Wind Speed Correction Method for Wind Power Based on Markov Chains

With the rapid development of the global economy, energy demand has surged, while the finite nature of traditional fossil fuels has led to energy shortages and environmental pollution. Wind energy, as a renewable and clean energy source, has become an important alternative. However, the variability of wind speed affects the output power of wind farms, consequently impacting grid stability. To address this, this paper proposes a wind speed error correction method based on Markov chains to improve the accuracy of indirect wind power forecasting. By analyzing NWP (Numerical Weather Prediction) and SCADA(Supervisory Control And Data Acquisition) wind speed data, a transition probability matrix for wind speed errors was established, facilitating wind speed correction. Experimental results demonstrate that the forecasting error of wind power, after correction using the Markov chain, is significantly lower than that of uncorrected data, validating the effectiveness of this method and contributing to enhanced wind energy utilization efficiency and grid stability.