Machine Learning Based Photovoltaics (PV) Power Prediction Using Different Environmental Parameters of Qatar
<div><p>Photovoltaics (PV) output power is highly sensitive to many environmental parameters and the power produced by the PV systems is significantly affected by the harsh environments. The annual PV power density of around 2000 kWh/m2 in the Arabian Peninsula is an exploitable wealth o...
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| مؤلفون آخرون: | , , , , , |
| منشور في: |
2019
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| _version_ | 1864513526343663616 |
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| author | Amith Khandakar (14151981) |
| author2 | Muhammad E. H. Chowdhury (16494003) Monzure- Khoda Kazi (18060871) Kamel Benhmed (18060874) Farid Touati (1556026) Mohammed Al-Hitmi (16864239) Antonio Jr S. P. Gonzales (18060877) |
| author2_role | author author author author author author |
| author_facet | Amith Khandakar (14151981) Muhammad E. H. Chowdhury (16494003) Monzure- Khoda Kazi (18060871) Kamel Benhmed (18060874) Farid Touati (1556026) Mohammed Al-Hitmi (16864239) Antonio Jr S. P. Gonzales (18060877) |
| author_role | author |
| dc.creator.none.fl_str_mv | Amith Khandakar (14151981) Muhammad E. H. Chowdhury (16494003) Monzure- Khoda Kazi (18060871) Kamel Benhmed (18060874) Farid Touati (1556026) Mohammed Al-Hitmi (16864239) Antonio Jr S. P. Gonzales (18060877) |
| dc.date.none.fl_str_mv | 2019-07-19T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3390/en12142782 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Machine_Learning_Based_Photovoltaics_PV_Power_Prediction_Using_Different_Environmental_Parameters_of_Qatar/25295398 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electrical engineering Electronics, sensors and digital hardware Environmental engineering PV power prediction artificial neural network renewable energy environmental parameters multiple regression model |
| dc.title.none.fl_str_mv | Machine Learning Based Photovoltaics (PV) Power Prediction Using Different Environmental Parameters of Qatar |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <div><p>Photovoltaics (PV) output power is highly sensitive to many environmental parameters and the power produced by the PV systems is significantly affected by the harsh environments. The annual PV power density of around 2000 kWh/m2 in the Arabian Peninsula is an exploitable wealth of energy source. These countries plan to increase the contribution of power from renewable energy (RE) over the years. Due to its abundance, the focus of RE is on solar energy. Evaluation and analysis of PV performance in terms of predicting the output PV power with less error demands investigation of the effects of relevant environmental parameters on its performance. In this paper, the authors have studied the effects of the relevant environmental parameters, such as irradiance, relative humidity, ambient temperature, wind speed, PV surface temperature and accumulated dust on the output power of the PV panel. Calibration of several sensors for an in-house built PV system was described. Several multiple regression models and artificial neural network (ANN)-based prediction models were trained and tested to forecast the hourly power output of the PV system. The ANN models with all the features and features selected using correlation feature selection (CFS) and relief feature selection (ReliefF) techniques were found to successfully predict PV output power with Root Mean Square Error (RMSE) of 2.1436, 6.1555, and 5.5351, respectively. Two different bias calculation techniques were used to evaluate the instances of biased prediction, which can be utilized to reduce bias to improve accuracy. The ANN model outperforms other regression models, such as a linear regression model, M5P decision tree and gaussian process regression (GPR) model. This will have a noteworthy contribution in scaling the PV deployment in countries like Qatar and increase the share of PV power in the national power production.</p><p> </p></div><h2>Other Information</h2> <p> Published in: Energies<br> License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/en12142782" target="_blank">https://dx.doi.org/10.3390/en12142782</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_1ba9c02eb850e88f0e1a64387d412bd0 |
| identifier_str_mv | 10.3390/en12142782 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25295398 |
| publishDate | 2019 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Machine Learning Based Photovoltaics (PV) Power Prediction Using Different Environmental Parameters of QatarAmith Khandakar (14151981)Muhammad E. H. Chowdhury (16494003)Monzure- Khoda Kazi (18060871)Kamel Benhmed (18060874)Farid Touati (1556026)Mohammed Al-Hitmi (16864239)Antonio Jr S. P. Gonzales (18060877)EngineeringElectrical engineeringElectronics, sensors and digital hardwareEnvironmental engineeringPV power predictionartificial neural networkrenewable energyenvironmental parametersmultiple regression model<div><p>Photovoltaics (PV) output power is highly sensitive to many environmental parameters and the power produced by the PV systems is significantly affected by the harsh environments. The annual PV power density of around 2000 kWh/m2 in the Arabian Peninsula is an exploitable wealth of energy source. These countries plan to increase the contribution of power from renewable energy (RE) over the years. Due to its abundance, the focus of RE is on solar energy. Evaluation and analysis of PV performance in terms of predicting the output PV power with less error demands investigation of the effects of relevant environmental parameters on its performance. In this paper, the authors have studied the effects of the relevant environmental parameters, such as irradiance, relative humidity, ambient temperature, wind speed, PV surface temperature and accumulated dust on the output power of the PV panel. Calibration of several sensors for an in-house built PV system was described. Several multiple regression models and artificial neural network (ANN)-based prediction models were trained and tested to forecast the hourly power output of the PV system. The ANN models with all the features and features selected using correlation feature selection (CFS) and relief feature selection (ReliefF) techniques were found to successfully predict PV output power with Root Mean Square Error (RMSE) of 2.1436, 6.1555, and 5.5351, respectively. Two different bias calculation techniques were used to evaluate the instances of biased prediction, which can be utilized to reduce bias to improve accuracy. The ANN model outperforms other regression models, such as a linear regression model, M5P decision tree and gaussian process regression (GPR) model. This will have a noteworthy contribution in scaling the PV deployment in countries like Qatar and increase the share of PV power in the national power production.</p><p> </p></div><h2>Other Information</h2> <p> Published in: Energies<br> License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/en12142782" target="_blank">https://dx.doi.org/10.3390/en12142782</a></p>2019-07-19T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3390/en12142782https://figshare.com/articles/journal_contribution/Machine_Learning_Based_Photovoltaics_PV_Power_Prediction_Using_Different_Environmental_Parameters_of_Qatar/25295398CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/252953982019-07-19T03:00:00Z |
| spellingShingle | Machine Learning Based Photovoltaics (PV) Power Prediction Using Different Environmental Parameters of Qatar Amith Khandakar (14151981) Engineering Electrical engineering Electronics, sensors and digital hardware Environmental engineering PV power prediction artificial neural network renewable energy environmental parameters multiple regression model |
| status_str | publishedVersion |
| title | Machine Learning Based Photovoltaics (PV) Power Prediction Using Different Environmental Parameters of Qatar |
| title_full | Machine Learning Based Photovoltaics (PV) Power Prediction Using Different Environmental Parameters of Qatar |
| title_fullStr | Machine Learning Based Photovoltaics (PV) Power Prediction Using Different Environmental Parameters of Qatar |
| title_full_unstemmed | Machine Learning Based Photovoltaics (PV) Power Prediction Using Different Environmental Parameters of Qatar |
| title_short | Machine Learning Based Photovoltaics (PV) Power Prediction Using Different Environmental Parameters of Qatar |
| title_sort | Machine Learning Based Photovoltaics (PV) Power Prediction Using Different Environmental Parameters of Qatar |
| topic | Engineering Electrical engineering Electronics, sensors and digital hardware Environmental engineering PV power prediction artificial neural network renewable energy environmental parameters multiple regression model |