Data-driven configuration optimization of an off-grid wind/PV/hydrogen system based on modified NSGA-II and CRITIC-TOPSIS

Abstract The increasing global industrialization and over-exploitation of fossil fuels has induced the release of greenhouse gases, leading to an increase in global temperature and causing environmental issues. There is therefore an urgent necessity to reach net-zero carbon emissions. Only 4.5% of countries have achieved carbon neutrality, and most countries are still planning to do so by 2050–2070. Moreover, synergies between different countries have hampered synergies between adaptation and mitigation policies, as well as their co-benefits. Here, we present a strategy to reach a carbon neutral economy by examining the outcome goals of the 26th summit of the United Nations Climate Change Conference of the Parties (COP 26). Methods have been designed for mapping carbon emissions, such as input–output models, spatial systems, geographic information system maps, light detection and ranging techniques, and logarithmic mean divisia. We present decarbonization technologies and initiatives, and negative emissions technologies, and we discuss carbon trading and carbon tax. We propose plans for carbon neutrality such as shifting away from fossil fuels toward renewable energy, and the development of low-carbon technologies, low-carbon agriculture, changing dietary habits and increasing the value of food and agricultural waste. Developing resilient buildings and cities, introducing decentralized energy systems, and the electrification of the transportation sector is also necessary. We also review the life cycle analysis of carbon neutral systems. 

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Strategies to achieve a carbon neutral society: a review

Nowadays, coronavirus (COVID-19) is getting international attention due it considered as a life-threatened epidemic disease that hard to control the spread of infection around the world. Machine learning (ML) is one of intelligent technique that able to automatically predict the event with reasonable accuracy based on the experience and learning process. In the meantime, a rapid number of ML models have been proposed for predicate the cases of COVID-19. Thus, there is need for an evaluation and benchmarking of COVID-19 ML models which considered the main challenge of this study. Furthermore, there is no single study have addressed the problem of evaluation and benchmarking of COVID diagnosis models. However, this study proposed an intelligent methodology is to help the health organisations in the selection COVID-19 diagnosis system. The benchmarking and evaluation of diagnostic models for COVID-19 is not a trivial process. There are multiple criteria requires to evaluate and some of the criteria are conflicting with each other. Our study is formulated as a decision matrix (DM) that embedded mix of ten evaluation criteria and twelve diagnostic models for COVID-19. The multi-criteria decision-making (MCDM) method is employed to evaluate and benchmarking the different diagnostic models for COVID19 with respect to the evaluation criteria. An integrated MCDM method are proposed where TOPSIS applied for the benchmarking and ranking purpose while Entropy used to calculate the weights of criteria. The study results revealed that the benchmarking and selection problems associated with COVID19 diagnosis models can be effectively solved using the integration of Entropy and TOPSIS. The SVM (linear) classifier is selected as the best diagnosis model for COVID19 with the closeness coefficient value of 0.9899 for our case study data. Furthermore, the proposed methodology has solved the significant variance for each criterion in terms of ideal best and worst best value, beside issue when specific diagnosis models have same ideal best value.

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Benchmarking Methodology for Selection of Optimal COVID-19 Diagnostic Model Based on Entropy and TOPSIS Methods

A new hybrid multi-criteria decision-making approach for location selection of sustainable offshore wind energy stations: A case study

Solar energy potential assessment: A framework to integrate geographic, technological, and economic indices for a potential analysis

A geographical information system based multi-criteria decision-making approach for location analysis and evaluation of urban photovoltaic charging station: A case study in Beijing

Fangtong Liu

Papers

A geographical information system based multi-criteria decision-making approach for location analysis and evaluation of urban photovoltaic charging station: A case study in Beijing

Optimal site selection for parabolic trough concentrating solar power plant using extended PROMETHEE method: A case in China

Performance efficiency assessment of photovoltaic poverty alleviation projects in China: A three-phase data envelopment analysis model

Risk assessment of electric vehicle supply chain based on fuzzy synthetic evaluation

A DEMATEL-TODIM based decision framework for PV power generation project in expressway service area under an intuitionistic fuzzy environment