Predicting Global Irradiance Combining Forecasting Models Through Machine Learning

This paper has been presented at : 13th International Conference on Hybrid Artificial Intelligence Systems (HAIS 2018)Predicting solar irradiance is an active research problem, with many physical models having being designed to accurately predict Global Horizontal Irradiance. However, some of the models are better at short time horizons, while others are more accurate for medium and long horizons. The aim of this research is to automatically combine the predictions of four different models (Smart Persistence, Satellite, Cloud Index Advection and Diffusion, and Solar Weather Research and Forecasting) by means of a state-of-the-art machine learning method (Extreme Gradient Boosting). With this purpose, the four models are used as inputs to the machine learning model, so that the output is an improved Global Irradiance forecast. A 2-year dataset of predictions and measures at one radiometric station in Seville has been gathered to validate the method proposed. Three approaches are studied: a general model, a model for each horizon, and models for groups of horizons. Experimental results show that the machine learning combination of predictors is, on average, more accurate than the predictors themselves.The authors are supported by the Spanish Ministry of Economy and Competitiveness, projects ENE2014-56126-C2-1-R and ENE2014-56126-C2-2-R and FEDER funds. Some of the authors are also funded by the Junta de Andalucía (research group TEP-220)

Food intake in free-feeding and energy-deprived lean rats is mediated by the neuropeptide Y5 receptor.

The new neuropeptide Y (NPY) Y5 receptor antagonist CGP 71683A displayed high affinity for the cloned rat NPY Y5 subtype, but > 1, 000-fold lower affinity for the cloned rat NPY Y1, Y2, and Y4 subtypes. In LMTK cells transfected with the human NPY Y5 receptor, CGP 71683A was without intrinsic activity and antagonized NPY-induced Ca2+ transients. CGP 71683A was given intraperitoneally (dose range 1-100 mg/kg) to a series of animal models of high hypothalamic NPY levels. In lean satiated rats CGP 71683A significantly antagonized the increase in food intake induced by intracerebroventricular injection of NPY. In 24-h fasted and streptozotocin diabetic rats CGP 71683A dose-dependently inhibited food intake. During the dark phase, CGP 71683A dose-dependently inhibited food intake in free-feeding lean rats without affecting the normal pattern of food intake or inducing taste aversion. In free-feeding lean rats, intraperitoneal administration of CGP 71683A for 28 d inhibited food intake dose-dependently with a maximum reduction observed on days 3 and 4. Despite the return of food intake to control levels, body weight and the peripheral fat mass remained significantly reduced. The data demonstrate that the NPY Y5 receptor subtype plays a role in NPY-induced food intake, but also suggest that, with chronic blockade, counterregulatory mechanisms are induced to restore appetite