A wavelet-coupled support vector machine model for forecasting global incident solar radiation using limited meteorological dataset

Deo, Ravinesh C. and Wen, Xiaohu and Feng, Qi (2016) A wavelet-coupled support vector machine model for forecasting global incident solar radiation using limited meteorological dataset. Applied Energy, 168. pp. 568-593. ISSN 0306-2619

Abstract

A solar radiation forecasting model can be utilized is a scientific contrivance for investigating future viability
of solar energy potentials. In this paper, a wavelet-coupled support vector machine (W-SVM) model was adopted to forecast global incident solar radiation based on the sunshine hours (St), minimum temperature (Tmax), maximum temperature (Tmax), windspeed (U), evaporation (E) and precipitation (P) as the predictor variables. To ascertain conclusive results, the merit of the W-SVM was benchmarked with the classical SVM model. For daily forecasting, sixteen months of data (01-March-2014 to 30-June-2015) partitioned into the train (65%) and test (35%) set for the three metropolitan stations (Brisbane City, Cairns Aero and Townsville Aero) were utilized. Data were decomposed into their wavelet sub-series by discrete wavelet transformation algorithm and summed up to create new series with one approximation and four levels of detail using Daubechies-2 mother wavelet. For daily forecasting, six model scenarios were formulated where the number of input was increased and the forecast was assessed by statistical metrics (correlation coefficient r; Willmott’s index d; Nash-Sutcliffe coefficient ENS; peak deviation Pdv), distribution statistics and prediction errors (mean absolute error MAE; root mean square error RMSE; mean absolute percentage error MAPE; relative root mean square error RMSE). Results for daily forecasts showed that the W-SVM model outperformed the classical SVM model for optimum input combinations. A sensitivity analysis with single predictor variables yielded the best performance with St as an input, confirming that the largest contributions for forecasting solar energy is derived from the sunshine hours per day compared to the other prescribed inputs. All six inputs were required in the optimum W-SVM for Brisbane and Cairns stations to yield r = 0.928, d = 0.927, ENS = 0.858, Pdv = 1.757%, MAE = 1.819 MJ m�2
and r = 0.881, d = 0.870, ENS = 0.762, Pdv = 9.633%, MAE = 2.086 MJ m�2, respectively. However, for Townsville, the time-series of St, Tmin and Tmax and E were required in the optimum model to yield r = 0.858, d = 0.886, ENS = 0.722, Pdv = 10.282% and MAE = 2.167 MJ m�2. In terms of the relative model errors over daily forecast horizon, W-SVM model was the most accurate precise for Townsville
(RRMSE = 12.568%; MAPE = 12.666%) followed by Brisbane (13.313%; 13.872%) and Cairns (14.467%; 15.675%) weather stations. A set of alternative models developed over the monthly, seasonal and annual forecast horizons verified the long-term forecasting skill, where lagged inputs of Tmax, Tmin, E, P and VP for Roma Post Office and Toowoomba Regional stations were employed. The wavelet-coupled model performed well, with r = 0.965, d = 0.964, Pdv = 2.249%, RRMSE = 5.942% and MAPE = 4.696% (Roma) and r = 0.958, d = 0.943, Pdv = 0.979%, RRMSE = 7.66% and MAPE = 6.20% (Toowoomba). Accordingly, the results conclusively ascertained the importance of wavelet-coupled SVM predictive model as a qualified stratagem for short and long-term forecasting of solar energy for assessment of solar energy prospectivity in this study region.


Statistics for USQ ePrint 28776
Statistics for this ePrint Item
Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Third place winner for the USQ School-Specific 2016 Publication Excellence Awards for journal Articles - School of Agricultural, Computational and Environmental Sciences. The research project was supported by the USQ Academic Division Research Activation Incentive Scheme (RAIS; July– September 2015) grant awarded to Dr RC Deo to establish research collaboration with CAREERI Director Professor Feng Qi and Associate Professor Xiaohu Wen at the Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences (China). Permanent restricted access to published version due to publisher copyright policy.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 17 Feb 2016 02:20
Last Modified: 03 Jan 2018 00:57
Uncontrolled Keywords: solar energy model; wavelet-coupled model; support vector machines
Fields of Research : 05 Environmental Sciences > 0502 Environmental Science and Management > 050204 Environmental Impact Assessment
04 Earth Sciences > 0401 Atmospheric Sciences > 040107 Meteorology
09 Engineering > 0906 Electrical and Electronic Engineering > 090608 Renewable Power and Energy Systems Engineering (excl. Solar Cells)
05 Environmental Sciences > 0502 Environmental Science and Management > 050206 Environmental Monitoring
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080105 Expert Systems
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080110 Simulation and Modelling
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation
01 Mathematical Sciences > 0102 Applied Mathematics > 010204 Dynamical Systems in Applications
04 Earth Sciences > 0401 Atmospheric Sciences > 040103 Atmospheric Radiation
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970105 Expanding Knowledge in the Environmental Sciences
B Economic Development > 85 Energy > 8505 Renewable Energy > 850506 Solar-Thermal Energy
D Environment > 96 Environment > 9602 Atmosphere and Weather > 960202 Atmospheric Processes and Dynamics
Identification Number or DOI: 10.1016/j.apenergy.2016.01.130
URI: http://eprints.usq.edu.au/id/eprint/28776

Actions (login required)

View Item Archive Repository Staff Only