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• W4385458541 abstract "ABSTRACTThe increasing energy demand has significantly improved solar photovoltaic (SPV) systems as a distributed energy source. Real-time control of SPV performance is vital for accurate solar power (SP) prediction. The article proposes an ensemble Machine Learning Approach (MLA) called Random Forest Algorithm-Based Regression Model (RFARM) for hourly forecasting of SP. The approach selectively analyzes meteorological and solar irradiance data (SI) to enhance short-term solar panel prediction. It focuses on employing a correlation-based approach using an RFA with regression to achieve improved SP prediction accuracy. The study compares the PV power generated at Thiagarajar College of Engineering (TCE), Madurai, using four prediction techniques: Artificial Neural Networks (ANN), Random Forest (RF), K-Nearest Neighbours (KNN), and Support Vector Machine (SVM) along with a proposed RFARM for different meteorological weather conditions over a 24-hour time horizon. The proposed RFARM method achieves high prediction accuracy by selecting significant parameters, avoiding artificial filtering, and minimizing errors, particularly in predicting solar output during cloud shading. The RFARM model outperforms conventional methods in predicting the daily curve of solar power performance. It achieves an RMSE of 1.52, MAE of 14, and R-squared of 98%. Feature selection further improves accuracy, reducing RMSE by 12.5% and MAE by 17.2% respectively.CO EDITOR-IN-CHIEF: Kuo, Cheng-ChienASSOCIATE EDITOR: Kuo, Cheng-ChienKEYWORDS: Solar photovoltaic (SPV)predictionmachine learning (MLA)ANNRFARMRFKNNSVMfeature selective technique Nomenclature AC=Alternating currentANFIS=Adaptive Neuro-Fuzzy Inference SystemANN=Artificial Neural NetworksAT=Atmosphere TemperatureBI=Beam Irradiance (W/m2)CT=Cell Temperature (C)D=DayDC=Direct currentDI=Diffuse Irradiance (W/m2)DL=Deep LearningDPP=Data Pre-ProcessingDR=Demand responseDSM=Demand Side ManagementH=HourKNN=K-Nearest NeighboursLRM=Linear Regression ModelLsSVR=Least square Support Vector RegressionM=MonthMAE=Mean Absolute ErrorMAPE=Mean Absolute Percentage ErrorMLA=Machine Learning AlgorithmMSE=Mean Squared ErrorMVR=Multivariate regression modelPA=Plane of Array Irradiance (W/m2)PV=PhotovoltaicRF=Random ForestRFARM=Random Forest Algorithm-Based Regression ModelRMSE=Root Mean Square ErrorSI=Solar irradianceSP=Solar PowerSVM=Support Vector MachineTCE=Thiagarajar College Of EngineeringVIF=Variance Inflation FactorWS=Wind Speed (m/s)AcknowledgmentsThis research has been made possible by the Department of Electrical and Electronics Engineering, TCE, Madurai, which has provided all the necessary data, and I wish to express my sincere thanks to the administration and the staff.Disclosure statementNo potential conflict of interest was reported by the author(s).Correction StatementThis article has been corrected with minor changes. These changes do not impact the academic content of the article." @default.
• W4385458541 created "2023-08-02" @default.
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• W4385458541 date "2023-08-01" @default.
• W4385458541 modified "2023-09-27" @default.
• W4385458541 title "An ensemble machine learning-based solar power prediction of meteorological variability conditions to improve accuracy in forecasting" @default.
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• W4385458541 doi "https://doi.org/10.1080/02533839.2023.2238777" @default.
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