Investigating the impact of weather parameters selection on the prediction of solar radiation under different genera of cloud cover: A case-study in a subtropical location


Chakchak J., Çetin N. S.

Measurement, vol.176, pp.1-25, 2021 (Journal Indexed in SCI Expanded)

  • Publication Type: Article / Article
  • Volume: 176
  • Publication Date: 2021
  • Doi Number: 10.1016/j.measurement.2021.109159
  • Title of Journal : Measurement
  • Page Numbers: pp.1-25

Abstract

In the present study, a classification system of four meteorological indices were introduced to classify the sky

types from sunny to cloudy. These meteorological indices are; cloud cover (Cc), sunshine hour (S), clearness

index (Kt) and diffuse fraction (K). Frequency of occurrence and cumulative frequency distribution of each sky

indices were established to interpret the prevailing sky conditions in eight cases, each case investigates one day,

these days were chosen according to the type of clouds that were overwhelming that day at a subtropical location

in Turkey (Izmir). The impact of Cc, on measured global solar radiation (GSR) intensity were also analyzed. In

order to predict GSR in each selected case and to making it more and more precise, NARX, FFNN and GRNN

weather parameters-based models were evaluated and compared. To achieve accuracy of estimation, ten

different input configurations were used to train the models. In addition global solar radiation in a cloudless skies

(GSR’) is evaluated as a new input parameter. NARX models had the best performance for estimation of GSR in

the first case, with GPI of 0.936 (NARX-C1M1). Results are also indicating that all NARX artificial neural network

models provided better estimations than GRNN and FFNN models in case 2 with GPI value of 0.872 (NARXC2M1).

As for case 3, NARX was considered as the best models (NARX-C3M1, GPI = 0.957), followed by NARX

(NARX-C3M6, GPI = 0.956) and then FFNN (FF-C3M1, GPI = 0.949). In case 4, NARX models had the highest

GPI value (NARX-C4M1, GPI = 0.957) compared with FFNN and GRNN indicated models. Regarding case 5,

NARX was considered as the best network (NARX-C5M4, GPI = 0.748), followed by NARX-C5M9 (GPI = 0.549),

while FFNN and GRNN model couldn’t provide positive results. Likewise, the NARX models did provide better

results in case 6 with best GPI value up to 0.64. Furthermore, only NARX and FFNN models provided the better

estimation in case 7 (best model, NARX-C7M1 with GPI = 0.826), whereas, GRNN models did not give such

acceptable results. Similar to the mentioned above and in comparison with FFNN and GRNN, we have deducated

that NARX models offered a better accuracy results compared with the other models (best model, NARX-C8M4

with GIP = 0.364) in case8.