Enhancing Solar Energy Forecast Using Multi-Column Convolutional Neural Network and Multipoint Time Series Approach
The rapid expansion of solar industries presents unknown technological challenges. A dedicated and suitable energy forecast is an effective solution for the daily dispatching and production of the electricity grid. The traditional forecast technique uses weather and plant parameters as the model inf...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/15/1/107 |
| _version_ | 1797431271767408640 |
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| author | Anil Kumar Yashwant Kashyap Panagiotis Kosmopoulos |
| author_facet | Anil Kumar Yashwant Kashyap Panagiotis Kosmopoulos |
| author_sort | Anil Kumar |
| collection | DOAJ |
| description | The rapid expansion of solar industries presents unknown technological challenges. A dedicated and suitable energy forecast is an effective solution for the daily dispatching and production of the electricity grid. The traditional forecast technique uses weather and plant parameters as the model information. Nevertheless, these are insufficient to consider problematic weather variability and the various plant characteristics in the actual field. Considering the above facts and inspired by the excellent implementation of the multi-column convolutional neural network (MCNN) in image processing, we developed a novel approach for forecasting solar energy by transforming multipoint time series (MT) into images for the MCNN to examine. We first processed the data to convert the time series solar energy into image matrices. We observed that the MCNN showed a preeminent response under a ground-based high-resolution spatial–temporal image matrix with a 0.2826% and 0.5826% RMSE for 15 min-ahead forecast under clear (CR) and cloudy (CD) conditions, respectively. Our process was performed on the MATLAB deep learning platform and tested on CR and CD solar energy conditions. The excellent execution of the suggested technique was compared with state-of-the-art deep neural network solar forecasting techniques. |
| first_indexed | 2024-03-09T09:41:34Z |
| format | Article |
| id | doaj.art-44b92d38eef64127883294282e57975c |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T09:41:34Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-44b92d38eef64127883294282e57975c2023-12-02T00:50:57ZengMDPI AGRemote Sensing2072-42922022-12-0115110710.3390/rs15010107Enhancing Solar Energy Forecast Using Multi-Column Convolutional Neural Network and Multipoint Time Series ApproachAnil Kumar0Yashwant Kashyap1Panagiotis Kosmopoulos2Electrical and Electronics Engineering Department, National Institute of Technology Karnataka, Surathkal 575025, IndiaElectrical and Electronics Engineering Department, National Institute of Technology Karnataka, Surathkal 575025, IndiaInstitute for Environmental Research and Sustainable Development, National Observatory of Athens (IERSD/NOA), 15236 Athens, GreeceThe rapid expansion of solar industries presents unknown technological challenges. A dedicated and suitable energy forecast is an effective solution for the daily dispatching and production of the electricity grid. The traditional forecast technique uses weather and plant parameters as the model information. Nevertheless, these are insufficient to consider problematic weather variability and the various plant characteristics in the actual field. Considering the above facts and inspired by the excellent implementation of the multi-column convolutional neural network (MCNN) in image processing, we developed a novel approach for forecasting solar energy by transforming multipoint time series (MT) into images for the MCNN to examine. We first processed the data to convert the time series solar energy into image matrices. We observed that the MCNN showed a preeminent response under a ground-based high-resolution spatial–temporal image matrix with a 0.2826% and 0.5826% RMSE for 15 min-ahead forecast under clear (CR) and cloudy (CD) conditions, respectively. Our process was performed on the MATLAB deep learning platform and tested on CR and CD solar energy conditions. The excellent execution of the suggested technique was compared with state-of-the-art deep neural network solar forecasting techniques.https://www.mdpi.com/2072-4292/15/1/107convolutional neural network (CNN)multi-column convolutional neural network (MCNN) multipoint approachsolar generation forecast |
| spellingShingle | Anil Kumar Yashwant Kashyap Panagiotis Kosmopoulos Enhancing Solar Energy Forecast Using Multi-Column Convolutional Neural Network and Multipoint Time Series Approach Remote Sensing convolutional neural network (CNN) multi-column convolutional neural network (MCNN) multipoint approach solar generation forecast |
| title | Enhancing Solar Energy Forecast Using Multi-Column Convolutional Neural Network and Multipoint Time Series Approach |
| title_full | Enhancing Solar Energy Forecast Using Multi-Column Convolutional Neural Network and Multipoint Time Series Approach |
| title_fullStr | Enhancing Solar Energy Forecast Using Multi-Column Convolutional Neural Network and Multipoint Time Series Approach |
| title_full_unstemmed | Enhancing Solar Energy Forecast Using Multi-Column Convolutional Neural Network and Multipoint Time Series Approach |
| title_short | Enhancing Solar Energy Forecast Using Multi-Column Convolutional Neural Network and Multipoint Time Series Approach |
| title_sort | enhancing solar energy forecast using multi column convolutional neural network and multipoint time series approach |
| topic | convolutional neural network (CNN) multi-column convolutional neural network (MCNN) multipoint approach solar generation forecast |
| url | https://www.mdpi.com/2072-4292/15/1/107 |
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