Remote areas are usually fed-in terms of electricity supply-from conventional generators that run on diesel. Recently, there is increasing interest on hybrid RES-based systems, including wind and solar power coupled with energy storage. To this end, optimum dispatching of such configurations is largely based on the capacity of prognostic tools employed in the respective energy management system. Acknowledging this, the aim of this work is the prediction of wind speed, 24hours ahead on an hourly basis, for the optimum operation of hybrid power stations (HPS) with the use of artificial neural networks (ANN). For this purpose, hourly data of wind speed have been used at a specific location (Tilos Island, Greece) where a HPS is going to be installed, including also a wind turbine of 800kW. More specifically, an ANN which is fed with historical wind and air pressure data was developed in order to predict the wind speed at hub height on an hourly basis for the next 24 hours. Results indicate that the proposed methodology gives an adequate forecast of wind speed in order to design an automated wind power information tool that could much facilitate the tasks of the energy management system.
Nowadays demand side management has become an important issue. Managing the energy resources in an optimal manner has become imperative among energy planners and policy makers. An integrated energy management approach is essential for the sustainable development of any electricity grid. The main objective of this work is the development of a forecasting model in order to predict one day ahead the energy demand of Tilos Island, Greece. For this purpose, an artificial neural network (ANN) forecasting model was developed to predict the energy demand of the entire island region. Prediction concerns 24-hours ahead on an hourly basis, with the developed ANN model being fed with historical data of energy demand, historical data of solar irradiation and historical data of a biometeorological index known as Cooling Power Index. Results show that the proposed methodology gives a sufficient forecast of energy demand in order to design an automated energy demand information tool for end-users such as distribution and transmission system operators.
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