Open AccessPower load forecasting research based on neural network and Holt-winters method
Author(s) -
JianJun Fan,
Xinzhong Liu,
Zhimin Li,
Xinku Wang,
Shengnan Cao,
Jiakun Lei
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/692/2/022120
Subject(s) - artificial neural network , smoothing , exponential smoothing , computer science , electric power system , electrical load , power (physics) , seasonality , series (stratigraphy) , data mining , econometrics , algorithm , artificial intelligence , machine learning , mathematics , paleontology , physics , quantum mechanics , computer vision , biology
Since the electric load shows a very obvious periodicity in time series, increasing the periodic factor in the power load forecasting is a research direction of power load forecasting. It is by adding trend and seasonality (that is, periodicity) to smoothing values that the Holt-winters algorithm improves the accuracy of predictions. In this paper, Holt-winters algorithm and neural network algorithm are used to build power load prediction models respectively, and data of city A in Shandong province is used for testing. Experimental results show that the prediction results of the two algorithms are similar, but the Holt-winters algorithm is slightly more accurate.