Open AccessResearch on Short-term Power Load Forecasting Based on Bi-GRU
Author(s) -
Huan He,
Haomiao Wang,
Hongliang Ma,
Xuesong Liu,
Jia Yang,
Gangjun Gong
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1639/1/012017
Subject(s) - computer science , term (time) , relevance (law) , artificial neural network , power (physics) , dimension (graph theory) , power grid , probabilistic forecasting , stability (learning theory) , electric power system , artificial intelligence , reliability engineering , machine learning , data mining , operations research , engineering , physics , mathematics , quantum mechanics , probabilistic logic , political science , pure mathematics , law
The precise, accurate and efficient short-term load forecasting can guide the power supply companies to rationally arrange power dispatch plans, help improve the stability of grid operation, and significantly, improving power utilization, thereby optimizing corporate marketing strategies and increasing corporate economic returns. Short-term load forecasting methods based on deep learning have gained greater attention from academia and power companies. Among them, the load forecasting model based on recurrent neural network has gained excellent forecasting results compared with traditional machine learning models. The advantage of the cyclic neural network is that it can extract the degree of relevance of the data in the time dimension, but the unidirectional network only considers the impact of historical data on the current forecast. This paper proposes a load forecasting model based on a bidirectional gated recurrent unit, which further improves the relevance of data. However, it introduces meteorological factors and the influence of holidays to improve the accuracy of forecast results. Taking the power load data of a district of a city in China as an example, the prediction results of this model meet the actual requirements and show better prediction performance over Bi-LSTM, LSTM, GRU, and other models.