Sen Guo

Comprehensive benefit evaluation of eco-industrial parks by employing the best-worst method based on circular economy and sustainability

At the aim of solving the increasing conflicts among the economic growth, resource shortage, and environmental aggravation, the eco-industrial park becomes a significant research issue to achieve sustainable development and circular economy. Therefore, evaluating the comprehensive benefit of eco-industrial parks and providing references and policy formulation in supporting the improvement of construction and management level for eco-industrial parks are of great significance. In this paper, a hybrid framework was proposed to assess the comprehensive benefit of eco-industrial parks in terms of circular economy and sustainability. Firstly, the evaluation index system was constructed by using grey-Delphi method, which included economic benefit criteria, social benefit criteria, and environmental benefit criteria with nine quantitative sub-criteria and four qualitative sub-criteria. Then, a new comparison-based method, namely the best-worst method, was employed to determine the weights of all sub-criteria and the performance values of all selected eco-industrial parks with respect to the qualitative sub-criteria. Finally, five selected representative eco-industrial parks in China were ranked in terms of comprehensive benefit, and the optimal eco-industrial park was selected. According to the results of comprehensive benefit evaluation for eco-industrial parks, the strengths and weaknesses of each eco-industrial park were obvious. At the end, the recommendations for the effective and rapid development of eco-industrial parks were formulated.

DGM (1, 1) model optimized by MVO (multi-verse optimizer) for annual peak load forecasting

A large number of renewable energies and uncertain power load accessing electric power system make the power load forecasting more complicated and face more new challenges. This paper presents a hybrid annual peak load forecasting model [namely MVO-DGM (1, 1)], which employs the latest optimization algorithm MVO (multi-verse optimizer) to determine two parameters of DGM (1, 1) model, and then uses the optimized DGM (1, 1) model to forecast annual peak load. The annual peak load of Shandong province in China from 2005 to 2014 is selected as the empirical example, and the analysis results demonstrate that the MVO algorithm for parameters’ determination of DGM (1, 1) model has significant superiority over the least square estimation method, particle swarm optimization and fruit fly optimization algorithm in terms of annual peak load forecasting. In addition, the proposed MVO-DGM (1, 1) peak load forecasting model has more excellent forecasting performance than other non-optimized forecasting techniques and other optimized DGM (1, 1) models due to its ascended local optima avoidance and better convergence speed. The hybrid MVO-DGM (1, 1) model proposed in this paper is feasible and effective in annual peak load forecasting, which can improve the forecasting accuracy.