Data-Driven Short-Term Load Forecasting for Multiple Locations: An Integrated Approach
Short-term load forecasting (STLF) plays a crucial role in the planning, management, and stability of a country’s power system operation. In this study, we have developed a novel approach that can simultaneously predict the load demand of different regions in Bangladesh. When making predictions for...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Big Data and Cognitive Computing |
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| Online Access: | https://www.mdpi.com/2504-2289/8/2/12 |
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| author | Anik Baul Gobinda Chandra Sarker Prokash Sikder Utpal Mozumder Ahmed Abdelgawad |
| author_facet | Anik Baul Gobinda Chandra Sarker Prokash Sikder Utpal Mozumder Ahmed Abdelgawad |
| author_sort | Anik Baul |
| collection | DOAJ |
| description | Short-term load forecasting (STLF) plays a crucial role in the planning, management, and stability of a country’s power system operation. In this study, we have developed a novel approach that can simultaneously predict the load demand of different regions in Bangladesh. When making predictions for loads from multiple locations simultaneously, the overall accuracy of the forecast can be improved by incorporating features from the various areas while reducing the complexity of using multiple models. Accurate and timely load predictions for specific regions with distinct demographics and economic characteristics can assist transmission and distribution companies in properly allocating their resources. Bangladesh, being a relatively small country, is divided into nine distinct power zones for electricity transmission across the nation. In this study, we have proposed a hybrid model, combining the Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU), designed to forecast load demand seven days ahead for each of the nine power zones simultaneously. For our study, nine years of data from a historical electricity demand dataset (from January 2014 to April 2023) are collected from the Power Grid Company of Bangladesh (PGCB) website. Considering the nonstationary characteristics of the dataset, the Interquartile Range (IQR) method and load averaging are employed to deal effectively with the outliers. Then, for more granularity, this data set has been augmented with interpolation at every 1 h interval. The proposed CNN-GRU model, trained on this augmented and refined dataset, is evaluated against established algorithms in the literature, including Long Short-Term Memory Networks (LSTM), GRU, CNN-LSTM, CNN-GRU, and Transformer-based algorithms. Compared to other approaches, the proposed technique demonstrated superior forecasting accuracy in terms of mean absolute performance error (MAPE) and root mean squared error (RMSE). The dataset and the source code are openly accessible to motivate further research. |
| first_indexed | 2024-03-07T22:42:15Z |
| format | Article |
| id | doaj.art-098d1fa7ad8e452798dad95e2af2ba10 |
| institution | Directory Open Access Journal |
| issn | 2504-2289 |
| language | English |
| last_indexed | 2024-03-07T22:42:15Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj.art-098d1fa7ad8e452798dad95e2af2ba102024-02-23T15:07:37ZengMDPI AGBig Data and Cognitive Computing2504-22892024-01-01821210.3390/bdcc8020012Data-Driven Short-Term Load Forecasting for Multiple Locations: An Integrated ApproachAnik Baul0Gobinda Chandra Sarker1Prokash Sikder2Utpal Mozumder3Ahmed Abdelgawad4College of Science and Engineering, Central Michigan University, Mount Pleasant, MI 48858, USADepartment of Robotics and Mechatronics Engineering, University of Dhaka, Dhaka 1000, BangladeshDepartment of Computer Science and Engineering, Institute of Science and Technology, Dhaka 1209, BangladeshDepartment of Electrical and Computer Engineering, University of Alaska Fairbanks, Fairbanks, AK 99775, USACollege of Science and Engineering, Central Michigan University, Mount Pleasant, MI 48858, USAShort-term load forecasting (STLF) plays a crucial role in the planning, management, and stability of a country’s power system operation. In this study, we have developed a novel approach that can simultaneously predict the load demand of different regions in Bangladesh. When making predictions for loads from multiple locations simultaneously, the overall accuracy of the forecast can be improved by incorporating features from the various areas while reducing the complexity of using multiple models. Accurate and timely load predictions for specific regions with distinct demographics and economic characteristics can assist transmission and distribution companies in properly allocating their resources. Bangladesh, being a relatively small country, is divided into nine distinct power zones for electricity transmission across the nation. In this study, we have proposed a hybrid model, combining the Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU), designed to forecast load demand seven days ahead for each of the nine power zones simultaneously. For our study, nine years of data from a historical electricity demand dataset (from January 2014 to April 2023) are collected from the Power Grid Company of Bangladesh (PGCB) website. Considering the nonstationary characteristics of the dataset, the Interquartile Range (IQR) method and load averaging are employed to deal effectively with the outliers. Then, for more granularity, this data set has been augmented with interpolation at every 1 h interval. The proposed CNN-GRU model, trained on this augmented and refined dataset, is evaluated against established algorithms in the literature, including Long Short-Term Memory Networks (LSTM), GRU, CNN-LSTM, CNN-GRU, and Transformer-based algorithms. Compared to other approaches, the proposed technique demonstrated superior forecasting accuracy in terms of mean absolute performance error (MAPE) and root mean squared error (RMSE). The dataset and the source code are openly accessible to motivate further research.https://www.mdpi.com/2504-2289/8/2/12short-term load forecastingCNN-GRU hybrid modeldeep learningbangladesh power system |
| spellingShingle | Anik Baul Gobinda Chandra Sarker Prokash Sikder Utpal Mozumder Ahmed Abdelgawad Data-Driven Short-Term Load Forecasting for Multiple Locations: An Integrated Approach Big Data and Cognitive Computing short-term load forecasting CNN-GRU hybrid model deep learning bangladesh power system |
| title | Data-Driven Short-Term Load Forecasting for Multiple Locations: An Integrated Approach |
| title_full | Data-Driven Short-Term Load Forecasting for Multiple Locations: An Integrated Approach |
| title_fullStr | Data-Driven Short-Term Load Forecasting for Multiple Locations: An Integrated Approach |
| title_full_unstemmed | Data-Driven Short-Term Load Forecasting for Multiple Locations: An Integrated Approach |
| title_short | Data-Driven Short-Term Load Forecasting for Multiple Locations: An Integrated Approach |
| title_sort | data driven short term load forecasting for multiple locations an integrated approach |
| topic | short-term load forecasting CNN-GRU hybrid model deep learning bangladesh power system |
| url | https://www.mdpi.com/2504-2289/8/2/12 |
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