Deep-Learning Prediction Model with Serial Two-Level Decomposition Based on Bayesian Optimization

Xue-Bo Jin,Hong-Xing Wang,Ting-Li Su,Xiao-Yi Wang,Yu-Ting Bai,Jian-Lei Kong

doi:10.1155/2020/4346803

Abstract

The power load prediction is significant in a sustainable power system, which is the key to the energy system’s economic operation. An accurate prediction of the power load can provide a reliable decision for power system planning. However, it is challenging to predict the power load with a single model, especially for multistep prediction, because the time series load data have multiple periods. This paper presents a deep hybrid model with a serial two‐level decomposition structure. First, the power load data are decomposed into components; then, the gated recurrent unit (GRU) network, with the Bayesian optimization parameters, is used as the subpredictor for each component. Last, the predictions of different components are fused to achieve the final predictions. The power load data of American Electric Power (AEP) were used to verify the proposed predictor. The results showed that the proposed prediction method could effectively improve the accuracy of power load prediction.

Highlights

With the rapid development of society, electric power is applied in all aspects of production and life
Due to the nonstorage of power, excess energy will cause a waste of resources, and excessive operation has an impact on the safety of power equipment [1,2,3]. erefore, power load prediction is of considerable significance to power enterprises. e benefits of load prediction include effective planning of annual power supply, reduction of power waste and costs, and development of operation plans
The electric power load data are from American Electric Power Company (AEP), which includes 26,280 data from January 1, 2017, to January 1, 2020

Summary

Introduction

With the rapid development of society, electric power is applied in all aspects of production and life. The seasonal-trend decomposition procedure based on loess (STL) can obtain trend, seasonal, and residual components of the complex data [27], which have been used as a hybrid prediction in the authors’ former research for weather forecasting [8, 28] Another decomposition method named as wavelet decomposition, Wang et al [29], decomposed the original time series to construct the predictor for different subsignals. [34,35,36] decomposed the original data based on a wavelet algorithm and predicted the components by a particle swarm optimization (PSO) neural network Another optimization method named as the fruit fly optimization algorithm (FOA) is used to select parameters for the generalized regression neural network (GRNN) [37]. Is paper uses a serial two-level decomposition structure to improve the prediction performance due to the complexity of multiple periods of the power load data. The results of each submodel are fused to get the final prediction results

Result

Experiment Results and Discussion

Conclusions