Accurate solar radiation nowcasting models are critical for the integration of the increasing solar energy in power systems. This work explored the benefits obtained by the blending of four all-sky-imagers (ASI)-based models, two satellite-images-based models and a data-driven model. Two blending approaches (general and horizon) and two blending models (linear and random forest (RF)) were evaluated. The relative contribution of the different forecasting models in the blended-models-derived benefits was also explored. The study was conducted in Southern Spain; blending models provide one-minute resolution 90 min-ahead GHI and DNI forecasts. The results show that the general approach and the RF blending model present higher performance and provide enhanced forecasts. The improvement in rRMSE values obtained by model blending was up to 30% for GHI (40% for DNI), depending on the forecasting horizon. The greatest improvement was found at lead times between 15 and 30 min, and was negligible beyond 50 min. The results also show that blending models using only the data-driven model and the two satellite-images-based models (one using high resolution images and the other using low resolution images) perform similarly to blending models that used the ASI-based forecasts. Therefore, it was concluded that suitable model blending might prevent the use of expensive (and highly demanding, in terms of maintenance) ASI-based systems for point nowcasting.
<p>The share of solar energy in the electricity systems of many countries in the world will reach unprecedented values in the coming decades, fostered by the mitigation of climate change and also by the economic competitiveness of this energy. Accurate solar radiation forecasting models are critical for the integration of the increasing solar energy in power systems.</p><p>In this work, the benefits obtained by blending seven models: four All-sky imagers (ASI)-based, two satellite images based (one using low resolution and other using high-resolution images) and a data-driven model, were analyzed. The use of two blending models (linear and Random Forest (RF)) and two blending approaches (General and Horizon) were explored. The horizon approach constructs a different blending model for each forecast horizon, while the general approach trains a single model valid for all horizons. The study is conducted in southern Spain and blending models provide one-minute resolution 90-minutes ahead GHI and DNI forecasts. Results show the General approach and the RF blending model to perform superior and to provide enhanced forecasts. The relative improvement in rRMSE obtained by model blending was up to 30% for GHI (40% for DNI), being maximum at lead times between 15 and 30 minutes and negligible at lead times greater than 50 minutes. Results also show that blending of just the data-driven model and the two satellite models (low and high resolution), without including the ASI-based models, performs similarly to those blending models that used as input the ASI-based models. Results then indicate that, for point nowcasting, the use of ASI-based forecasting systems can be avoided by using a suitable blending of data-driven, high resolution and low resolution satellite-images-based forecasting models.</p>
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