Short-Term Power Load Forecasting Based on Feature Selection and Optimized Extreme Learning Machine

SHANG Liqun; LI Hongbo; HOU Yadong; HUANG Chenhao; ZHANG Jiantao

doi:10.7652/xjtuxb202204018

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Short-Term Power Load Forecasting Based on Feature Selection and Optimized Extreme Learning Machine

更新时间：2025-07-09

- Short-Term Power Load Forecasting Based on Feature Selection and Optimized Extreme Learning Machine
- Vol. 56, Issue 4, Pages: 165-175(2022)
- 作者机构：
  
  西安科技大学电气与控制工程学院,西安,710054
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202204018
  CLC： TM715
- Online First：10 April 2022，
  
  Published：2022
- 稿件说明：
移动端阅览
SHANG Liqun, LI Hongbo, HOU Yadong, et al. Short-Term Power Load Forecasting Based on Feature Selection and Optimized Extreme Learning Machine[J]. 2022, 56(4): 165-175.
DOI：

SHANG Liqun, LI Hongbo, HOU Yadong, et al. Short-Term Power Load Forecasting Based on Feature Selection and Optimized Extreme Learning Machine[J]. 2022, 56(4): 165-175. DOI： 10.7652/xjtuxb202204018.

摘要

针对负荷预测过程中特征量难以确定以及极限学习机(ELM)存在因随机产生的初始权值和阈值导致输出稳定性低的问题

提出了基于格拉姆施密特正交化与皮尔逊相关性分析相结合的特征选择方法(GSO-PCA)和改进灰狼算法(IGWO)优化ELM的短期电力负荷预测模型(IGWO-ELM)。对两种不同类型的特征分别使用GSO算法和PCA进行优选

并根据平均绝对百分比误差(MAPE)确定最优特征集

与传统的经验特征选择、最大互信息系数特征选择、随机森林特征选择比较

GSO-PCA特征选择的MAPE分别降低了1.3%、0.55%和0.83%

验证了其优越性; 将Tent混沌映射和粒子群优化算法(PSO)融入到灰狼优化算法中

得到IGWO

并利用两种典型的测试函数对IGWO性能进行测试

证明了其具有更强的寻优能力; 使用IGWO算法对ELM的初始权值和阈值进行动态优化

建立IGWO-ELM短期负荷预测模型。将拟合优度检验系数、平均绝对误差、均方根误差和MAPE作为评价指标

结合实例分析

与传统的模型进行比较。仿真结果表明:所提预测模型得到的4个评价指标分别为0.997 8、54.90 kW、72.02 kW和1.52%

明显优于其他模型

验证了所提模型的有效性和优越性。

Abstract

For the difficulty in determining the feature quantity and the low output stability of the extreme learning machine(ELM)due to randomly generated initial weights and thresholds during load forecasting

it is proposed in this paper that the Gram-Schmidt orthogonalization and Pearson correlation analysis(GSO-PCA)and improved gray wolf optimization(IGWO)algorithm be used to optimize the short-term power load forecasting model of ELM(IGWO-ELM). GSO and PCA are used for optimal selection of two distinct features respectively

and the mean absolute percentage error(MAPE)is based for determination of the optimal feature set. MAPE in GSO-PCA is 1.3%

0.55% and 0.83% lower respectively compared with that in traditional experience

maximum information coefficient and random forest for feature selection. This verifies its superiority. The Tent chaotic mapping and particle swarm optimization(PSO)algorithm are integrated into the gray wolf optimization algorithm to obtain IGWO algorithm which is tested with two typical test functions and proved with better optimization capability. The IGWO algorithm is used to dynamically optimize initial weights and threshold of the ELM and establish IGWO-ELM short-term power load forecasting model. This established model is then compared with a traditional model in terms of four evaluation indexes

i.e.

goodness of fit test coefficient

mean absolute error

root mean square error and MAPE and in line with case study. The simulation results show that these four indexes are 0.997 8

54.90 kW

72.02 kW and 1.52% and are siqnificantly better other models. This verifies the effectiveness and superiority of the proposed model.

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references

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