Multi-Step Short-Term Power Consumption Forecasting Using Multi-Channel LSTM With Time Location Considering Customer Behavior

Short-Term Load Forecasting (STLF) is one critical assignment regarding the power supply and demand in the smart grid. Multi-step STLF provides strong evidence for decision-making to achieve consistent, quick supply and reduce direct or indirect cost. However, most of the current research only focuses on one-step STLF, which cannot satisfy the human-beings needs. Besides, short-term consumption fluctuates significantly in different periods and people, which increases the difficulty of forecasting. In this paper, we present a novel deep model named multi-channel long short-term memory (LSTM) with time location (TL-MCLSTM) in a multiple output strategy to forecast the multi-step short-term power consumption. The proposed model contains three channels: power consumption, time location, and customer behavior channels, respectively. Power consumption channel reflects the change and general trend of use; Time location channel reflects the hidden pattern of customer habits, which records the information consisting of time, day of the week, holidays. Moreover, we combine a convolution autoencoder and k-means to identify the type of behavior at the customer behavior channel. Power consumption and time location channels are trained individually through the LSTM as it has excellent memory function. Extracted features from LSTM in power consumption and time location channels are combined with customer behavior as comprehensive features to forecast. We designed, trained, and verified our proposed deep model on two nature data sets, and compared with other leading deep learning-based methods. The comparative studies have confirmed the effectiveness and priority of TL-MCLSTM for multi-step short-term consumption forecasting.

[1]  Li Han,et al.  Multi‐step wind power forecast based on VMD‐LSTM , 2019, IET Renewable Power Generation.

[2]  Ying Liu,et al.  Comparative analysis of the outcomes of differing time series forecasting strategies , 2017, 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD).

[3]  Hong Ji,et al.  Nonpooling Convolutional Neural Network Forecasting for Seasonal Time Series With Trends , 2020, IEEE Transactions on Neural Networks and Learning Systems.

[4]  Yang Du,et al.  A Hybrid LSTM Neural Network for Energy Consumption Forecasting of Individual Households , 2019, IEEE Access.

[5]  Jing Liu,et al.  Multi-step ahead forecasting of heat load in district heating systems using machine learning algorithms , 2019 .

[6]  Oliver Kramer,et al.  Comparing support vector regression for PV power forecasting to a physical modeling approach using measurement, numerical weather prediction, and cloud motion data , 2016 .

[7]  J. Contreras,et al.  ARIMA Models to Predict Next-Day Electricity Prices , 2002, IEEE Power Engineering Review.

[8]  Mohsen Guizani,et al.  Deep Features Learning for Medical Image Analysis with Convolutional Autoencoder Neural Network , 2017 .

[9]  Mohammad Yusri Hassan,et al.  A review on applications of ANN and SVM for building electrical energy consumption forecasting , 2014 .

[10]  Jiro Katto,et al.  Deep Convolutional AutoEncoder-based Lossy Image Compression , 2018, 2018 Picture Coding Symposium (PCS).

[11]  Zhuofu Deng,et al.  Multi-Scale Convolutional Neural Network With Time-Cognition for Multi-Step Short-Term Load Forecasting , 2019, IEEE Access.

[12]  David J. Hill,et al.  Short-Term Residential Load Forecasting Based on Resident Behaviour Learning , 2018, IEEE Transactions on Power Systems.

[13]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Virginijus Radziukynas,et al.  Short-term wind speed forecasting with ARIMA model , 2014, 2014 55th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON).

[15]  Xiaorui Shao,et al.  Accurate Deep Model for Electricity Consumption Forecasting Using Multi-channel and Multi-Scale Feature Fusion CNN–LSTM , 2020 .

[16]  Jian Ma,et al.  A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network , 2018, Energies.

[17]  Fei-Yue Wang,et al.  Forecasting Horticultural Products Price Using ARIMA Model and Neural Network Based on a Large-Scale Data Set Collected by Web Crawler , 2019, IEEE Transactions on Computational Social Systems.

[18]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[19]  Ying Liu,et al.  Multi-step Time Series Forecasting of Electric Load Using Machine Learning Models , 2018, ICAISC.

[20]  Ping-Huan Kuo,et al.  A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting , 2018 .

[21]  Haiyan Wu,et al.  A Clustering Method Based on K-Means Algorithm , 2012 .

[22]  Mianxiong Dong,et al.  Everything is Image: CNN-based Short-Term Electrical Load Forecasting for Smart Grid , 2017, 2017 14th International Symposium on Pervasive Systems, Algorithms and Networks & 2017 11th International Conference on Frontier of Computer Science and Technology & 2017 Third International Symposium of Creative Computing (ISPAN-FCST-ISCC).

[23]  Yu Tsao,et al.  Speech enhancement based on deep denoising autoencoder , 2013, INTERSPEECH.

[24]  Duong Tuan Anh,et al.  Comparison of Strategies for Multi-step-Ahead Prediction of Time Series Using Neural Network , 2015, 2015 International Conference on Advanced Computing and Applications (ACOMP).

[25]  Yuan Zhang,et al.  Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network , 2019, IEEE Transactions on Smart Grid.

[26]  Ran Li,et al.  Deep Learning for Household Load Forecasting—A Novel Pooling Deep RNN , 2018, IEEE Transactions on Smart Grid.

[27]  Piergiuseppe Di Marco,et al.  Fault Detection and Diagnosis Using Combined Autoencoder and Long Short-Term Memory Network , 2019, Sensors.

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

[29]  Ning Jin,et al.  Multi-Step Short-Term Power Consumption Forecasting with a Hybrid Deep Learning Strategy , 2018, Energies.

[30]  Qinghua Hu,et al.  Transfer learning for short-term wind speed prediction with deep neural networks , 2016 .

[31]  Sijie CHEN,et al.  From demand response to transactive energy: state of the art , 2017 .

[32]  Zachary Chase Lipton.  A Critical Review of Recurrent Neural Networks for Sequence Learning , 2015, ArXiv.

[33]  Lei Huang,et al.  A CNN based bagging learning approach to short-term load forecasting in smart grid , 2017, 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI).

[34]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.