支持向量机
粒子群优化
计算机科学
特征选择
人工智能
电力系统
相关向量机
数据挖掘
机器学习
冗余(工程)
功率(物理)
量子力学
操作系统
物理
出处
期刊:Applied Energy
[Elsevier]
日期:2020-09-24
卷期号:279: 115332-115332
被引量:134
标识
DOI:10.1016/j.apenergy.2020.115332
摘要
Abstract Accurate power load forecasting contributes to guaranteeing safe dispatch and stable operation of a power system. As a great forecasting tool, support vector machine is widely used in power load forecasting. However, due to the rapid development of information technology, the prediction result of simple support vector machine is no longer accurate enough to forecast in the smart grid. To enhance the prediction accuracy, this paper makes some improvements on support vector machine, and proposes a hybrid model integrated with intelligent methods for feature selection and parameter optimization. Firstly, real-time price becomes an important influencing factor of power load as people increasingly rely on demand and real-time price to adjust their electricity consumption patterns. Thus, real-time price, together with other factors that affect power load, is taken as a candidate feature, and minimal redundancy maximal relevance is applied to derive informative features from candidate features. Secondly, as for another feature, the historical load sequence, to make its selection more general, this paper employs the weighted gray relation projection algorithm for holidays to be predicted. Finally, second-order oscillation and repulsion particle swarm optimization is used for optimizing parameters of support vector machine. Moreover, the proposed model is tested via simulations on datasets of Singapore. By comparing prediction results of the proposed model, the support vector machine before improvement and other three forecasting models, this paper confirms that the improvements on support vector machine are effective, and the proposed model outperforms the other forecasting models in aspect of accuracy, stability and effectiveness.
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