A novel heuristic method for wind farm power prediction: A case study

Integration of wind power has a broad impact on power system operations, ranging from short-term system operations to long-term planning. The traditional deterministic unit commitment and economic dispatch algorithms that power generation operators are currently using in the power system operations cannot capture uncertainties from wind power. To this end, an accurate wind farm power forecasting can highly support distribution and transmission system managers to improve power system management. In this research, authors present a novel hybrid method based on combination of imperialistic competitive algorithm (ICA) and artificial neural network (ANN) method to boost the short-term wind farm power prediction exactness using data from a numerical weather prediction (NWP) as well as measured data from an online SCADA. Besides, a very short-term generation power forecasting is implemented based on the values of wind speed and wind generation. An extensive comparative literature survey on presented methods in cases of short-term and very short-term is provided in this paper. At first step, considering environmental factors (i.e. geographical conditions, wind speed, humidity, temperature and other factors) a predictive model for wind speed forecasting is provided by the means of a multilayer perceptron (MLP) artificial neural network. At next step, ICA is employed in order to update weights of neural network. Validation of proposed method is confirmed using data of an actual wind farm. (C) 2014 Elsevier Ltd. All rights reserved.