EFFICIENT METHODOLOGY FOR LONG TERM FORECASTING OF THE SOLAR RESOURCE
CASE STUDY FOR CALIFORNIA’S CENTRAL VALLEY
DOI:
https://doi.org/10.59627/cbens.2010.1701Palavras-chave:
Global Horizontal Irradiation, Direct Normal Irradiation, Solar Irradiation Forecasting, Gamma Test Model Input SelectionResumo
We develop a long-term solar irradiance forecasting model by adopting predicted meteorological variables from the U.S. National Weather System (NWS) forecasting database data as inputs. We evaluate the solar resource in Merced, which is centrally located between Bakersfield and Sacramento, at the heart of California's central valley (Merced is also very close to the geographical center of the state of California). An important component of our study is the development of a set of criteria for selecting relevant inputs for the forecasting model. We select variable inputs using a version of the Gamma test for stochastic modeling. In all, eleven inputs are considered, nine of which are meteorological variables, while the other two depend on solar geotemporal quantities. The solar geotemporal variables are found to be critically important, while the most relevant meteorological variables include sky cover,probability of precipitation, and maximum and minimum temperatures. A secondary objective of our study is to assess model quality as a function of both forecast horizon and seasonal dependence. For Global Horizontal Irradiance (GHI), the relative Root-Mean-Square-Error (rRMSE) is shown to be stable and relatively flat for 5-day ahead forecasts when calculated over the entire data set (a period of over a year). For Direct Normal Irradiance (DNI), the rRMSE increases by over 15% after 5 days. When the rRMSEs are calculated using monthly averages, the results show that model quality is best (lowest rRMSEs) during the months of March through September (7 months). These results are important because the highest quality of forecast results coincide with peak demand of power for the region. Prediction intervals are also derived based on regression of the squared residuals on the input variables; in this way, the model quality dependency on sky conditions is obtained. This information is also important because, as results on the monthly RMSEs suggest, forecastability depends strongly on local weather conditions and on seasonal variations.
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