https://orcid.org/0000-0002-2551-4396
![Sample our Engineering & Technology journals, sign in here to start your access, latest two full volumes FREE to you for 14 days]({"type":"image" , "src" : "/sda/5933/TOC-Subject-Sample-2021-Engineering-Tech.jpg"})
ABSTRACT
The 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:
ASSOCIATE EDITOR:
Nomenclature
| AC | = | Alternating current |
| ANFIS | = | Adaptive Neuro-Fuzzy Inference System |
| ANN | = | Artificial Neural Networks |
| AT | = | Atmosphere Temperature |
| BI | = | Beam Irradiance (W/m2) |
| CT | = | Cell Temperature (C) |
| D | = | Day |
| DC | = | Direct current |
| DI | = | Diffuse Irradiance (W/m2) |
| DL | = | Deep Learning |
| DPP | = | Data Pre-Processing |
| DR | = | Demand response |
| DSM | = | Demand Side Management |
| H | = | Hour |
| KNN | = | K-Nearest Neighbours |
| LRM | = | Linear Regression Model |
| LsSVR | = | Least square Support Vector Regression |
| M | = | Month |
| MAE | = | Mean Absolute Error |
| MAPE | = | Mean Absolute Percentage Error |
| MLA | = | Machine Learning Algorithm |
| MSE | = | Mean Squared Error |
| MVR | = | Multivariate regression model |
| PA | = | Plane of Array Irradiance (W/m2) |
| PV | = | Photovoltaic |
| RF | = | Random Forest |
| RFARM | = | Random Forest Algorithm-Based Regression Model |
| RMSE | = | Root Mean Square Error |
| SI | = | Solar irradiance |
| SP | = | Solar Power |
| SVM | = | Support Vector Machine |
| TCE | = | Thiagarajar College Of Engineering |
| VIF | = | Variance Inflation Factor |
| WS | = | Wind Speed (m/s) |
Acknowledgments
This 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 statement
No potential conflict of interest was reported by the author(s).
Correction Statement
This article has been corrected with minor changes. These changes do not impact the academic content of the article.