cascade, Albania

Malka, Lorenc; Daci, Alfred; Kuriqi, Alban; Bartocci, Pietro; Rrapaj, Ermonela

doi:10.3390/en15114032

Cited by 43 publications

(9 citation statements)

References 31 publications

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“…Future infrastructure for generating and distributing electricity must include electric energy storage [ 85 ]. Bangladesh is situated in South Asia between 20°34′N to 26°38′N latitude and between 88°01′E to 92°41′E longitude which is a perfect location for solar energy utilization and storage [ [86] , [87] , [88] ].…”

Section: Future Prospect Of Re In Bangladeshmentioning

confidence: 99%

Current status of running renewable energy in Bangladesh and future prospect: A global comparison

Abdullah-Al-Mahbub

Islam

2023

Heliyon

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Section: Future Prospect Of Re In Bangladeshmentioning

confidence: 99%

Current status of running renewable energy in Bangladesh and future prospect: A global comparison

Abdullah-Al-Mahbub

Islam

2023

Heliyon

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“…Energy storage devices, frequently utilizing batteries, are essential elements in addressing the need for more sustainable energy sources. Energy storage systems not only mitigate reliance on nonrenewable energy sources but also enhance the stability and cost-efectiveness of renewable energy [4]. Batteries fnd extensive utilization across a wide spectrum of applications throughout the domain of renewable energy, apart from their primary roles in energy storage and transmission.…”

Section: Introductionmentioning

confidence: 99%

AI-Driven Digital Twin Model for Reliable Lithium-Ion Battery Discharge Capacity Predictions

Nair,

Vakharia,

Shah

et al. 2024

International Journal of Intelligent Systems

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The present study proposes a novel method for predicting the discharge capabilities of lithium-ion (Li-ion) batteries using a digital twin model in practice. By combining cutting-edge machine learning techniques, such as AdaBoost and long short-term memory (LSTM) network, with a semiempirical mathematical structure, the digital twin (DT)—a virtual representation that mimics the behavior of actual batteries in real time is constructed. Various metaheuristic optimization methods, such as antlion, grey wolf optimization (GWO), and improved grey wolf optimization (IGWO), are used to adjust hyperparameters in order to optimize the models. As indicators of performance, mean absolute error (MAE) and root-mean-square error (RMSE) are applied to the models after they have undergone extensive training and ten-fold cross-validation. The models are rigorously trained and cross-validated using the NASA battery aging dataset, a widely accepted benchmark dataset for battery research. The IGWO-AdaBoost digital twin model emerges as the standout performer, achieving exceptional accuracy in predicting the discharge capacity. This model demonstrates the lowest mean absolute error (MAE) of 0.01, showcasing its superior precision in estimating discharge capabilities. Additionally, the root mean square error (RMSE) for the IGWO-AdaBoost DT model is also the lowest at 0.01. The findings of this study offer insightful information about the potential utilization of the digital twin model to accurately predict the discharge capacity of batteries.

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“…Consumption of fossil fuels, such as coal, oil, and natural gas, is a substantial factor that affects CO 2 emissions. Therefore, it is imperative to promote the transformation of energy consumption structure from fossil fuels to renewable energy such as hydropower (Kuriqi et al 2019(Kuriqi et al , 2020(Kuriqi et al , 2021Malka et al 2022), solar energy (Rabaia et al 2021;Djaafari et al 2022), and wind energy (Sadorsky 2021).…”

Section: Introductionmentioning

confidence: 99%

Forecasting carbon dioxide emissions: application of a novel two-stage procedure based on machine learning models

Wang

Jin

2023

Journal of Water and Climate Change

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Accurate forecast of carbon dioxide (CO2) emissions plays a significant role in China's carbon peaking and carbon neutrality policies. A novel two-stage forecast procedure based on support vector regression (SVR), random forest (RF), ridge regression (Ridge), and artificial neural network (ANN) is proposed and evaluated by comparing it with the single-stage forecast procedure. Nine independent variables’ data (study period: 1985–2020) are used to forecast the CO2 emissions in China. Our results reveal that, when the time gap, h increases from 1 to 8, the average root mean squared error (RMSE) and mean absolute error (MAE) of SVR–SVR, SVR–RF, SVR–Ridge, and SVR–ANN are almost uniformly lower than errors arising from their single-stage version, respectively. Among these two-stage models, SVR–ANN exhibits the lowest forecast errors, whereas SVR–RF admits the highest. The mean percentage decrease in forecast errors of SVR–SVR vs. SVR, SVR–RF vs. RF, SVR–Ridge vs. Ridge, and SVR–ANN vs. ANN are 36.06, 5.98, 43.05, and 14.81 for RMSE, and 36.06, 6.91, 43.27, and 15.35 for MAE. Our two-stage procedure is also suitable to forecast other variables, such as fossil fuel and renewable energy consumption.

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Energy Storage Benefits Assessment Using Multiple-Choice Criteria: The Case of Drini River Cascade, Albania

Cited by 43 publications

References 31 publications

Current status of running renewable energy in Bangladesh and future prospect: A global comparison

Current status of running renewable energy in Bangladesh and future prospect: A global comparison

AI-Driven Digital Twin Model for Reliable Lithium-Ion Battery Discharge Capacity Predictions

Forecasting carbon dioxide emissions: application of a novel two-stage procedure based on machine learning models

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