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Evaluating the Performance of Autoregressive Model for Solar Radiation Forecasting
- Source :
- Nigerian Journal of Environmental Sciences and Technology. 5:444-455
- Publication Year :
- 2021
- Publisher :
- University of Benin - Faculty of Environmental Sciences, 2021.
-
Abstract
- The solar radiation data taken from 14 meteorological stations in Nigeria has been analyzed. The periodic component of the data which covered a period of 13 (mostly 1977-1989) years was removed via Fourier analysis while the residual series was subjected to autoregressive analysis. It was evident from the t-test and autocorrelation plots of the modified (i.e. without the periodic component) series that there exist significant persistence at nine stations including Sokoto, Nguru, Kano, Maiduguri, Bauchi, Yola, Minna, Ibadan, and Benin. The autocorrelation at Jos, Bida, Ikeja, Enugu and Port Harcourt were however found to be insignificant. As the sample partial autocorrelation function cuts off after lag 1, a non-seasonal autoregressive model of order 1, AR (1), was identified for stations with autocorrelation. The Q-statistic of error series suggested that the models were adequate as identified. Moreover, the exploratory plots of the model residual series showed agreement with the quantitative statistics and thus enforces the inference that the models were adequate for monthly mean daily global solar radiation forecasts at some of the study stations. It is interesting to note that all the stations within the sub-sahelian region showed significant persistence whereas all the stations in the coastal region except Benin were found with insignificant autocorrelation. Expectedly, the performance evaluation of the model gave impressive result for the stations within the sub-sahelian region but a relatively weak result for the coastal region. The result for the midland region was mixed whereas it was difficult to conclude on the Guinea savannah region with result from only one station.
- Subjects :
- Autoregressive model
Econometrics
Environmental science
Radiation
Subjects
Details
- ISSN :
- 26160501 and 2616051X
- Volume :
- 5
- Database :
- OpenAIRE
- Journal :
- Nigerian Journal of Environmental Sciences and Technology
- Accession number :
- edsair.doi...........33b1983741eb38fcfe8902dfce11256e