Duan Jiandong, Qiu Xinyu, Ma Wentao, Tian Xuan, Shang Di
School of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China.
Entropy (Basel). 2018 Feb 8;20(2):112. doi: 10.3390/e20020112.
In recent years, with the deepening of China's electricity sales side reform and electricity market opening up gradually, the forecasting of electricity consumption (FoEC) becomes an extremely important technique for the electricity market. At present, how to forecast the electricity accurately and make an evaluation of results scientifically are still key research topics. In this paper, we propose a novel prediction scheme based on the least-square support vector machine (LSSVM) model with a maximum correntropy criterion (MCC) to forecast the electricity consumption (EC). Firstly, the electricity characteristics of various industries are analyzed to determine the factors that mainly affect the changes in electricity, such as the gross domestic product (GDP), temperature, and so on. Secondly, according to the statistics of the status quo of the small sample data, the LSSVM model is employed as the prediction model. In order to optimize the parameters of the LSSVM model, we further use the local similarity function MCC as the evaluation criterion. Thirdly, we employ the -fold cross-validation and grid searching methods to improve the learning ability. In the experiments, we have used the EC data of Shaanxi Province in China to evaluate the proposed prediction scheme, and the results show that the proposed prediction scheme outperforms the method based on the traditional LSSVM model.
近年来，随着中国电力销售侧改革的不断深入以及电力市场的逐步开放，电力消费预测成为电力市场一项极为重要的技术。目前，如何准确预测电力以及科学地评估预测结果仍是关键的研究课题。本文提出一种基于带有最大相关熵准则（MCC）的最小二乘支持向量机（LSSVM）模型的新型预测方案来预测电力消费（EC）。首先，分析各行业的用电特性，确定主要影响电力变化的因素，如国内生产总值（GDP）、温度等。其次，根据小样本数据现状的统计，采用LSSVM模型作为预测模型。为了优化LSSVM模型的参数，进一步使用局部相似性函数MCC作为评估准则。第三，采用k折交叉验证和网格搜索方法来提高学习能力。在实验中，我们使用了中国陕西省的电力消费数据来评估所提出的预测方案，结果表明所提出的预测方案优于基于传统LSSVM模型的方法。
