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

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.

[1]  Yanhong Luo,et al.  Wind Power Prediction Based on LSTM Networks and Nonparametric Kernel Density Estimation , 2019, IEEE Access.

[2]  Pierluigi Siano,et al.  A Novel RBF Training Algorithm for Short-Term Electric Load Forecasting and Comparative Studies , 2015, IEEE Transactions on Industrial Electronics.

[3]  Suleyman Serdar Kozat,et al.  Nonuniformly Sampled Data Processing Using LSTM Networks , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[4]  Rosalind W. Picard,et al.  Multimodal Ambulatory Sleep Detection Using LSTM Recurrent Neural Networks , 2019, IEEE Journal of Biomedical and Health Informatics.

[5]  Jun Hu,et al.  Short-Term Load Forecasting With Deep Residual Networks , 2018, IEEE Transactions on Smart Grid.

[6]  K. P. Soman,et al.  A data-driven strategy for short-term electric load forecasting using dynamic mode decomposition model , 2018, Applied Energy.

[7]  Suleyman S. Kozat,et al.  Online Training of LSTM Networks in Distributed Systems for Variable Length Data Sequences , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[8]  Ljupco Kocarev,et al.  Deep belief network based electricity load forecasting: An analysis of Macedonian case , 2016 .

[9]  Tao Ding,et al.  Hybrid method for short‐term photovoltaic power forecasting based on deep convolutional neural network , 2018, IET Generation, Transmission & Distribution.

[10]  Heng Tao Shen,et al.  Video Captioning With Attention-Based LSTM and Semantic Consistency , 2017, IEEE Transactions on Multimedia.

[11]  Mohamed Abdel-Nasser,et al.  Accurate photovoltaic power forecasting models using deep LSTM-RNN , 2017, Neural Computing and Applications.

[12]  Shiwen Mao,et al.  LASSO and LSTM Integrated Temporal Model for Short-Term Solar Intensity Forecasting , 2019, IEEE Internet of Things Journal.

[13]  Heng Huang,et al.  Using Smart Meter Data to Improve the Accuracy of Intraday Load Forecasting Considering Customer Behavior Similarities , 2015, IEEE Transactions on Smart Grid.

[14]  Ying-Yi Hong,et al.  Day-Ahead Solar Irradiation Forecasting Utilizing Gramian Angular Field and Convolutional Long Short-Term Memory , 2020, IEEE Access.

[15]  Yunjun Yu,et al.  An LSTM Short-Term Solar Irradiance Forecasting Under Complicated Weather Conditions , 2019, IEEE Access.

[16]  Canbing Li,et al.  Distributed Multi-Energy Operation of Coupled Electricity, Heating, and Natural Gas Networks , 2020, IEEE Transactions on Sustainable Energy.

[17]  Vladimir Ceperic,et al.  A Strategy for Short-Term Load Forecasting by Support Vector Regression Machines , 2013, IEEE Transactions on Power Systems.

[18]  Heng Tao Shen,et al.  Video Captioning by Adversarial LSTM , 2018, IEEE Transactions on Image Processing.

[19]  Zutao Zhang,et al.  Using Appearance to Predict Pedestrian Trajectories Through Disparity-Guided Attention and Convolutional LSTM , 2021, IEEE Transactions on Vehicular Technology.

[20]  Ping-Huan Kuo,et al.  Multiple-Input Deep Convolutional Neural Network Model for Short-Term Photovoltaic Power Forecasting , 2019, IEEE Access.

[21]  M. Omar Faruque,et al.  Forecasting of PV plant output using hybrid wavelet‐based LSTM‐DNN structure model , 2019, IET Renewable Power Generation.

[22]  Feng Zheng,et al.  Improved Deep Belief Network for Short-Term Load Forecasting Considering Demand-Side Management , 2020, IEEE Transactions on Power Systems.

[23]  Suleyman Serdar Kozat,et al.  Efficient Online Learning Algorithms Based on LSTM Neural Networks , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[24]  Amanpreet Kaur,et al.  Net load forecasting for high renewable energy penetration grids , 2016 .

[25]  Jürgen Schmidhuber,et al.  LSTM: A Search Space Odyssey , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[26]  S. A. Soliman,et al.  Short-term electric load forecasting based on Kalman filtering algorithm with moving window weather and load model , 2004 .

[27]  Ting Wang,et al.  Short‐term power load forecasting based on multi‐layer bidirectional recurrent neural network , 2019, IET Generation, Transmission & Distribution.

[28]  Wai Lok Woo,et al.  Load Disaggregation Using One-Directional Convolutional Stacked Long Short-Term Memory Recurrent Neural Network , 2020, IEEE Systems Journal.