A Hybrid Channel-Communication-Enabled CNN-LSTM Model for Electricity Load Forecasting
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-03-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/15/6/2263 |
| _version_ | 1797471731925909504 |
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| author | Faisal Saeed Anand Paul Hyuncheol Seo |
| author_facet | Faisal Saeed Anand Paul Hyuncheol Seo |
| author_sort | Faisal Saeed |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T19:53:18Z |
| format | Article |
| id | doaj.art-a295f6a567db4f5bbe4697353706f584 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T19:53:18Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-a295f6a567db4f5bbe4697353706f5842023-11-24T01:06:01ZengMDPI AGEnergies1996-10732022-03-01156226310.3390/en15062263A Hybrid Channel-Communication-Enabled CNN-LSTM Model for Electricity Load ForecastingFaisal Saeed0Anand Paul1Hyuncheol Seo2Department of Computer Science and Engineering, Kyungpook National University, Buk-gu, Daegu 41566, KoreaDepartment of Computer Science and Engineering, Kyungpook National University, Buk-gu, Daegu 41566, KoreaSchool of Architectural, Civil, Environmental and Energy Engineering, Kyungpook National University, Daegu 41566, KoreaSmart 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.https://www.mdpi.com/1996-1073/15/6/2263cross-channel communicationConvolutional Neural NetworksLSTMelectricityloadforecasting |
| spellingShingle | Faisal Saeed Anand Paul Hyuncheol Seo A Hybrid Channel-Communication-Enabled CNN-LSTM Model for Electricity Load Forecasting Energies cross-channel communication Convolutional Neural Networks LSTM electricity load forecasting |
| title | A Hybrid Channel-Communication-Enabled CNN-LSTM Model for Electricity Load Forecasting |
| title_full | A Hybrid Channel-Communication-Enabled CNN-LSTM Model for Electricity Load Forecasting |
| title_fullStr | A Hybrid Channel-Communication-Enabled CNN-LSTM Model for Electricity Load Forecasting |
| title_full_unstemmed | A Hybrid Channel-Communication-Enabled CNN-LSTM Model for Electricity Load Forecasting |
| title_short | A Hybrid Channel-Communication-Enabled CNN-LSTM Model for Electricity Load Forecasting |
| title_sort | hybrid channel communication enabled cnn lstm model for electricity load forecasting |
| topic | cross-channel communication Convolutional Neural Networks LSTM electricity load forecasting |
| url | https://www.mdpi.com/1996-1073/15/6/2263 |
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