Application of a probabilistic LHS-PAWN approach to assess building cooling energy demand uncertainties

Chaturvedi, Shobhit; Rajasekar, E.

doi:10.1007/s12273-021-0815-6

Cited by 16 publications

(7 citation statements)

References 53 publications

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“…To test the feasibility and reliability of the optimal scheduling method of a low-carbon energy supply chain based on machine learning and game theory, a low-carbon energy supply chain is selected as the experimental object, and the data scheduling method of this low-carbon energy supply chain is used. The methods in [3] and [4] are selected as the comparison. The experimental configuration includes a desktop computer with Windows 2010 operating system, 16 GB hard disk, and i812.25 CPU, and the parameters during the experiment are set, as shown in Table 1.…”

Section: Experimental Analysismentioning

confidence: 99%

“…As a sustainable energy form, low-carbon energy has become an important direction of global energy transformation because of its environmental friendliness and little impact on climate [2]. In the traditional energy supply chain, suppliers, manufacturers, and distributors obtain the maximum benefits through game theory and have achieved remarkable results in practice [3][4]. However, in the low-carbon energy supply chain, due to its particularity, the traditional game theory model and optimization method are facing great challenges.…”

Section: Introductionmentioning

confidence: 99%

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Optimal scheduling method of low-carbon energy supply chain based on machine learning and game theory

Yan,

Zhang,

Wang

et al. 2024

J. Phys.: Conf. Ser.

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Aiming at the quality and stability of a low-carbon new energy supply chain, this paper puts forward an optimal scheduling method for a low-carbon energy supply chain based on machine learning and game theory. By establishing an index and preprocessing keywords, the similarity between the index and the data in the platform database is calculated, and the data-sharing result of the low-carbon energy supply chain is obtained. This paper analyzes the game benefits of each subject in the optimization of the low-carbon energy supply chain, obtains the game matrix, and realizes the optimal scheduling of the low-carbon energy supply chain. The experimental results show that the scheduling convergence error of the low-carbon energy supply chain is low, the accuracy is high, and the load supply equipment can run stably.

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Section: Experimental Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal scheduling method of low-carbon energy supply chain based on machine learning and game theory

Yan,

Zhang,

Wang

et al. 2024

J. Phys.: Conf. Ser.

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show abstract

“…To examine the sensitivity of our model, Latin Hypercubic sampling (LHS) combined with distribution-based sensitivity analysis (PAWN) were employed [55]. The selected parameters were first propagated by LHS, and then the output uncertainty was characterized by executing the LHS-created model.…”

Section: Sensitivity Analysismentioning

confidence: 99%

Seasonal Dynamics of Algal Net Primary Production in Response to Phosphorus Input in a Mesotrophic Subtropical Plateau Lake, Southwestern China

Zhang

Hou

et al. 2022

Water

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A comprehensive 3-dimensional hydrodynamic and eutrophication model, the environmental fluid dynamics code model (EFDC) with three functional phytoplankton groups, was applied to simulate the algal dynamics in a mesotrophic P-limited subtropical plateau lake, Lake Erhai, Southwestern China. Field investigations revealed the seasonal patterns in external total phosphorus (TP) input and TP concentration, as well as the composition of the phytoplankton community. The model was calibrated to reproduce qualitative features and the succession of phytoplankton communities, and the net primary production was calculated. The modeled daily net primary production (NPP) ranged between −16.89 and 15.12 mg C/m2/d and exhibited significant seasonal variation. The competition for phosphorus and temperature was identified as the primary governing factor of NPP by analyzing the parameter sensitivity and limitation factors of the lake. The simulation of four nutrient loading reduction scenarios suggested high phytoplankton biomass and NPP sensitivity to the external TP reduction. A significant positive correlation was found among NPP, total phytoplankton biomass and TP concentration. Overall, this work offers an alternative approach to estimating lake NPP, which has the potential to improve sustainable lake management.

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“…Likewise, Tian et al [35] investigated the accuracy of sensitivity and regression analysis based on a set of BEM input-output pairs. Similar methods have also been adopted in several later studies on sensitivity analysis or data-driven modelling [38][39][40], amongst which Geraldi et al [39] used an empirical dataset instead. In these studies, the bootstrap enabled the uncertainty quantification of their sensitivity or error indicators that were conventionally presented in deterministic terms.…”

Section: Application In the Built Environmentmentioning

confidence: 99%

On the robustness of thermal comfort against uncertain future climate: A Bayesian bootstrap method

Cui¹,

Raslan²,

Korolija³

et al. 2022

Building and Environment

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Application of a probabilistic LHS-PAWN approach to assess building cooling energy demand uncertainties

Cited by 16 publications

References 53 publications

Optimal scheduling method of low-carbon energy supply chain based on machine learning and game theory

Optimal scheduling method of low-carbon energy supply chain based on machine learning and game theory

Seasonal Dynamics of Algal Net Primary Production in Response to Phosphorus Input in a Mesotrophic Subtropical Plateau Lake, Southwestern China

On the robustness of thermal comfort against uncertain future climate: A Bayesian bootstrap method

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