Wind power forecasting: A case study in terrain using artificial intelligence
Abstract The rapidly increasing world population and developing technology bring about the need for energy. Limited reserves of fossil fuels and the damages caused on the environment by these fuels have necessitated the move towards renewable energy sources such as wind. Wind energy is at the top among renewable energy resources as a type of economical and rapidly improving form of energy production. Wind, which as an intermittent and stochastic structure may change momentary changes and this situation makes it difficult to estimate its power accurately. It is crucial to accurately estimate wind power to calculate energy unit costs beforehand and ensure safe connection with the power grid. The purpose of this study is to accurately estimate wind power at 61 m at the wind measurement station by using 25,926 units of real time data including data on wind power, wind direction, temperature, humidity and pressure at 31 m. For this purpose, 100 artificial neural networks were trained and tested using the method of Multilayer Perceptron (MLP) The system's error rates were analyzed, and the lowest rate of error was at the MLP 6 24 1 network with 6 inputs, 24 hidden layers, exponential hidden activation, and output activation as identity.