Constructing a smart framework for supplying the biogas energy in green buildings using an integration of response surface methodology, artificial intelligence and petri net modelling

Shahsavar, Mohammad Mahdi; Akrami, Mehran; Gheibi, Mohammad; Kavianpour, Babak; Fathollahi-Fard, Amir M.; Behzadian, Kourosh

doi:10.1016/j.enconman.2021.114794

Cited by 54 publications

(13 citation statements)

References 57 publications

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“…We followed the advice of several reliable sources, including [ 76 , 77 , 78 , 79 , 80 , 81 ], when putting up our comparisons. This method is also applicable in various sectors, such as other healthcare management services as well as the energy sector [ 82 , 83 , 84 ].…”

Section: Resultsmentioning

confidence: 99%

A Novel Hybrid Parametric and Non-Parametric Optimisation Model for Average Technical Efficiency Assessment in Public Hospitals during and Post-COVID-19 Pandemic

et al. 2021

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The COVID-19 pandemic has had a significant impact on hospitals and healthcare systems around the world. The cost of business disruption combined with lingering COVID-19 costs has placed many public hospitals on a course to insolvency. To quickly return to financial stability, hospitals should implement efficiency measure. An average technical efficiency (ATE) model made up of data envelopment analysis (DEA) and stochastic frontier analysis (SFA) for assessing efficiency in public hospitals during and after the COVID-19 pandemic is offered. The DEA method is a non-parametric method that requires no information other than the input and output quantities. SFA is a parametric method that considers stochastic noise in data and allows statistical testing of hypotheses about production structure and degree of inefficiency. The rationale for using these two competing approaches is to balance each method’s strengths, weaknesses and introduce a novel integrated approach. To show the applicability and efficacy of the proposed hybrid VRS-CRS-SFA (VCS) model, a case study is presented.

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Section: Resultsmentioning

confidence: 99%

A Novel Hybrid Parametric and Non-Parametric Optimisation Model for Average Technical Efficiency Assessment in Public Hospitals during and Post-COVID-19 Pandemic

et al. 2021

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“…Statistical methods extract the internal relationships between historical data and establish a specific mapping relationship between prediction model input and output. Specifically, statistical methods include the persistence model, the autoregressive moving average (ARMA) model (Boland et al, 2016), the regression method (Trapero et al, 2015), the exponential smoothing method (Dong et al, 2013), the support vector machine (SVM) (Jufri et al, 2019), and the artificial neural network (ANN) (Shahsavar et al, 2021). Integrated methods incorporate multiple models into physical or statistical methods (Abdel-Nasser and Mahmoud, 2019), effectively circumventing the limitations of a single model and increasing the model's predictive accuracy.…”

Section: Literature Review and Motivationmentioning

confidence: 99%

A Photovoltaic Power Predicting Model Using the Differential Evolution Algorithm and Multi-Task Learning

Pang

Liu²,

Zhang³

et al. 2022

Front. Mater.

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The rapid growth of photovoltaic installed capacity exacerbates the power management challenges faced by photovoltaic power stations, emphasizing the importance of accurate and stable photovoltaic generation forecasting. As a result, researchers conducted research and developed several photovoltaic power prediction models. However, many prediction models focus exclusively on the algorithm structure in order to improve model accuracy, oblivious to how the dataset is constructed and divided for the prediction model. This paper proposes a comprehensive model to address this gap. To be more precise, the differential evolution algorithm is constantly looking for optimal values between different populations and determining the best way to construct datasets for prediction tasks. Multi-task learning enables the transfer of knowledge between related tasks via parameter sharing layers, referring to the accuracy and stability of prediction models. Overall, the proposed model achieves high prediction accuracy and stability. The prediction error of the proposed model is less than 450W in RMSE, NRMSE is less than 2.5%, and R-Square is greater than 99% in multiple prediction tasks. Additionally, when compared to other single-task prediction models with an R-Square greater than 96%, the proposed model further reduces the root mean squared error by an average of 28% and the standard deviation of root mean squared error by 54%.

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“…Our independent proposed model does not require predetermined preference information, and this is one of the main advantages of the suggested method, which cannot be seen in recent studies. This method is also applicable in various sectors, such as other healthcare management services as well as energy sector [ 79 , 80 , 81 ].…”

Section: Numerical Example In Hospitals That Provide Stroke Care Servicesmentioning

confidence: 99%

Developing a Novel Integrated Generalised Data Envelopment Analysis (DEA) to Evaluate Hospitals Providing Stroke Care Services

et al. 2021

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Stroke is the biggest cause of adult disability and the third biggest cause of death in the US. Stroke is a medical emergency, and the treatment given in the early hours is important in shaping the patient’s long-term recovery and prognosis. Despite the fact that substantial attention has been dedicated to this complex and difficult issue in healthcare, novel strategies such as operation research-based approaches have hardly been used to deal with the difficult challenges associated with stroke. This study proposes a novel approach with data envelopment analysis (DEA) and multi-objective linear programming (MOLP) in hospitals that provide stroke care services to select the most efficient approach, which will be a new experiment in literature perception. DEA and MOLP are widely used for performance evaluation and efficiency measurement. Despite their similarities and common concepts, the two disciplines have evolved separately. The generalised DEA (GDEA) cannot incorporate the preferences of decision-makers (DMs) preferences and historical efficiency data. In contrast, MOLP can incorporate the DM’s preferences into the decision-making process. We transform the GDEA model into MOLP through the max-ordering approach to (i) solve the problem interactively; (ii) use the step method (STEM) and consider DM’s preferences; (iii) eliminate the need for predetermined preference information; and (iv) apply the most preferred solution (MPS) to identify the most efficient approach. A case study of hospitals that provide stroke care services is taken as an example to illustrate the potential application of the proposed approach method.

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Constructing a smart framework for supplying the biogas energy in green buildings using an integration of response surface methodology, artificial intelligence and petri net modelling

Cited by 54 publications

References 57 publications

A Novel Hybrid Parametric and Non-Parametric Optimisation Model for Average Technical Efficiency Assessment in Public Hospitals during and Post-COVID-19 Pandemic

A Novel Hybrid Parametric and Non-Parametric Optimisation Model for Average Technical Efficiency Assessment in Public Hospitals during and Post-COVID-19 Pandemic

A Photovoltaic Power Predicting Model Using the Differential Evolution Algorithm and Multi-Task Learning

Developing a Novel Integrated Generalised Data Envelopment Analysis (DEA) to Evaluate Hospitals Providing Stroke Care Services

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