K-means has been widely used in solving a wide range of clustering problems arising in engineering and industrial applications, but it still suffers from several issues. To address these issues, a hierarchical K-means method enhanced by Trust-Tech (H-KTT) is presented in this paper. The proposed H-KTT method is composed of two stages. The first stage of H-KTT is a hierarchical K-means (H-K-means) method for enhancing K-means with better initial points. Second, the H-Kmeans method is further enhanced to find multiple high-quality clustering results by the Trust-Tech methodology. The H-KTT method was evaluated on several test datasets including the clustering of Automatic Meter Reading (AMR), popular in power grids, with promising results. In particular, the evaluation results indicate that the proposed H-KTT method can significantly improve both the quality and stability of the clustering results by the K-means method. Furthermore, while the K-means gives stochastic clustering results, the proposed H-KTT method usually gives deterministic clustering results.
In order to actively respond to the “14th Five-Year Plan,” the PGA algorithm is used to develop a new energy planning strategy in this paper. The project can make full use of my country’s abundant renewable energy resources, encourage energy conservation and reduction of emissions, improve the energy structure’s low-carbon level, support the development of smart green energy, and achieve ecological civilization construction. This solution can show users how much greenhouse gas emissions can be reduced through some environmental changes, as well as the basic issues of meeting the future energy needs. It can display the benefits, costs, and emissions data under different scenarios in the future and use the scenario demonstration method to show energy planning to make energy data more vivid. It allows people, technicians, and decision makers to understand what will happen to China’s carbon emissions over time in the next 15 years. This paper innovatively combines a particle swarm optimization algorithm with a genetic algorithm and designs a PGA algorithm for path optimization. In terms of carbon emission reduction, comparative trials demonstrate that the PGA algorithm’s path optimization is 58.06 percent greater than the genetic algorithm; In terms of cost, the PGA algorithm’s path optimization is 15.72% less expensive than the genetic algorithm’s. This article provides a reference path for selecting the best results for future energy planning schemes and provides a new strategy for the “14th Five-Year” energy plan.
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