A Hybrid Channel-Communication-Enabled CNN-LSTM Model for Electricity Load Forecasting

Faisal Saeed, Anand Paul and Hyuncheol Seo
Additional contact information
Faisal Saeed: Department of Computer Science and Engineering, Kyungpook National University, Buk-gu, Daegu 41566, Korea
Anand Paul: Department of Computer Science and Engineering, Kyungpook National University, Buk-gu, Daegu 41566, Korea
Hyuncheol Seo: School of Architectural, Civil, Environmental and Energy Engineering, Kyungpook National University, Daegu 41566, Korea

Energies, 2022, vol. 15, issue 6, 1-17

Abstract: Smart grids provide a unique platform to the participants of energy markets to tweak their offerings based on demand-side management. Responding quickly to the needs of the market can help to improve the reliability of the system, as well as the cost of capital investments. Electric load forecasting is important because it is used to make and run decisions about the power grid. However, people use electricity in nonlinear ways, which makes the electric load profile a complicated signal. Even though there has been a lot of research done in this field, an accurate forecasting model is still needed. In this regard, this article proposed a hybrid cross-channel-communication (C3)-enabled CNN-LSTM model for accurate load forecasting which helps decision making in smart grids. The proposed model is the combination of three different models, i.e., a C3 block to enable channel communication of a CNN (convolutional neural networks) model, two convolutional layers to extract the features and an LSTM (long short-term memory network) model for forecasting. In the proposed hybrid model, Leaky ReLu (rectified linear unit) was used as activation function instead of sigmoid. The channel communication in CNN model makes the proposed model very light and efficient. Extensive experimentation was done on electricity load data. The results show the model’s high efficiency. The proposed model shows 98.3% accuracy and 0.4560 MAPE error.

Keywords: cross-channel communication; Convolutional Neural Networks; LSTM; electricity; load; forecasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/6/2263/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/6/2263/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:6:p:2263-:d:775314

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().