Multi-convolution feature extraction and recurrent neural network dependent model for short-term load forecasting
Load forecasting is critical for power system operation and market planning.With the increased penetration of renewable energy and the massive consumption of electric energy, improving load forecasting accuracy has become a dif�cult task. Recently, it was demonstrated that deep learning models perfo...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Institute of Electrical and Electronics Engineers
2021
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/6263/ http://eprints.uthm.edu.my/6263/1/J13190_7ec3c064e09d1a741eeb13c13aa40aaf.pdf |
| _version_ | 1848888760476893184 |
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| author | Goh, Hui Hwang He, Biliang Hui Liu, Hui Liu Zhang, Dongdong Wei Dai, Wei Dai Kurniawan, Tonni Agustiono Goh, Kai Chen |
| author_facet | Goh, Hui Hwang He, Biliang Hui Liu, Hui Liu Zhang, Dongdong Wei Dai, Wei Dai Kurniawan, Tonni Agustiono Goh, Kai Chen |
| author_sort | Goh, Hui Hwang |
| building | UTHM Institutional Repository |
| collection | Online Access |
| description | Load forecasting is critical for power system operation and market planning.With the increased penetration of renewable energy and the massive consumption of electric energy, improving load forecasting accuracy has become a dif�cult task. Recently, it was demonstrated that deep learning models perform well for short-term load forecasting (STLF). However, prior research has demonstrated that the hybrid deep learning model outperforms the single model. We propose a hybrid neural network in this article that combines elements of a convolutional neural network (1D-CNN) and a long short memory network (LSTM) in novel ways. Multiple independent 1D-CNNs are used to extract load, calendar, and weather features from the proposed hybrid model, while LSTM is used to learn time patterns. This architecture is referred to as a CNN-LSTM network with multiple heads (MCNN-LSTM). To demonstrate the proposed hybrid deep learning model's superior performance, the proposed method is applied to Ireland's load data for single-step and multi-step load forecasting. In comparison to the widely used CNN-LSTM hybrid model, the proposed model improved single-step prediction by 16.73% and 24-step load prediction by 20.33%. Additionally, we use the Maine dataset to verify the proposed model's generalizability. |
| first_indexed | 2025-11-15T20:15:24Z |
| format | Article |
| id | uthm-6263 |
| institution | Universiti Tun Hussein Onn Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T20:15:24Z |
| publishDate | 2021 |
| publisher | Institute of Electrical and Electronics Engineers |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uthm-62632022-01-28T00:06:18Z http://eprints.uthm.edu.my/6263/ Multi-convolution feature extraction and recurrent neural network dependent model for short-term load forecasting Goh, Hui Hwang He, Biliang Hui Liu, Hui Liu Zhang, Dongdong Wei Dai, Wei Dai Kurniawan, Tonni Agustiono Goh, Kai Chen TK452-454.4 Electric apparatus and materials. Electric circuits. Electric networks Load forecasting is critical for power system operation and market planning.With the increased penetration of renewable energy and the massive consumption of electric energy, improving load forecasting accuracy has become a dif�cult task. Recently, it was demonstrated that deep learning models perform well for short-term load forecasting (STLF). However, prior research has demonstrated that the hybrid deep learning model outperforms the single model. We propose a hybrid neural network in this article that combines elements of a convolutional neural network (1D-CNN) and a long short memory network (LSTM) in novel ways. Multiple independent 1D-CNNs are used to extract load, calendar, and weather features from the proposed hybrid model, while LSTM is used to learn time patterns. This architecture is referred to as a CNN-LSTM network with multiple heads (MCNN-LSTM). To demonstrate the proposed hybrid deep learning model's superior performance, the proposed method is applied to Ireland's load data for single-step and multi-step load forecasting. In comparison to the widely used CNN-LSTM hybrid model, the proposed model improved single-step prediction by 16.73% and 24-step load prediction by 20.33%. Additionally, we use the Maine dataset to verify the proposed model's generalizability. Institute of Electrical and Electronics Engineers 2021 Article PeerReviewed text en http://eprints.uthm.edu.my/6263/1/J13190_7ec3c064e09d1a741eeb13c13aa40aaf.pdf Goh, Hui Hwang and He, Biliang and Hui Liu, Hui Liu and Zhang, Dongdong and Wei Dai, Wei Dai and Kurniawan, Tonni Agustiono and Goh, Kai Chen (2021) Multi-convolution feature extraction and recurrent neural network dependent model for short-term load forecasting. IEEE Access, 9. pp. 118528-118540. ISSN 2169-3536 |
| spellingShingle | TK452-454.4 Electric apparatus and materials. Electric circuits. Electric networks Goh, Hui Hwang He, Biliang Hui Liu, Hui Liu Zhang, Dongdong Wei Dai, Wei Dai Kurniawan, Tonni Agustiono Goh, Kai Chen Multi-convolution feature extraction and recurrent neural network dependent model for short-term load forecasting |
| title | Multi-convolution feature extraction and recurrent neural network dependent model for short-term load forecasting |
| title_full | Multi-convolution feature extraction and recurrent neural network dependent model for short-term load forecasting |
| title_fullStr | Multi-convolution feature extraction and recurrent neural network dependent model for short-term load forecasting |
| title_full_unstemmed | Multi-convolution feature extraction and recurrent neural network dependent model for short-term load forecasting |
| title_short | Multi-convolution feature extraction and recurrent neural network dependent model for short-term load forecasting |
| title_sort | multi-convolution feature extraction and recurrent neural network dependent model for short-term load forecasting |
| topic | TK452-454.4 Electric apparatus and materials. Electric circuits. Electric networks |
| url | http://eprints.uthm.edu.my/6263/ http://eprints.uthm.edu.my/6263/1/J13190_7ec3c064e09d1a741eeb13c13aa40aaf.pdf |