Modelling and forecasting for solar irradiance from solarimetric station
Keywords:Solar Energy, Solar Irradiance, Statistical Analysis, Time Series, Photovoltaic Systems
This paper proposes two approaches for modeling solar irradiance time series. The first is an exploratory analysis and second is a periodic forecast model for solar irradiance data from solarimetric station in a country city of Bahia, Brazil. Two normality hypothesis tests were applied, Anderson-Darling and Shapiro-Wilk. The shape and symmetry of the data were also analyzed using boxplot and histograms to investigate the extreme points, as well as great asymmetry in the distribution of data at different times. Through adherence tests, the normal distribution of solar irradiance in most hours within the annual periodic is rejected. A study by season was carried out, which showed a different behavior in relation to the symmetry of the data, with autumn being the most uniform and spring being the most stochastic. Spring and summer presented better conditions for installation of photovoltaic plates, due to the high solar irradiance rates, and autumn and winter presented satisfactory solar irradiance to maintain this form of generation throughout the year.The second approach is a forecast to solar irradiance in photovoltaic generation systems. The importance of this forecast type is to favor the routine of planning, operation and maintenance of these types of systems, in addition to serving as a basis for feasibility studies and expansion of solar generation. Given a stochastic resource and the periodic behavior of solar irradiance data, a periodic autoregressive model was considered; the statistics were used by the maximum likelihood method, based on hourly measurements of irradiance over a period of one year.
A. G. Ferreira, Meteorologia pr´atica. Oficina de Textos, 2006.
EPE, “Plano nacional de energia 2050,” Rio De Janeiro, 2015.
ANEEL, “Atlas de energia el´etrica, do brasil,” Bras´ılia, Brasil. 3aEd,
C. Tiba, N. Fraidenraich, F. Lyra, and A. Nogueira, “Atlas solarim´etrico
do brasil: banco de dados terrestres,” Recife: Editora Universit´aria da
UFPE, p. 32, 2000.
C. G. Ribeiro, H. B. A. Neto, and T. S. Sene, “A oscilac¸ ˜ao do prec¸o
do petr´oleo: uma an´alise sobre o per´ıodo entre 2010-2015,” Estudos
internacionais: revista de relac¸ ˜oes internacionais da PUC Minas, vol. 6,
no. 1, pp. 87–106, 2018.
C. A. Nobre, J. Reid, and A. P. S. Veiga, “Fundamentos cient´ıficos das
mudanc¸as clim´aticas,” S˜ao Jos´e dos Campos, SP: Rede Clima/INPE,
F. R. Martins, T. G. Soares, and F. J. L. Lima, “Generating solar
irradiance data series with 1-minute time resolution based on hourly
observational data,” IEEE Latin America Transactions, vol. 100, no. 1e,
ANEEL, “Atlas brasileiro de energia el´etrica,” Agˆencia Nacional de
Energia El´etrica, Bras´ılia, Brasil. 3aEd, 2008.
R. Bondarik, L. A. Pilatti, and D. J. Horst, “Uma vis˜ao geral sobre
o potencial de gerac¸ ˜ao de energias renov´aveis no brasil,” Interciencia,
vol. 43, no. 10, pp. 680–688, 2018.
M. Diagne, M. David, P. Lauret, J. Boland, and N. Schmutz, “Review
of solar irradiance forecasting methods and a proposition for small-scale
insular grids,” Renewable and Sustainable Energy Reviews, vol. 27, pp.
V. Kallio-Myers, A. Riihel¨a, P. Lahtinen, and A. Lindfors, “Global
horizontal irradiance forecast for finland based on geostationary weather
satellite data,” Solar Energy, vol. 198, pp. 68–80, 2020.
J. Mubiru and E. Banda, “Estimation of monthly average daily global
solar irradiation using artificial neural networks,” Solar energy, vol. 82,
no. 2, pp. 181–187, 2008.
J. G´omez, F. Carlesso, L. Vieira, and L. Da Silva, “Solar irradiance:
basic concepts,” Revista Brasileira de Ensino de F´ısica, vol. 40, no. 3,
F. Besharat, A. A. Dehghan, and A. R. Faghih, “Empirical models for
estimating global solar radiation: A review and case study,” Renewable
and Sustainable Energy Reviews, vol. 21, pp. 798–821, 2013.
