IEICE TRANSACTIONS on Information
Cross-Domain Energy Consumption Prediction via ED-LSTM Networks
Summary :
As an important type of science and technology service resource, energy consumption data play a vital role in the process of value chain integration between home appliance manufacturers and the state grid. Accurate electricity consumption prediction is essential for demand response programs in smart grid planning. The vast majority of existing prediction algorithms only exploit data belonging to a single domain, i.e., historical electricity load data. However, dependencies and correlations may exist among different domains, such as the regional weather condition and local residential/industrial energy consumption profiles. To take advantage of cross-domain resources, a hybrid energy consumption prediction framework is presented in this paper. This framework combines the long short-term memory model with an encoder-decoder unit (ED-LSTM) to perform sequence-to-sequence forecasting. Extensive experiments are conducted with several of the most commonly used algorithms over integrated cross-domain datasets. The results indicate that the proposed multistep forecasting framework outperforms most of the existing approaches.
- Publication
- IEICE TRANSACTIONS on Information Vol.E104-D No.8 pp.1204-1213
- Publication Date
- 2021/08/01
- Publicized
- 2021/05/11
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2020BDP0006
- Type of Manuscript
- Special Section PAPER (Special Section on Computational Intelligence and Big Data for Scientific and Technological Resources and Services)
- Category
Authors
Ye TAO
Qingdao University of Science and Technology
Fang KONG
Qingdao University of Science and Technology
Wenjun JU
Haier Technology Co., Ltd.
Hui LI
Qingdao University of Science and Technology
Ruichun HOU
Ocean University of China
Keyword
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Ye TAO, Fang KONG, Wenjun JU, Hui LI, Ruichun HOU, "Cross-Domain Energy Consumption Prediction via ED-LSTM Networks" in IEICE TRANSACTIONS on Information,
vol. E104-D, no. 8, pp. 1204-1213, August 2021, doi: 10.1587/transinf.2020BDP0006.
Abstract: As an important type of science and technology service resource, energy consumption data play a vital role in the process of value chain integration between home appliance manufacturers and the state grid. Accurate electricity consumption prediction is essential for demand response programs in smart grid planning. The vast majority of existing prediction algorithms only exploit data belonging to a single domain, i.e., historical electricity load data. However, dependencies and correlations may exist among different domains, such as the regional weather condition and local residential/industrial energy consumption profiles. To take advantage of cross-domain resources, a hybrid energy consumption prediction framework is presented in this paper. This framework combines the long short-term memory model with an encoder-decoder unit (ED-LSTM) to perform sequence-to-sequence forecasting. Extensive experiments are conducted with several of the most commonly used algorithms over integrated cross-domain datasets. The results indicate that the proposed multistep forecasting framework outperforms most of the existing approaches.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2020BDP0006/_p
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@ARTICLE{e104-d_8_1204,
author={Ye TAO, Fang KONG, Wenjun JU, Hui LI, Ruichun HOU, },
journal={IEICE TRANSACTIONS on Information},
title={Cross-Domain Energy Consumption Prediction via ED-LSTM Networks},
year={2021},
volume={E104-D},
number={8},
pages={1204-1213},
abstract={As an important type of science and technology service resource, energy consumption data play a vital role in the process of value chain integration between home appliance manufacturers and the state grid. Accurate electricity consumption prediction is essential for demand response programs in smart grid planning. The vast majority of existing prediction algorithms only exploit data belonging to a single domain, i.e., historical electricity load data. However, dependencies and correlations may exist among different domains, such as the regional weather condition and local residential/industrial energy consumption profiles. To take advantage of cross-domain resources, a hybrid energy consumption prediction framework is presented in this paper. This framework combines the long short-term memory model with an encoder-decoder unit (ED-LSTM) to perform sequence-to-sequence forecasting. Extensive experiments are conducted with several of the most commonly used algorithms over integrated cross-domain datasets. The results indicate that the proposed multistep forecasting framework outperforms most of the existing approaches.},
keywords={},
doi={10.1587/transinf.2020BDP0006},
ISSN={1745-1361},
month={August},}
Copy
TY - JOUR
TI - Cross-Domain Energy Consumption Prediction via ED-LSTM Networks
T2 - IEICE TRANSACTIONS on Information
SP - 1204
EP - 1213
AU - Ye TAO
AU - Fang KONG
AU - Wenjun JU
AU - Hui LI
AU - Ruichun HOU
PY - 2021
DO - 10.1587/transinf.2020BDP0006
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E104-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2021
AB - As an important type of science and technology service resource, energy consumption data play a vital role in the process of value chain integration between home appliance manufacturers and the state grid. Accurate electricity consumption prediction is essential for demand response programs in smart grid planning. The vast majority of existing prediction algorithms only exploit data belonging to a single domain, i.e., historical electricity load data. However, dependencies and correlations may exist among different domains, such as the regional weather condition and local residential/industrial energy consumption profiles. To take advantage of cross-domain resources, a hybrid energy consumption prediction framework is presented in this paper. This framework combines the long short-term memory model with an encoder-decoder unit (ED-LSTM) to perform sequence-to-sequence forecasting. Extensive experiments are conducted with several of the most commonly used algorithms over integrated cross-domain datasets. The results indicate that the proposed multistep forecasting framework outperforms most of the existing approaches.
ER -
English
