Machine learning-driven energy management of a hybrid nuclear-wind-solar-desalination plant

Pombo, Daniel Vázquez; Bindner, Henrik W.; Spataru, Sergiu; Sørensen, Poul Ejnar; Rygaard, Martin

doi:10.1016/j.desal.2022.115871

Cited by 12 publications

(2 citation statements)

References 49 publications

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“…Modeling languages and solvers [25] Linear [156]. Gurobi TM solver interfaced on Pyomo was used by [154], [155], [157] for solving linear and nonlinear power system problems. Another Python-based framework for optimizing energy systems is FINE (A Framework for Integrated Energy System Assessment).…”

Section: Optimization Algorithmmentioning

confidence: 99%

A review on recent standalone and grid integrated hybrid renewable energy systems: System optimization and energy management strategies

Basnet

Deschinkel

Moyne

et al. 2023

Renewable Energy Focus

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Section: Optimization Algorithmmentioning

confidence: 99%

A review on recent standalone and grid integrated hybrid renewable energy systems: System optimization and energy management strategies

Basnet

Deschinkel

Moyne

et al. 2023

Renewable Energy Focus

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“…This technology combines two renewable energy sources, wind and solar power, to generate electricity. However, with the help of machine learning, the performance of this hybrid system can be optimized by predicting weather conditions and adjusting their output, accordingly [13]. Today, methodologies for learning-based modeling are used to create exact forecast models for renewable energy sources.…”

Section: Introductionmentioning

confidence: 99%

An Artificial Neural Network-Based Approach for Real-Time Hybrid Wind–Solar Resource Assessment and Power Estimation

et al. 2023

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The precise prediction of power estimates of wind–solar renewable energy sources becomes challenging due to their intermittent nature and difference in intensity between day and night. Machine-learning algorithms are non-linear mapping functions to approximate any given function from known input–output pairs and can be used for this purpose. This paper presents an artificial neural network (ANN)-based method to predict hybrid wind–solar resources and estimate power generation by correlating wind speed and solar radiation for real-time data. The proposed ANN allows optimization of the hybrid system’s operation by efficient wind and solar energy production estimation for a given set of weather conditions. The proposed model uses temperature, humidity, air pressure, solar radiation, optimum angle, and target values of known wind speeds, solar radiation, and optimum angle. A normalization function to narrow the error distribution and an iterative method with the Levenberg–Marquardt training function is used to reduce error. The experimental results show the effectiveness of the proposed approach against the existing wind, solar, or wind–solar estimation methods. It is envisaged that such an intelligent yet simplified method for predicting wind speed, solar radiation, and optimum angle, and designing wind–solar hybrid systems can improve the accuracy and efficiency of renewable energy generation.

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A hyper-optimisation method based on a physics-informed machine learning and point clouds for a flat plate solar collector

Han

Mesgarpour

Asirvatham

et al. 2023

J Therm Anal Calorim

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This paper presents a new way to hyper-optimise a flat plate solar collector using a combination of regenerated point clouds, constructal theory, and physics-informed machine learning (PIML). The behaviour of the flat plate solar collector is studied as solar radiation and ambient temperature change, using both precise numerical simulation and PIML. The novel hyper-optimisation method integrates these two approaches to improve the performance of the solar collector. In this method, the volume of fluid and solid structure of the flat plate solar collector (FPSC) is transformed into point clouds based on constructal theory. The point clouds are then regenerated into a continuous and uniform 3D geometry using optimised parameters. To put the modified version of the flat plate solar collector (FPSC) into practice, a computational method is used to generate a training data set for machine learning, specifically for neural networks. After thoroughly verifying the computational results, the PIM is trained using the generated training data set. This study marked the first time that a regular computational method is replaced with PIML output to reduce the computational cost of prediction. In the second layer of calculation, a deep neural network is used to make predictions based on the outputs generated by PIML. Seven independent parameters are used to predict heat transfer and efficiency over time, including time, heat flux, mass flow rate, inlet temperature, number of pairs and clusters, and volume fraction of nanofluid, while 16 hidden layers and 63 learnable neurons are engaged in this prediction layer. The geometry matrix is redefined by constructal theory principles in a series of iteration loops to generate the first flat plate solar collector based on constructal theory (CTFPSC). The results indicated that the hyper-optimisation method could reduce calculation costs by 18.31% compared with the regular computational method. In addition, the results reveal that maximum outlet temperatures are possible when Nc > 3 and Np> 5.

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Machine learning-driven energy management of a hybrid nuclear-wind-solar-desalination plant

Cited by 12 publications

References 49 publications

A review on recent standalone and grid integrated hybrid renewable energy systems: System optimization and energy management strategies

A review on recent standalone and grid integrated hybrid renewable energy systems: System optimization and energy management strategies

An Artificial Neural Network-Based Approach for Real-Time Hybrid Wind–Solar Resource Assessment and Power Estimation

A hyper-optimisation method based on a physics-informed machine learning and point clouds for a flat plate solar collector

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