Improved Evaluation Index Based Short-term Interval Prediction of Fluctuation Load

被引：0

作者：

Xu S. ^{[1
,2
]}

Zhang H. ^{[3
]}

Lin X. ^{[1
,2
]}

Li Z. ^{[1
,2
]}

Zhuo Y. ^{[4
]}

Wang Z. ^{[1
,2
]}

Sui Q. ^{[1
,2
]}

机构：

[1] State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan

[2] School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan

[3] Institute of Management Science, Guangdong Power Grid Corporation, Guangzhou

[4] Electric Power Dispatching and Control Center of Guangxi Power Grid Corporation, Nanning

来源：

Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2020年 / 44卷 / 02期

基金：

中国国家自然科学基金;

关键词：

Boundary estimation; Fluctuation load; Interval prediction; Neural network; Particle swarm optimization algorithm;

D O I：

10.7500/AEPS20190123002

中图分类号：

学科分类号：

摘要：

To solve the problem that traditional point-to-point prediction method is not applicable to the load with large fluctuation and uncertainty, this paper implements a prediction interval method based on improved evaluation index to improve existing forecasting evaluation index from two aspects of interval width and cumulative error, which enhances the reasonableness of prediction results. On this basis, weighing the characteristics and importance of each evaluation index for the influence on prediction results, the comprehensive evaluation index for interval prediction is established, and the interval prediction model is constructed by using neural network. Aiming at the optimization of the comprehensive evaluation index, the particle swarm optimization algorithm is used to train and optimize the structure parameters, so as to achieve ideal effect of interval prediction for fluctuating load. The historical load data with strong uncertainty is used to validate the proposed method. Comparing with the traditional point-to-point and interval forecasting methods, the results and analysis of the improved interval prediction verifies the effectiveness and superiority of the method. © 2020 Automation of Electric Power Systems Press.

引用

页码：156 / 163

页数：7

共 20 条

[1]

Huang N., Qi B., Liu Z., Et al., Probabilistic short-term load forecasting using Gaussian process regression with area grey incidence decision making, Automation of Electric Power Systems, 42, 23, pp. 64-75, (2018)

[2]

Wang B., Load forecasting of multiple linear regression based on temperature recency effect, Water Resources and Power, 36, 10, pp. 201-205, (2018)

[3]

Fang B., Li L., Zhao J., Et al., Short-term load forecasting based on the combination of dynamic similarity and static similarity, Power System Protection and Control, 46, 15, pp. 29-35, (2018)

[4]

Gu Y., Ma D., Cheng H., Power load forecasting based on similar data selection and improved gradient boosting decision tree, Proceedings of the CSU-EPSA, 31, 5, pp. 64-69, (2019)

[5]

Li X., Li C., Cong L., Et al., Nonlinear theoretical short term load forecasting based on dynamic weight similar day selection algorithm, Power System Protection and Control, 45, 6, pp. 1-8, (2017)

[6]

Yan J., Power load forecasting based on trend extrapolation method, Foreign Investment in China, 4, (2011)

[7]

Peng W., Wang J., Yin S., Short-term load forecasting model based on attention-LSTM in electricity market, Power System Technology, 43, 5, pp. 1745-1751, (2019)

[8]

Su X., Liu T., Cao H., Et al., A multiple distributed BP neural networks approach for short-term load forecasting based on Hadoop framework, Proceedings of the CSEE, 37, 17, pp. 4966-4973, (2017)

[9]

Wu Y., Lei J., Bao L., Et al., Short-term load forecasting based on improved grey relational analysis and neural network optimized by bat algorithm, Automation of Electric Power Systems, 42, 20, pp. 67-74, (2018)

[10]

Yang N., Li H., Yuan J., Et al., Medium-and long-term load forecasting method considering grey correlation degree analysis, Proceedings of the CSU-EPSA, 30, 6, pp. 108-114, (2018)

← 1 2 →