A Hybrid Deep Learning-Based Network for Photovoltaic Power Forecasting
For efficient energy distribution, microgrids (MG) provide significant assistance to main grids and act as a bridge between the power generation and consumption. Renewable energy generation resources, particularly photovoltaics (PVs), are considered as a clean source of energy but are highly complex...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2022/7040601 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849409744503046144 |
|---|---|
| author | Altaf Hussain Zulfiqar Ahmad Khan Tanveer Hussain Fath U Min Ullah Seungmin Rho Sung Wook Baik |
| author_facet | Altaf Hussain Zulfiqar Ahmad Khan Tanveer Hussain Fath U Min Ullah Seungmin Rho Sung Wook Baik |
| author_sort | Altaf Hussain |
| collection | DOAJ |
| description | For efficient energy distribution, microgrids (MG) provide significant assistance to main grids and act as a bridge between the power generation and consumption. Renewable energy generation resources, particularly photovoltaics (PVs), are considered as a clean source of energy but are highly complex, volatile, and intermittent in nature making their forecasting challenging. Thus, a reliable, optimized, and a robust forecasting method deployed at MG objectifies these challenges by providing accurate renewable energy production forecasting and establishing a precise power generation and consumption matching at MG. Furthermore, it ensures effective planning, operation, and acquisition from the main grid in the case of superior or inferior amounts of energy, respectively. Therefore, in this work, we develop an end-to-end hybrid network for automatic PV power forecasting, comprising three basic steps. Firstly, data preprocessing is performed to normalize, remove the outliers, and deal with the missing values prominently. Next, the temporal features are extracted using deep sequential modelling schemes, followed by the extraction of spatial features via convolutional neural networks. These features are then fed to fully connected layers for optimal PV power forecasting. In the third step, the proposed model is evaluated on publicly available PV power generation datasets, where its performance reveals lower error rates when compared to state-of-the-art methods. |
| format | Article |
| id | doaj-art-7dbc442429f846908d2ed46f8fd2c22c |
| institution | Kabale University |
| issn | 1099-0526 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-7dbc442429f846908d2ed46f8fd2c22c2025-08-20T03:35:24ZengWileyComplexity1099-05262022-01-01202210.1155/2022/7040601A Hybrid Deep Learning-Based Network for Photovoltaic Power ForecastingAltaf Hussain0Zulfiqar Ahmad Khan1Tanveer Hussain2Fath U Min Ullah3Seungmin Rho4Sung Wook Baik5Sejong UniversitySejong UniversitySejong UniversitySejong UniversityDepartment of Industrial SecuritySejong UniversityFor efficient energy distribution, microgrids (MG) provide significant assistance to main grids and act as a bridge between the power generation and consumption. Renewable energy generation resources, particularly photovoltaics (PVs), are considered as a clean source of energy but are highly complex, volatile, and intermittent in nature making their forecasting challenging. Thus, a reliable, optimized, and a robust forecasting method deployed at MG objectifies these challenges by providing accurate renewable energy production forecasting and establishing a precise power generation and consumption matching at MG. Furthermore, it ensures effective planning, operation, and acquisition from the main grid in the case of superior or inferior amounts of energy, respectively. Therefore, in this work, we develop an end-to-end hybrid network for automatic PV power forecasting, comprising three basic steps. Firstly, data preprocessing is performed to normalize, remove the outliers, and deal with the missing values prominently. Next, the temporal features are extracted using deep sequential modelling schemes, followed by the extraction of spatial features via convolutional neural networks. These features are then fed to fully connected layers for optimal PV power forecasting. In the third step, the proposed model is evaluated on publicly available PV power generation datasets, where its performance reveals lower error rates when compared to state-of-the-art methods.http://dx.doi.org/10.1155/2022/7040601 |
| spellingShingle | Altaf Hussain Zulfiqar Ahmad Khan Tanveer Hussain Fath U Min Ullah Seungmin Rho Sung Wook Baik A Hybrid Deep Learning-Based Network for Photovoltaic Power Forecasting Complexity |
| title | A Hybrid Deep Learning-Based Network for Photovoltaic Power Forecasting |
| title_full | A Hybrid Deep Learning-Based Network for Photovoltaic Power Forecasting |
| title_fullStr | A Hybrid Deep Learning-Based Network for Photovoltaic Power Forecasting |
| title_full_unstemmed | A Hybrid Deep Learning-Based Network for Photovoltaic Power Forecasting |
| title_short | A Hybrid Deep Learning-Based Network for Photovoltaic Power Forecasting |
| title_sort | hybrid deep learning based network for photovoltaic power forecasting |
| url | http://dx.doi.org/10.1155/2022/7040601 |
| work_keys_str_mv | AT altafhussain ahybriddeeplearningbasednetworkforphotovoltaicpowerforecasting AT zulfiqarahmadkhan ahybriddeeplearningbasednetworkforphotovoltaicpowerforecasting AT tanveerhussain ahybriddeeplearningbasednetworkforphotovoltaicpowerforecasting AT fathuminullah ahybriddeeplearningbasednetworkforphotovoltaicpowerforecasting AT seungminrho ahybriddeeplearningbasednetworkforphotovoltaicpowerforecasting AT sungwookbaik ahybriddeeplearningbasednetworkforphotovoltaicpowerforecasting AT altafhussain hybriddeeplearningbasednetworkforphotovoltaicpowerforecasting AT zulfiqarahmadkhan hybriddeeplearningbasednetworkforphotovoltaicpowerforecasting AT tanveerhussain hybriddeeplearningbasednetworkforphotovoltaicpowerforecasting AT fathuminullah hybriddeeplearningbasednetworkforphotovoltaicpowerforecasting AT seungminrho hybriddeeplearningbasednetworkforphotovoltaicpowerforecasting AT sungwookbaik hybriddeeplearningbasednetworkforphotovoltaicpowerforecasting |