Titlebook: Distributed Computing and Artificial Intelligence, 16th International Conference, Special Sessions; Enrique Herrera-Viedma,Zita Vale,Rober

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https://doi.org/10.1007/978-1-349-14245-3een the states. The propagation is briefly analyzed considering different initial conditions and network topologies (complete networks, random networks, scale-free networks and small-world networks), and interesting conclusions are derived.

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Energy Consumption Forecasting Using Ensemble Learning Algorithmsf the forecasting models is assessed, and results show that the Adaboost model is superior to the other considered models for the one-hour ahead forecasts. The results of this study compared to previous works indicates that ensemble learning methods are a viable choice for short-term load forecast.

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Study of Multi-Tariff Influence on the Distributed Generation Remuneratione in the formation of groups. Thus, three scenarios were created: Whole Week, Work Days and Weekend. Profiles were added for 548 units of DG. The justification for the formation of groups will be a fairer remuneration and according to the contribution of each resource to the management of the network.

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