Open AccessA Short-Term Electricity Forecasting Scheme Based on Combined GRU Model with STL Decomposition
Author(s) -
Yu Tian,
Shengjie Zhou,
Musen Wen,
J G Li
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/701/1/012008
Subject(s) - computer science , smart grid , decomposition , autoregressive model , term (time) , grid , electric power system , scheme (mathematics) , electricity , data mining , key (lock) , time series , autoregressive integrated moving average , artificial neural network , power (physics) , artificial intelligence , machine learning , engineering , econometrics , mathematics , ecology , mathematical analysis , physics , geometry , computer security , quantum mechanics , electrical engineering , biology
With development of smart grid, the stable operation of grid has put forward higher requirements for system dispatch. In particular, short-term load forecasting of power systems is a key factor of power grid management systems, which is related to the safety, economy, and stable operation of the smart grid. However, short-term electricity forecasting is affected by many external factors. It has complex characteristics, especially non-linear relationships, so it cannot be accurately predicted. Recently, Recurrent Neural Network based models have good performance in electricity forecasting because of their excellent ability to capture non-linear relationships. However, they cannot fully capture historical information, especially local historical information, which has an impact on prediction accuracy. In order to address these problems, we propose a scheme by combining STL decomposition and GRU model. Specifically, we first decompose the original time series into three different components by STL. The decomposition results are separately imported into the main prediction module, which uses two GRU networks with different structures to obtain the local and global dependencies of the data. We also add an autoregressive method to make the model more robust. The proposed scheme is validated based on real-world data, and the simulation results show that our proposed method can perfectly capture local and global information and achieve higher prediction accuracy than traditional models.