Day-Ahead Wind Power Forecasting Using Numerical Weather Prediction

Abstract

New power plants are included in the grid continuously to meet the increasing need for electrical energy. More than half of the power plants that have been newly added to the grid since 2015 are renewable energy power plants. As of 2022, the total installed renewable energy power increased by 11% compared to the previous year and reached 3146 GW. This increase will likely accelerate with the widespread use of electric vehicles. Wind energy is one of the renewable energy types with the highest installed capacity among renewable energy sources. Due to the nature of the wind, fluctuations in production cause adverse effects on the management of the electricity grid. Therefore, the forecast of renewable energy generation is very important for managing the electricity grid. This paper presents a wind energy forecast for the next 24 hours using data from a wind farm in Turkey. The forecast model consists of two stages. In the first stage, numerical weather predictions (NWP) have improved by using Artificial Neural Networks (ANN) and Artificial Bee Colony (ABC) methods. In the second stage, estimation is carried out by the ANFIS method. Improving the NWP using past NWP data and actual wind speed data increases the power forecast accuracy. The forecast results are compared with the forecasts made by the wind power plant (WPP). The proposed model has forecasted wind power with NRMSE 16.73%, and it has given more successful results than the forecast made by the power plant.