Short-Term Load Forecasting Based on Deep Learning Bidirectional LSTM Neural Network

Changchun Cai,Zhixiang Deng,Yuan Tao,Tianqi Zhu

doi:10.3390/app11178129

Changchun Cai, Zhixiang Deng + Show 2 more

Open Access

https://doi.org/10.3390/app11178129

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Journal: Applied Sciences	Publication Date: Sep 1, 2021
Citations: 21	License type: CC BY 4.0

Affiliation: Hohai University

Abstract

Accurate load forecasting guarantees the stable and economic operation of power systems. With the increasing integration of distributed generations and electrical vehicles, the variability and randomness characteristics of individual loads and the distributed generation has increased the complexity of power loads in power systems. Hence, accurate and robust load forecasting results are becoming increasingly important in modern power systems. The paper presents a multi-layer stacked bidirectional long short-term memory (LSTM)-based short-term load forecasting framework; the method includes neural network architecture, model training, and bootstrapping. In the proposed method, reverse computing is combined with forward computing, and a feedback calculation mechanism is designed to solve the coupling of before and after time-series information of the power load. In order to improve the convergence of the algorithm, deep learning training is introduced to mine the correlation between historical loads, and the multi-layer stacked style of the network is established to manage the power load information. Finally, actual data are applied to test the proposed method, and a comparison of the results of the proposed method with different methods shows that the proposed method can extract dynamic features from the data as well as make accurate predictions, and the availability of the proposed method is verified with real operational data.

Highlights

A reliable and accuracy short-term load forecasting system is the basis of energy trade between the customers and electrical utility companies [1,2]
mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE) are common indicators to evaluate the accuracy of the proposed model based on the measurement value and estimated value
It can be seen that the multilayer stacked bidirectional long short-term memory (LSTM) neural network will be more competitive, and the error comparison of those methods is shown in Table 3, where the MAPE, RMSE, and MAE

Summary

Introduction

A reliable and accuracy short-term load forecasting system is the basis of energy trade between the customers and electrical utility companies [1,2]. With the increasing penetration of distributed generations and consumer energy systems, the randomness and variability of load profiles bring more challenges for short-term load forecasting systems. Different types of neural networks such as back propagation (BP) [5], radial basis function (RBF) [6], and extreme learning machines (ELM) [7,8] have been proposed and applied in short-term load forecasting. A regularizing term and the combination of multiple ELM is added to reduce the randomness of traditional ELM in photovoltaic power forecasting in [8]. Low convergence speed is always an obstacle to the large-scale application of neural networks

Methods

Results

Conclusion