Open AccessShort-term Load Forecasting Based on Electricity Price in LSTM in Power Grid
Author(s) -
Ruyi Cai,
Shixin Li,
Jiecai Tian,
Liqiang Ren
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/569/4/042046
Subject(s) - term (time) , electricity , computer science , electricity market , grid , stability (learning theory) , electricity price forecasting , electric power system , artificial neural network , electricity price , power grid , power (physics) , econometrics , artificial intelligence , economics , machine learning , engineering , mathematics , electrical engineering , physics , geometry , quantum mechanics
In the electricity market, accurate short-term load forecasting can ensure the safe and stable operation of the grid, but the real-time fluctuation of electricity price increases the complexity of load changes and increases the difficulty of forecasting. In response to this problem, this paper studies the correlation between electricity price and power load, and provides a basis for the prediction of short-term load in the active distribution network. Based on the correlation between electricity price and power load, this paper proposes a short-term load forecasting model for long-term and short-term memory-cycle neural networks. Taking the power data of a certain area as an example, the LSTM model and other models were used to carry out simulation experiments. The results show that the proposed method outperforms other models in terms of prediction accuracy and stability.