Open AccessResearch on Short-term Power Load Forecasting Method Based on Temperature Accumulation Effect Embedded in Time Series
Author(s) -
Duan Jinchang,
Zhiyong Li,
Haicheng Yao,
Bao Li,
Qiang Zhou
Publication year - 2020
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/738/1/012036
Subject(s) - term (time) , series (stratigraphy) , time series , computer science , power (physics) , econometrics , environmental science , mathematics , machine learning , physics , thermodynamics , geology , paleontology , quantum mechanics
Short-term power load forecasting is an important link in power grid dispatching, which has an important impact on unit combination, economic dispatching, optimal power flow, etc. As meteorological factors have great influence on load, the influence of meteorological factors should be considered reasonably in short-term load forecasting. The accuracy of short-term electric charge prediction results can help senior personnel of the power system to make accurate and feasible power operation methods. In order to ensure the safe and stable operation of power grid in various special periods and to ensure the economic benefits of related power enterprises to the greatest extent, it is imperative to establish a highly accurate prediction model. The accuracy of power load forecasting will directly affect the position of each power enterprise in the market. Based on the function of time series embedding, this paper analyzes the rule of cumulative effect on load. The correlation between temperature and load is greatly improved after considering the cumulative effect to deal with temperature correction.