A tiered NARX model for forecasting day-ahead energy production in distributed solar PV systems
This study presents a hierarchical forecasting approach for day-ahead energy production in distributed solar Photovoltaic (PV) systems using a tiered Nonlinear Autoregressive Exogenous (NARX) model. The methodology was applied to 52 PV systems installed at The University of Jordan, covering three pr...
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| Language: | English |
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Elsevier
2024-12-01
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| Series: | Cleaner Engineering and Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666790824001113 |
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| author | Sameer Al-Dahidi Mohammad Alrbai Bilal Rinchi Loiy Al-Ghussain Osama Ayadi Ali Alahmer |
| author_facet | Sameer Al-Dahidi Mohammad Alrbai Bilal Rinchi Loiy Al-Ghussain Osama Ayadi Ali Alahmer |
| author_sort | Sameer Al-Dahidi |
| collection | DOAJ |
| description | This study presents a hierarchical forecasting approach for day-ahead energy production in distributed solar Photovoltaic (PV) systems using a tiered Nonlinear Autoregressive Exogenous (NARX) model. The methodology was applied to 52 PV systems installed at The University of Jordan, covering three prediction scales: fleet-wide, zone-specific, and site-specific. The model incorporated weather data, including solar irradiation, temperature, and humidity, to forecast the next day's energy production. Based on a new metric called the OverallMetric, fleet-wide predictions outperform the zone-specific and site-specific averages by 3.21% and 5.35%, respectively. Normalized Root Mean Square Errors (nRMSE) for fleet-wide, zone-specific, and site-specific predictions are 0.148, 0.141, and 0.137, respectively. The Correlation Coefficient (R) is above 80% for all prediction scales, with the accuracy constrained by the model's difficulty in adapting to abrupt weather changes, leading to overestimation. The model performs best when weather patterns and PV generation are consistent with previous days. This demonstrates that adapting models to the characteristics of each scale significantly improves forecast accuracy, enabling more effective macro-level planning and micro-level operational decisions. |
| format | Article |
| id | doaj-art-b2b2cbb6b3534c72aa585bf44b8d8f82 |
| institution | OA Journals |
| issn | 2666-7908 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Cleaner Engineering and Technology |
| spelling | doaj-art-b2b2cbb6b3534c72aa585bf44b8d8f822025-08-20T02:34:44ZengElsevierCleaner Engineering and Technology2666-79082024-12-012310083110.1016/j.clet.2024.100831A tiered NARX model for forecasting day-ahead energy production in distributed solar PV systemsSameer Al-Dahidi0Mohammad Alrbai1Bilal Rinchi2Loiy Al-Ghussain3Osama Ayadi4Ali Alahmer5Department of Mechanical and Maintenance Engineering, School of Applied Technical Sciences, German Jordanian University, Amman, 11180, Jordan; Corresponding author.Department of Mechanical Engineering, School of Engineering, University of Jordan, Amman, 11942, JordanDepartment of Mechanical and Maintenance Engineering, School of Applied Technical Sciences, German Jordanian University, Amman, 11180, Jordan; Department of Mechanical Engineering, School of Engineering, University of Jordan, Amman, 11942, JordanEnergy Systems and Infrastructure Analysis Division, Argonne National Laboratory, Lemont, IL, 60439, USADepartment of Mechanical Engineering, School of Engineering, University of Jordan, Amman, 11942, JordanDepartment of Mechanical Engineering, Tuskegee University, Tuskegee, AL, 36088, USAThis study presents a hierarchical forecasting approach for day-ahead energy production in distributed solar Photovoltaic (PV) systems using a tiered Nonlinear Autoregressive Exogenous (NARX) model. The methodology was applied to 52 PV systems installed at The University of Jordan, covering three prediction scales: fleet-wide, zone-specific, and site-specific. The model incorporated weather data, including solar irradiation, temperature, and humidity, to forecast the next day's energy production. Based on a new metric called the OverallMetric, fleet-wide predictions outperform the zone-specific and site-specific averages by 3.21% and 5.35%, respectively. Normalized Root Mean Square Errors (nRMSE) for fleet-wide, zone-specific, and site-specific predictions are 0.148, 0.141, and 0.137, respectively. The Correlation Coefficient (R) is above 80% for all prediction scales, with the accuracy constrained by the model's difficulty in adapting to abrupt weather changes, leading to overestimation. The model performs best when weather patterns and PV generation are consistent with previous days. This demonstrates that adapting models to the characteristics of each scale significantly improves forecast accuracy, enabling more effective macro-level planning and micro-level operational decisions.http://www.sciencedirect.com/science/article/pii/S2666790824001113Solar photovoltaic systemsPrediction scalesEnergy productionForecasting analysisNonlinear autoregressive exogenousReal case study |
| spellingShingle | Sameer Al-Dahidi Mohammad Alrbai Bilal Rinchi Loiy Al-Ghussain Osama Ayadi Ali Alahmer A tiered NARX model for forecasting day-ahead energy production in distributed solar PV systems Cleaner Engineering and Technology Solar photovoltaic systems Prediction scales Energy production Forecasting analysis Nonlinear autoregressive exogenous Real case study |
| title | A tiered NARX model for forecasting day-ahead energy production in distributed solar PV systems |
| title_full | A tiered NARX model for forecasting day-ahead energy production in distributed solar PV systems |
| title_fullStr | A tiered NARX model for forecasting day-ahead energy production in distributed solar PV systems |
| title_full_unstemmed | A tiered NARX model for forecasting day-ahead energy production in distributed solar PV systems |
| title_short | A tiered NARX model for forecasting day-ahead energy production in distributed solar PV systems |
| title_sort | tiered narx model for forecasting day ahead energy production in distributed solar pv systems |
| topic | Solar photovoltaic systems Prediction scales Energy production Forecasting analysis Nonlinear autoregressive exogenous Real case study |
| url | http://www.sciencedirect.com/science/article/pii/S2666790824001113 |
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