TY - JOUR
T1 - A short-term load forecasting method using integrated CNN and LSTM network
AU - Rafi, Shafiul Hasan
AU - Al-Masood, Nahid
AU - Deeba, Shohana Rahman
AU - Hossain, Eklas
N1 - Publisher Copyright:
© 2021 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Bangladesh power system
KW - Convolutional neural network
KW - Evaluation metrics
KW - Long-short-term memory network
KW - Short-term load forecasting
UR - http://www.scopus.com/inward/record.url?scp=85111947983&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2021.3060654
DO - 10.1109/ACCESS.2021.3060654
M3 - Article
AN - SCOPUS:85111947983
VL - 9
SP - 32436
EP - 32448
JO - IEEE Access
JF - IEEE Access
M1 - 3060654
ER -