Open AccessAn attention‐based CNN‐LSTM‐BiLSTM model for short‐term electric load forecasting in integrated energy system
Author(s) -
Wu Kuihua,
Wu Jian,
Feng Liang,
Yang Bo,
Liang Rong,
Yang Shenquan,
Zhao Ren
Publication year - 2021
Publication title -
international transactions on electrical energy systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.428
H-Index - 42
ISSN - 2050-7038
DOI - 10.1002/2050-7038.12637
Subject(s) - computer science , artificial neural network , backpropagation , support vector machine , artificial intelligence , electrical load , energy (signal processing) , electric power system , complementarity (molecular biology) , term (time) , convolutional neural network , power (physics) , engineering , voltage , statistics , physics , mathematics , quantum mechanics , biology , electrical engineering , genetics
In recent years, diverse energy has been integrated into the power system, which constitutes a regional integrated energy system (IES). However, the coupling and complementation of multiple energy sources make load forecasting more difficult. For the time‐sequence and non‐linear characteristics of electric load and the complementarity of different energy in IES, this paper proposed an attention‐based convolutional neural network (CNN) combined with long short‐term memory (LSTM) and bidirectional long short‐term memory (BiLSTM) model for short‐term load forecasting in IES. The historical load, temperature, cooling load, and gas consumption of the past 5 days are used as the input features. CNN integrated with attention block is utilized to extract effective features of the load impact factors. Then the load of the next hour is forecasted by the LSTM combined with BiLSTM layers. Finally, the model is verified by the data from an integrated energy park in North China. The results show that the proposed method has better forecasting performance than CNN‐BiLSTM, CNN‐LSTM, BiLSTM, LSTM, backpropagation neural network (BPNN), random forest regression (RFR), and support vector machine regression (SVR).