On Short-Term Load Forecasting Using Machine Learning Techniques and a Novel Parallel Deep LSTM-CNN Approach
Since electricity plays a crucial role in countries' industrial infrastructures, power companies are trying to monitor and control infrastructures to improve energy management and scheduling. Accurate forecasting is a critical task for a stable and efficient energy supply, where load and supply...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9356582/ |
| _version_ | 1819180806449397760 |
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| author | Behnam Farsi Manar Amayri Nizar Bouguila Ursula Eicker |
| author_facet | Behnam Farsi Manar Amayri Nizar Bouguila Ursula Eicker |
| author_sort | Behnam Farsi |
| collection | DOAJ |
| description | Since electricity plays a crucial role in countries' industrial infrastructures, power companies are trying to monitor and control infrastructures to improve energy management and scheduling. Accurate forecasting is a critical task for a stable and efficient energy supply, where load and supply are matched. This article discusses various algorithms and a new hybrid deep learning model which combines long short-term memory networks (LSTM) and convolutional neural network (CNN) model to analyze their performance for short-term load forecasting. The proposed model is called parallel LSTM-CNN Network or PLCNet. Two real-world data sets, namely “hourly load consumption of Malaysia ” as well as “daily power electric consumption of Germany”, are used to test and compare the presented models. To evaluate the tested models' performance, root mean squared error (RMSE), mean absolute percentage error (MAPE), and R-squared were used. In total, this article is divided into two parts. In the first part, different machine learning models, including the PLCNet, predict the next time step load. In the second part, the model's performance, which has shown the most accurate results in the first part, is discussed in different time horizons. The results show that deep neural networks models, especially PLCNet, are good candidates for being used as short-term prediction tools. PLCNet improved the accuracy from 83.17% to 91.18% for the German data and achieved 98.23% accuracy in Malaysian data, which is an excellent result in load forecasting. |
| first_indexed | 2024-12-22T22:20:11Z |
| format | Article |
| id | doaj.art-2e2a8bf7eff5465facf975feb2028bb1 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T22:20:11Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-2e2a8bf7eff5465facf975feb2028bb12022-12-21T18:10:41ZengIEEEIEEE Access2169-35362021-01-019311913121210.1109/ACCESS.2021.30602909356582On Short-Term Load Forecasting Using Machine Learning Techniques and a Novel Parallel Deep LSTM-CNN ApproachBehnam Farsi0https://orcid.org/0000-0002-7287-2989Manar Amayri1Nizar Bouguila2https://orcid.org/0000-0001-7224-7940Ursula Eicker3Concordia Institute for Information Systems Engineering(CIISE), Concordia University, Montreal, QC, CanadaG-SCOP Lab, Grenoble Institute of Technology, Grenoble, FranceConcordia Institute for Information Systems Engineering(CIISE), Concordia University, Montreal, QC, CanadaDepartment of Building, Civil and Environmental Engineering, Concordia University, Montreal, QC, CanadaSince electricity plays a crucial role in countries' industrial infrastructures, power companies are trying to monitor and control infrastructures to improve energy management and scheduling. Accurate forecasting is a critical task for a stable and efficient energy supply, where load and supply are matched. This article discusses various algorithms and a new hybrid deep learning model which combines long short-term memory networks (LSTM) and convolutional neural network (CNN) model to analyze their performance for short-term load forecasting. The proposed model is called parallel LSTM-CNN Network or PLCNet. Two real-world data sets, namely “hourly load consumption of Malaysia ” as well as “daily power electric consumption of Germany”, are used to test and compare the presented models. To evaluate the tested models' performance, root mean squared error (RMSE), mean absolute percentage error (MAPE), and R-squared were used. In total, this article is divided into two parts. In the first part, different machine learning models, including the PLCNet, predict the next time step load. In the second part, the model's performance, which has shown the most accurate results in the first part, is discussed in different time horizons. The results show that deep neural networks models, especially PLCNet, are good candidates for being used as short-term prediction tools. PLCNet improved the accuracy from 83.17% to 91.18% for the German data and achieved 98.23% accuracy in Malaysian data, which is an excellent result in load forecasting.https://ieeexplore.ieee.org/document/9356582/Electricitysmart gridsload consumptionshort-term load forecastingdeep learningtime series |
| spellingShingle | Behnam Farsi Manar Amayri Nizar Bouguila Ursula Eicker On Short-Term Load Forecasting Using Machine Learning Techniques and a Novel Parallel Deep LSTM-CNN Approach IEEE Access Electricity smart grids load consumption short-term load forecasting deep learning time series |
| title | On Short-Term Load Forecasting Using Machine Learning Techniques and a Novel Parallel Deep LSTM-CNN Approach |
| title_full | On Short-Term Load Forecasting Using Machine Learning Techniques and a Novel Parallel Deep LSTM-CNN Approach |
| title_fullStr | On Short-Term Load Forecasting Using Machine Learning Techniques and a Novel Parallel Deep LSTM-CNN Approach |
| title_full_unstemmed | On Short-Term Load Forecasting Using Machine Learning Techniques and a Novel Parallel Deep LSTM-CNN Approach |
| title_short | On Short-Term Load Forecasting Using Machine Learning Techniques and a Novel Parallel Deep LSTM-CNN Approach |
| title_sort | on short term load forecasting using machine learning techniques and a novel parallel deep lstm cnn approach |
| topic | Electricity smart grids load consumption short-term load forecasting deep learning time series |
| url | https://ieeexplore.ieee.org/document/9356582/ |
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