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首页> 外文期刊>Journal of intelligent & fuzzy systems: Applications in Engineering and Technology >An improved quantum clustering algorithm with weighted distance based on PSO and research on the prediction of electrical power demand
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An improved quantum clustering algorithm with weighted distance based on PSO and research on the prediction of electrical power demand

机译：基于PSO的加权距离改进量子聚类算法及电力需求预测研究

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摘要

The ability to accurately and reliably predict annual electricity demand is essential in modern society for effective planning, economic development, and to ensure the sustainability of the electricity supply. Considering the correlation between annual electricity demand and economic development, as well as annual electricity demand under low-carbon-economy targets, this paper proposes an improved quantum clustering algorithm (particle swarm optimization-weighted distance quantum clustering, PSO-WDQC) as a power demand forecasting model. This method can not only improve the accuracy of predictions but also accurately evaluate the economic development of a region. To demonstrate this ability, the paper applies the proposed method to low-dimensional Iris data as well as high-dimensional Wine data in order to verify the effectiveness of the method. Then, the method is combined with ridge regression to predict the demand for electricity under the low-carbon-economy target of China. The experimental results show that the method can accurately predict annual power demand with a relative error of 0.1674%. Moreover, the model accurately reflects that the Chinese economy has entered a new normal state since 2012, meaning that the economic growth rate has changed from high-speed to medium-high-speed.
机译：准确且可靠地预测年电力需求的能力在现代化社会中是必不可少的有效规划，经济发展，并确保电力供应的可持续性。考虑到年电力需求与经济发展之间的相关性，以及低碳经济目标下的年电费，本文提出了一种改进的量子聚类算法（粒子群优化 - 加权距离量子聚类，PSO-WDQC）作为电源需求预测模型。这种方法不仅可以提高预测的准确性，而且还可以准确地评估区域的经济发展。为了证明这种能力，本文将所提出的方法应用于低维虹膜数据以及高维葡萄酒数据，以验证该方法的有效性。然后，该方法与RIDGE回归结合，以预测中国低碳经济目标下的电力需求。实验结果表明，该方法可以准确地预测年的电力需求，相对误差为0.1674％。此外，该模型准确地反映了中国经济自2012年以来进入了新的正常状态，这意味着经济增长率从高速变为中高速。

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