Name
Abstract | ||
|---|---|---|
The variable nature of solar power output from PhotoVoltaic (PV) systems is the main obstacle for penetration of such power into the electricity grid. Thus, numerous methods have been proposed in the literature to construct forecasting models. In this paper, we present a comprehensive comparison of a set of prominent methods that utilize weather prediction for future. Firstly, we evaluate the prediction accuracy of widely used Neural Network (NN), Support Vector Regression (SVR), k-Nearest Neighbours (kNN), Multiple Linear Regression (MLR), and two persistent methods using four data sets for 2 years. We then analyze the sensitivities of their prediction accuracy to 10–25% possible error in the future weather prediction obtained from the Bureau of Meteorology (BoM). Results demonstrate that ensemble of NNs is the most promising method and achieves substantial improvement in accuracy over other prediction methods. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-030-04503-6_32 | PAKDD |
Field | DocType | Citations |
Data mining,Data set,Weather prediction,Regression,Computer science,Support vector machine,Solar power,Artificial intelligence,Artificial neural network,Photovoltaic system,Machine learning,Linear regression | Conference | 0 |
PageRank | References | Authors |
0.34 | 2 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mashud Rana | 1 | 0 | 0.34 |
| Ashfaqur Rahman | 2 | 177 | 26.70 |
| Liwan H. Liyanage | 3 | 36 | 3.39 |
| Mohammed Nazim Uddin | 4 | 0 | 0.34 |