This work addresses the solar resource assessment through long-term statistical analysis and typical weather data generation with different time resolutions, using measurements of Global Horizontal Irradiation (GHI) and other relevant meteorological variables from eight ground-based weather stations covering the south and north coasts and the central mountains of Madeira Island, Portugal. Typical data are generated based on the selection and concatenation of hourly data considering three different time periods (month, five-day and typical days) through a modified Sandia method. This analysis was carried out by computing the Root Mean Square Difference (RMSD) and the Normalized RMSD (NRMSD) for each time slot of the typical years taking the long-term average as reference. It was found that the datasets generated with typical days present a lower value of overall NRMSD. A comparison between the hourly values of the generated typical data and the long-term averages was also carried out using various statistical indicators. To simplify this analysis, those statistical indicators were combined into a single Global Performance Index (GPI). It was found that datasets based on typical days have the highest value of GPI, followed by the datasets based on typical five-day periods and then those based on typical months.

Knowledge on the diffuse horizontal irradiance (DHI), and direct normal irradiance (DNI) is crucial for the estimation of the irradiance on tilted surfaces, which in turn is critical for photovoltaic (PV) applications and for designing and simulating concentrated solar power (CSP) plants. Since global horizontal irradiance (GHI) is the most commonly measured solar radiation variable, it is advantageous for establishing a suitable method that uses it to compute DHI and DNI. In this way, a new model for predicting the diffuse fraction (K d) based on the climate zone is proposed, using only the clearness index (K t) as the predictor and 1-min resolution GHI data. A review of the literature on models that use hourly and sub-hourly K t values to compute K d was also carried out, and an extensive performance assessment of both the proposed model and the models from the literature was conducted using ten statistical indicators and a global performance index (GPI). A set of model parameters was determined for each climate zone considered in this study (arid, high albedo, temperate and tropical) using 48 worldwide radiometric stations. It was found that the best overall performing model was the model proposed in this work.

No abstract

No abstract

No abstract