Multi-directional gated recurrent unit and convolutional neural network for load and energy forecasting: A novel hybridization
Energy operations and schedules are significantly impacted by load and energy forecasting systems. An effective system is a requirement for a sustainable and equitable environment. Additionally, a trustworthy forecasting management system enhances the resilience of power systems by cutting power and...
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| Format: | Article |
| Language: | English |
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AIMS Press
2023-06-01
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| Series: | AIMS Mathematics |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/math.20231019?viewType=HTML |
| _version_ | 1797788513291206656 |
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| author | Fazeel Abid Muhammad Alam Faten S. Alamri Imran Siddique |
| author_facet | Fazeel Abid Muhammad Alam Faten S. Alamri Imran Siddique |
| author_sort | Fazeel Abid |
| collection | DOAJ |
| description | Energy operations and schedules are significantly impacted by load and energy forecasting systems. An effective system is a requirement for a sustainable and equitable environment. Additionally, a trustworthy forecasting management system enhances the resilience of power systems by cutting power and load-forecast flaws. However, due to the numerous inherent nonlinear properties of huge and diverse data, the classical statistical methodology cannot appropriately learn this non-linearity in data. Energy systems can appropriately evaluate data and regulate energy consumption because of advanced techniques. In comparison to machine learning, deep learning techniques have lately been used to predict energy consumption as well as to learn long-term dependencies. In this work, a fusion of novel multi-directional gated recurrent unit (MD-GRU) with convolutional neural network (CNN) using global average pooling (GAP) as hybridization is being proposed for load and energy forecasting. The spatial and temporal aspects, along with the high dimensionality of the data, are addressed by employing the capabilities of MD-GRU and CNN integration. The obtained results are compared to baseline algorithms including CNN, Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM), Gated Recurrent Unit (GRU), and Bidirectional Gated Recurrent Unit (Bi-GRU). The experimental findings indicate that the proposed approach surpasses conventional approaches in terms of accuracy, Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RSME). |
| first_indexed | 2024-03-13T01:37:38Z |
| format | Article |
| id | doaj.art-35485de43b16497691de6388343192e8 |
| institution | Directory Open Access Journal |
| issn | 2473-6988 |
| language | English |
| last_indexed | 2024-03-13T01:37:38Z |
| publishDate | 2023-06-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | AIMS Mathematics |
| spelling | doaj.art-35485de43b16497691de6388343192e82023-07-04T01:16:12ZengAIMS PressAIMS Mathematics2473-69882023-06-0189199932001710.3934/math.20231019Multi-directional gated recurrent unit and convolutional neural network for load and energy forecasting: A novel hybridizationFazeel Abid0Muhammad Alam1Faten S. Alamri 2Imran Siddique31. Department of Information Systems, University of Management and Technology, Lahore 54770, Pakistan2. Department of Computer Science and Informatics, London South Bank University, UK3. Department of Mathematical Sciences, College of Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia4. Department of Mathematics, University of Management and Technology, Lahore 54770, PakistanEnergy operations and schedules are significantly impacted by load and energy forecasting systems. An effective system is a requirement for a sustainable and equitable environment. Additionally, a trustworthy forecasting management system enhances the resilience of power systems by cutting power and load-forecast flaws. However, due to the numerous inherent nonlinear properties of huge and diverse data, the classical statistical methodology cannot appropriately learn this non-linearity in data. Energy systems can appropriately evaluate data and regulate energy consumption because of advanced techniques. In comparison to machine learning, deep learning techniques have lately been used to predict energy consumption as well as to learn long-term dependencies. In this work, a fusion of novel multi-directional gated recurrent unit (MD-GRU) with convolutional neural network (CNN) using global average pooling (GAP) as hybridization is being proposed for load and energy forecasting. The spatial and temporal aspects, along with the high dimensionality of the data, are addressed by employing the capabilities of MD-GRU and CNN integration. The obtained results are compared to baseline algorithms including CNN, Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM), Gated Recurrent Unit (GRU), and Bidirectional Gated Recurrent Unit (Bi-GRU). The experimental findings indicate that the proposed approach surpasses conventional approaches in terms of accuracy, Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RSME).https://www.aimspress.com/article/doi/10.3934/math.20231019?viewType=HTMLload and energy forecastingmulti-directional gated recurrent unit (md-gru)convolutional neural networksspatial and temporalhigh dimensionalitylong term dependencies |
| spellingShingle | Fazeel Abid Muhammad Alam Faten S. Alamri Imran Siddique Multi-directional gated recurrent unit and convolutional neural network for load and energy forecasting: A novel hybridization AIMS Mathematics load and energy forecasting multi-directional gated recurrent unit (md-gru) convolutional neural networks spatial and temporal high dimensionality long term dependencies |
| title | Multi-directional gated recurrent unit and convolutional neural network for load and energy forecasting: A novel hybridization |
| title_full | Multi-directional gated recurrent unit and convolutional neural network for load and energy forecasting: A novel hybridization |
| title_fullStr | Multi-directional gated recurrent unit and convolutional neural network for load and energy forecasting: A novel hybridization |
| title_full_unstemmed | Multi-directional gated recurrent unit and convolutional neural network for load and energy forecasting: A novel hybridization |
| title_short | Multi-directional gated recurrent unit and convolutional neural network for load and energy forecasting: A novel hybridization |
| title_sort | multi directional gated recurrent unit and convolutional neural network for load and energy forecasting a novel hybridization |
| topic | load and energy forecasting multi-directional gated recurrent unit (md-gru) convolutional neural networks spatial and temporal high dimensionality long term dependencies |
| url | https://www.aimspress.com/article/doi/10.3934/math.20231019?viewType=HTML |
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