Short Term Load Forecasting Algorithm Based on Improved Grey Theory

Abstract

Abstract: Based on the analysis of the traditional grey load forecasting theory and GM (1, n) model, the GM (1, n) model is improved again to solve the problem of large error of forecasting results due to the strict requirements of the traditional grey theory on the original data. The experiment shows that the load forecasting model established by this method has a great improvement in the forecasting accuracy, which will be helpful for the future grey theory.

Key words: load forecasting, grey theory, GM (1, n) model

XU Hui-jun , WANG Zong-yao , LI Zhong-qing , LI Peng-fei , ZHOU Xin-kang . Short Term Load Forecasting Algorithm Based on Improved Grey Theory [J]. Computer and Modernization, 2021, 0(03): 24-27.

References ［23］

［1］	王惠中,周佳,刘轲. 电力系统短期负荷预测方法研究综述［J］. 电气自动化, 2015(1):1-3.
［2］	黄玉龙,刘明波,郑文杰,等. 基于可信区间的模糊线性回归动态负荷参数预测［J］. 电工技术学报, 2015(24):196-205.
［3］	马天男,牛东晓,黄雅莉,等. 基于Spark平台和多变量L2-Boosting回归模型的分布式能源系统短期负荷预测［J］. 电网技术, 2016(6):1642-1649.
［4］	YILDIZ B， BILBAO J I， SPROUL A B． A review and analysis of regression and machine learning models on commercial building electricity load forecasting［J］． Renewable and Sustainable Energy Reviews, 2017(73):1104-1122.
［5］	TARSITANO A, AMERISE I L. Short-term load forecasting using a two-stage sarimax model［J］. Energy, 2017(5):108-114.
［6］	刘宝英,杨仁刚. 基于主成分分析的最小二乘支持向量机短期负荷预测模型［J］. 电力自动化设备, 2018(11):13-17.
［7］	吴潇雨,和敬涵,张沛,等. 基于灰色投影改进随机森林算法的电力系统短期负荷预测［J］．电力系统自动化, 2015,39(12):50-55.
［8］	王正新. 基于傅立叶级数的小样本振荡序列灰色预测方法［J］. 控制与决策, 2014(2):270-274.
［9］	廖峰,刘清良,贺辉,等. 基于改进灰色模型与综合气象因素的母线负荷预测［J］. 电网技术, 2011(10):183-188.
［10］	李伟,韩力. 组合灰色预测模型在电力负荷预测中的应用［J］. 重庆大学学报(自然科学版), 2004(1):34-39.
［11］	吴云,雷建文,鲍丽山,等. 基于改进灰色关联分析与蝙蝠优化神经网络的短期负荷预测［J］. 电力系统自动化, 2018,20(10):67-74.
［12］	葛少云,贾鸥莎,刘洪. 基于遗传灰色神经网络模型的实时电价条件下短期电力负荷预测［J］. 电网技术, 2012(1):224-229.
［13］	罗党,韦保磊,李海涛,等. 灰色区间预测模型及其性质［J］. 控制与决策, 2016(12):2293-2298.
［14］	陈焕远,刘新东,佘彩绮. 改进的灰色理论在短期负荷预测中的应用［J］. 水电能源科学, 2011(11):189-191.
［15］	王大鹏,汪秉文. 基于变权缓冲灰色模型的中长期负荷预测［J］. 电网技术, 2013(1):167-171.
［16］	张朝飞,罗建军,徐兵华,等. 基于灰色理论的新陈代谢自适应多参数预测方法［J］. 上海交通大学学报, 2017(8):970-976.
［17］	吴利丰. 分数阶灰色预测模型及其应用研究［D］. 南京:南京航空航天大学, 2015.
［18］	YU F, XU X Z． A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network［J］. Applied Energy, 2014(134):102-113.
［19］	DUDEK G. Neural networks for pattern-based short-term load forecasting: A comparative study［J］. Neurocomputing, 2016(105):64-74.
［20］	〖KG-*5〗BAHRAMI S, HOOSHMAND R, PARASTEGARI M. Short term electric load forecasting by wavelet transform and grey model improved by PSO (particle swarm optimization) algorithm［J］. Energy, 2014(5):434-442.
［21］	SAINI L M， AGGARWAL S K， KUMAR A. Parameter optimization using genetic algorithm for support vector machine-based price-forecasting model in National electricity market［J］. IET Generation Transmission & Distribution, 2010,4(1):36-49.
［22］	徐洪基. 灰色模型在电力负荷预测中的应用［J］. 江苏工学院学报, 1990(1):81-92.
［23］	XIAO L Y， SHAO W， LIANG T L， et al． A combined model based on multiple seasonal patterns and modified firefly algorithm for electrical load forecasting［J］. Applied Energy, 2016(167):135-153.

[1]	ZHANG Zhi-xia, XIE Bao-qiang. Natural Gas Load Forecasting Based on FCGA-LSTM and Transfer Learning [J]. Computer and Modernization, 2023, 0(07): 7-12.
[2]	ZHANG Zi-sen, XU Xiao-zhong. Load Forecasting Based on Decomposition and Multi-component Ensemble Learning [J]. Computer and Modernization, 2023, 0(03): 96-101.
[3]	YIN Chun-jie, XIAO Fa-da, LI Peng-fei, ZHAO Qin. Short-term Load Forecasting of Regional Microgrid Based on LSTM Neural Network [J]. Computer and Modernization, 2022, 0(04): 7-11.
[4]	OUYANG Meng-ke, SHEN Wei-kang, CHENG Hui, SHI Kai. Short-term Load Forecasting Model Based on VMD and MOGOA-LSTM [J]. Computer and Modernization, 2022, 0(03): 7-12.
[5]	GUO Cheng, WANG Xiao, WANG Bo, WANG Jia-fu. A Short-term Power Load Forecasting Method Based on Multi-layer Fusion Neural Network [J]. Computer and Modernization, 2021, 0(10): 94-99.
[6]	WEI Jian , ZHAO Hong-tao , LIU Dun-nan , JIA He-ping . Load Forecasting Method of Ultra Short-Term Based on Integrated Model [J]. Computer and Modernization, 2021, 0(03): 12-17.
[7]	LI Rui, LI Xiao-hui, LI Xiao-yu , DING Yue-min. Prediction of Health Index Based on Improved Non-equidistant Grey Model [J]. Computer and Modernization, 2020, 0(06): 101-.
[8]	YANG Dan, ZANG Lie, GAN Lu. Short-term Electrical Load Forecasting Based on Modified Genetic Algorithm and SVR [J]. Computer and Modernization, 2016, 0(6): 59-62,67.
[9]	WANG Dian;CAI Qiong. Application of Optimum Weighted Combination Method [J]. Computer and Modernization, 2012, 203(7): 188-191.