Research on Short-Term Load Prediction Based on Seq2seq Model

Gangjun Gong,Xiaonan An,Shuyan Sun,Nawaraj Kumar Mahato,Si Chen,Yafeng Wen

doi:10.3390/en12163199

Abstract

Electricity load prediction is the primary basis on which power-related departments to make logical and effective generation plans and scientific scheduling plans for the most effective power utilization. The perpetual evolution of deep learning has recommended advanced and innovative concepts for short-term load prediction. Taking into consideration the time and nonlinear characteristics of power system load data and further considering the impact of historical and future information on the current state, this paper proposes a Seq2seq short-term load prediction model based on a long short-term memory network (LSTM). Firstly, the periodic fluctuation characteristics of users’ load data are analyzed, establishing a correlation of the load data so as to determine the model’s order in the time series. Secondly, the specifications of the Seq2seq model are given preference and a coalescence of the Residual mechanism (Residual) and the two Attention mechanisms (Attention) is developed. Then, comparing the predictive performance of the model under different types of Attention mechanism, this paper finally adopts the Seq2seq short-term load prediction model of Residual LSTM and the Bahdanau Attention mechanism. Eventually, the prediction model obtains better results when merging the actual power system load data of a certain place. In order to validate the developed model, the Seq2seq was compared with recurrent neural network (RNN), LSTM, and gated recurrent unit (GRU) algorithms. Last but not least, the performance indices were calculated. when training and testing the model with power system load data, it was noted that the root mean square error (RMSE) of Seq2seq was decreased by 6.61%, 16.95%, and 7.80% compared with RNN, LSTM, and GRU, respectively. In addition, a supplementary case study was carried out using data for a small power system considering different weather conditions and user behaviors in order to confirm the applicability and stability of the proposed model. The Seq2seq model for short-term load prediction can be reported to demonstrate superiority in all areas, exhibiting better prediction and stable performance.

Highlights

Short-term load forecasting has an important impact on major decisions, such as the day-to-day operation of the power grid and dispatch planning [1]
In order to validate the developed model, the Seq2seq was compared with recurrent neural network (RNN), long short-term memory network (LSTM), and gated recurrent unit (GRU) algorithms
The performance indices were calculated. when training and testing the model with power system load data, it was noted that the root mean square error (RMSE) of Seq2seq was decreased by 6.61%, 16.95%, and 7.80% compared with RNN, LSTM, and GRU, respectively

Summary

Introduction

Short-term load forecasting has an important impact on major decisions, such as the day-to-day operation of the power grid and dispatch planning [1]. The implementation principle of these methods is simple, fast operation speeds that are suitable for processing data characteristics of a single and small data set. Despite their nonlinear characteristics, large data volumes, lack of robustness, and poor adaptability, modern load prediction methods mainly include. This research study was carried out on a personal computer with a single CPU of 2.6 GHz and. The process was done using Python 3.6.8, and the TensorFlow deep learning framework by Google This researchdeveloped study was carried out on a personal computer with a single CPU of 2.6 GHz and of 18 this theThe New York State.

Methods

Results

Conclusion