Multi-convolution feature extraction and recurrent neural network dependent model for short-term load forecasting

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

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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.

Item Type: Article
Uncontrolled Keywords: Short-term load forecast; deep learning; multi-head CNN-LSTM; multi-step load prediction
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK452-454.4 Electric apparatus and materials. Electric circuits. Electric networks
Divisions: Faculty of Technology Management and Business > Department of Construction Management
Depositing User: Mr. Abdul Rahim Mat Radzuan
Date Deposited: 28 Jan 2022 00:06
Last Modified: 28 Jan 2022 00:06

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