Taking into account the well-known benefits of using renewable energy as an important energy source, some practical aspects regarding operation must be underlined. In the specific case of wind power plants (WPP) forecasting the generated wind power is the most important issue for assuring stability of Romania’s National Power System (NPS) and economic efficiency of wind power plant operation. The wind power plant operator should report daily to National Dispatching Centre, the hourly energy production for the next day. In case of inaccurate energy production prediction, the National System Operator must take additional measures in order to maintain the stability of NPS. The purpose of our research is to develop a solution, based on Artificial Neural Networks, for forecasting the wind energy production of small output power wind farms located in hilly areas of Romania, thus improving the accuracy of the hourly prediction. The case study is based on two-year historical data for a wind power plant comprising two power production groups situated in Tulcea County, in southeastern Romania. By using this approach, we have also explored the possibility of algorithm generalization, starting from the detailed model of the first production group and generalize it to the second one.
renewable energy, wind power forecasting, upscaling technique, artificial neural network.
Ion LUNGU, George CĂRUŢAŞU, Alexandru PÎRJAN, Simona-Vasilica OPREA, Adela BÂRA, "A Two-step Forecasting Solution and Upscaling Technique for Small Size Wind Farms Located in Hilly Areas of Romania", Studies in Informatics and Control, ISSN 1220-1766, vol. 25(1), pp. 77-86, 2016. https://doi.org/10.24846/v25i1y201609
All articles published by Studies in Informatics and Control (SIC) are published in accordance with the terms and conditions of the Creative Commons Attribution License (CC BY-NC-SA 4.0).