TY - GEN
T1 - Comparison and sensitivity analysis of methods for solar PV power prediction
AU - Rana, Mashud
AU - Rahman, Ashfaqur
AU - Liyanage, Liwan
AU - Uddin, Mohammed Nazim
PY - 2018
Y1 - 2018
N2 - 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 Neighbour (kNN), Multiple Linear Regression (MLR), and two persistent methods using four data sets for 2 years. We then analyse the sensitivities of their prediction accuracy to 1"”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.
AB - 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 Neighbour (kNN), Multiple Linear Regression (MLR), and two persistent methods using four data sets for 2 years. We then analyse the sensitivities of their prediction accuracy to 1"”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.
KW - nearest neighbor analysis (statistics)
KW - neural networks (computer science)
KW - regression analysis
KW - solar energy
KW - vector analysis
KW - weather forecasting
UR - http://hdl.handle.net/1959.7/uws:52486
U2 - 10.1007/978-3-030-04503-6_32
DO - 10.1007/978-3-030-04503-6_32
M3 - Conference Paper
SN - 9783030045029
SP - 333
EP - 344
BT - Trends and Applications in Knowledge Discovery and Data Mining: PAKDD 2018 Workshops, Melbourne, Vic, Australia, June 3 2018, Revised Selected Papers
PB - Springer
T2 - Pacific-Asia Conference on Knowledge Discovery and Data Mining
Y2 - 3 June 2018
ER -
