Original scientific paper
https://doi.org/10.20532/cit.2024.1005783
Short-Term Power Load Forecasting Method Based on GRU-Transformer Combined Neural Network Model
Weiwei Mao
; College of Computer and Information Engineering, Luoyang Institute of Science and Technology, Luoyang, China
Suping Yu
; College of Computer and Information Engineering, Luoyang Institute of Science and Technology, Luoyang, China
*
Wenqing Chen
; School of Electrical Engineering and Automation, Luoyang Institute of Technology, Luoyang, China
* Corresponding author.
Abstract
Load Forecast (LF) is an important task in the planning, control and application of public power systems. Accurate Short Term Load Forecast (STLF) is the premise of safe and economical operation of a power system. In the research of short-term power load forecasting, machine learning and deep learning are the most popular methods at present, but there still exists a problem that the single and simple structure of power load forecasting model leads to low accuracy of load forecasting. In order to improve the accuracy of STLF, a Gated Cycle Unit (GRU)-Transformer combined neural network model is proposed. Transformer encoder structure is used as feature extractor to mine the complex mapping relationships between the input features and load. The advantage of self-attention mechanism is used to solve the problem of information loss of long sequences in short-term power load forecasting. At the same time, the multivariate time series model of GRU is used for model training. The experimental results on the power load data set of a certain region in southwest China and Panama City show that the proposed combined model prediction method has higher accuracy than those proposed in other literatures, which further proves its feasibility and superiority.
Keywords
short-term power load forecasting; self-attention mechanism; multivariate time series; gated recurrent unit; feature extractor
Hrčak ID:
319260
URI
Publication date:
15.7.2024.
Visits: 0 *