A. Angstrom, “Solar and terrestrial radiation. report to the international
commission for solar research on actinometric investigations of solar and
atmospheric radiation,” Quarterly Journal of the Royal Meteorological
Society, vol. 50, no. 210, pp. 121–126, 1924.
G. Mihalakakou, M. Santamouris, and D. Asimakopoulos, “The total
solar radiation time series simulation in athens, using neural networks,”
Theoretical and Applied Climatology, vol. 66, no. 3-4, pp. 185–197,
C. Voyant, J. G. De Gooijer, and G. Notton, “Periodic autoregressive
forecasting of global solar irradiation without knowledge-based model
implementation,” Solar Energy, vol. 174, pp. 121–129, 2018.
L. F. N. Lourenc¸o, M. B. de Camargo Salles, M. M. F. Gemignani, M. R.
Gouvea, and N. Kagan, “Time series modelling for solar irradiance estimation
in northeast brazil,” in 2017 IEEE 6th International Conference
on Renewable Energy Research and Applications (ICRERA). IEEE,
, pp. 401–405.
A. Mellit and A. M. Pavan, “A 24-h forecast of solar irradiance using
artificial neural network: Application for performance prediction of a
grid-connected pv plant at trieste, italy,” Solar Energy, vol. 84, no. 5,
pp. 807–821, 2010.
E. J. Balbinot, J. W. Scotton, S. M. Cerezer, and C. A. Martinazzo,
“Modelos de s´eries temporais aplicados a previs˜ao de radiac¸ ˜ao solar,”
Revista Perspectiva, 2017.
J. Bosch, G. Lopez, and F. Batlles, “Daily solar irradiation estimation
over a mountainous area using artificial neural networks,” Renewable
Energy, vol. 33, no. 7, pp. 1622–1628, 2008.
A. Mellit, M. Benghanem, and S. A. Kalogirou, “An adaptive waveletnetwork
model for forecasting daily total solar-radiation,” Applied
Energy, vol. 83, no. 7, pp. 705–722, 2006.
S. Pashiardis, S. A. Kalogirou, and A. Pelengaris, “Statistical analysis
for the characterization of solar energy utilization and inter-comparison
of solar radiation at two sites in cyprus,” Applied energy, vol. 190, pp.
G. Novais, “Distribuic¸ ˜ao m´edia dos climas zonais no globo: estudos
preliminares de uma nova classificac¸ ˜ao clim´atica (average distribution
of zonal climates on the globe: preliminary studies of a new climatic
classification),” Revista Brasileira de Geografia F´ısica, vol. 10, no. 5,
A. Papoulis and S. U. Pillai, Probability, random variables, and stochastic
processes. Tata McGraw-Hill Education, 2002.
R. Ballini, Analise e previs˜oes de vas˜oes utilizando modelos de series
temporais, redes neurais e redes neurais nebulosas. Tese de doutorado,
Doutorado em Engenharia El´etrica, Unicamp, 2000.
C. W. Hansen, J. S. Stein, and A. Ellis, “Statistical criteria for characterizing
irradiance time series,” Sandia National Laboratories SAND2010-
G. E. P. Box, G. M. Jenkins, and G. C. Reinsel, Time Series Analysis:
Forecasting and Control. Prentice-Hall International, Inc, 2008.
L. Martinez et al., “Pol´ıticas de controle malha fechada e malha aberta
no planejamento da operac¸ ˜ao energ´etica de sistemas hidrot´ermicos,”
C. Chen, J. Twycross, and J. M. Garibaldi, “A new accuracy measure
based on bounded relative error for time series forecasting,” PloS one,
vol. 12, no. 3, p. e0174202, 2017.
R. J. Hyndman and A. B. Koehler, “Another look at measures of forecast
accuracy,” International journal of forecasting, vol. 22, no. 4, pp. 679–
B.-k. Jeon and E.-J. Kim, “Next-day prediction of hourly solar irradiance
using local weather forecasts and lstm trained with non-local data,”
Energies, vol. 13, no. 20, p. 5258, 2020.