A Short-Term Load Forecasting Method Using Integrated CNN and LSTM Network

In this study, a new technique is proposed to forecast short-term electrical load. Load forecasting is an integral part of power system planning and operation. Precise forecasting of load is essential for unit commitment, capacity planning, network augmentation and demand side management. Load forec...

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Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.32436-32448
Main Authors: Rafi, Shafiul Hasan, Nahid-Al-Masood, Deeba, Shohana Rahman, Hossain, Eklas
Format: Article
Language:English
Subjects:
Bangladesh power system
Computer architecture
convolutional neural network
evaluation metrics
Forecasting
Load forecasting
Load modeling
Logic gates
long-short-term memory network
Power systems
Short-term load forecasting
Time series analysis
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Summary:In this study, a new technique is proposed to forecast short-term electrical load. Load forecasting is an integral part of power system planning and operation. Precise forecasting of load is essential for unit commitment, capacity planning, network augmentation and demand side management. Load forecasting can be generally categorized into three classes such as short-term, midterm and long-term. Short-term forecasting is usually done to predict load for next few hours to few weeks. In the literature, various methodologies such as regression analysis, machine learning approaches, deep learning methods and artificial intelligence systems have been used for short-term load forecasting. However, existing techniques may not always provide higher accuracy in short-term load forecasting. To overcome this challenge, a new approach is proposed in this paper for short-term load forecasting. The developed method is based on the integration of convolutional neural network (CNN) and long short-term memory (LSTM) network. The method is applied to Bangladesh power system to provide short-term forecasting of electrical load. Also, the effectiveness of the proposed technique is validated by comparing the forecasting errors with that of some existing approaches such as long short-term memory network, radial basis function network and extreme gradient boosting algorithm. It is found that the proposed strategy results in higher precision and accuracy in short-term load forecasting.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3060654