EMD-based gray combined forecasting model - Application to long-term forecasting of wind power generation.

Wind power is the most promising renewable energy source after hydropower because of its mature technology and low price, and has great potential for carbon emission reduction. Long-term forecasts of its power generation can help power companies to develop operational plans, grid configuration and power dispatch, and can also provide a basis for the government to formulate energy and environmental policies. However, due to the characteristics of China's monsoon climate and wind power industry development, wind power generation data are characterized by nonlinear cycles and small data volume, which makes accurate prediction more difficult. To this end, this paper develops a new prediction model and applies it to the long-term prediction of wind power generation in China, and proposes some targeted policy recommendations based on the prediction results to promote the development of China's wind power industry.

Effective allocation of resources in water pollution treatment alternatives: a multi-stage gray group decision-making method based on hesitant fuzzy linguistic term sets.

With the significant economic shift, water pollution treatment has gradually become a key problem which needs to be deeply investigated for the sustainable development of China. In the face of specific water pollution incidents, multiple alternatives are often required to work together in order to achieve better results. However, due to the limitation of resources, alternatives must be ranked to realize the effective allocation of resources, which means the more highly ranked ones should possess more disposable resources. Furthermore, the water pollution treatment process is a multi-stage and multi-objective process. In each stage, decision-makers may have different emphasis and thus have different preferences for the treatment alternatives. How to effectively aggregate decision-makers' preferences in different stages into an overall preference so as to form a ranking of treatment alternatives under global constraints has turned into a problem worthy of discussion. Under such background, this paper proposes a multi-stage gray group decision-making method, where decision-makers use Group-G1 to rank and weight the criteria, and in this way, the weights of decision-makers and criteria in each stage could be determined. Considering the difference and deficiency of the cognitive level of decision-makers, this paper adopts the form of hesitant fuzzy linguistic term sets (HFITS) to express the evaluation information of decision-makers. And then, gray incidence analysis is selected to rank the alternatives. After ranking the alternatives in each stage, the multi-stage rankings will be aggregated into an overall ranking and the resource allocation is made according to the priorities of the alternatives. Finally, an example of water pollution treatment alternatives ranking based on a cyanobacterial bloom in Taihu Lake, China, is given to illustrate the proposed approach.

Medical Waste Disposal Method Selection Based on a Hierarchical Decision Model with Intuitionistic Fuzzy Relations.

Although medical waste usually accounts for a small fraction of urban municipal waste, its proper disposal has been a challenging issue as it often contains infectious, radioactive, or hazardous waste. This article proposes a two-level hierarchical multicriteria decision model to address medical waste disposal method selection (MWDMS), where disposal methods are assessed against different criteria as intuitionistic fuzzy preference relations and criteria weights are furnished as real values. This paper first introduces new operations for a special class of intuitionistic fuzzy values, whose membership and non-membership information is cross ratio based ]0, 1[-values. New score and accuracy functions are defined in order to develop a comparison approach for ]0, 1[-valued intuitionistic fuzzy numbers. A weighted geometric operator is then put forward to aggregate a collection of ]0, 1[-valued intuitionistic fuzzy values. Similar to Saaty's 1-9 scale, this paper proposes a cross-ratio-based bipolar 0.1-0.9 scale to characterize pairwise comparison results. Subsequently, a two-level hierarchical structure is formulated to handle multicriteria decision problems with intuitionistic preference relations. Finally, the proposed decision framework is applied to MWDMS to illustrate its feasibility and effectiveness.