Variable Support Segment-Based Short-Term Wind Speed Forecasting

Abstract

Accurate short-term wind speed forecasting plays an important role in the development of wind energy. However, the inertia of airflow means that wind speed has the properties of time variance and inertia, which pose a challenge in the task of wind speed forecasting. We employ the variable support segment method to describe these two properties. We then propose a variable support segment-based short-term wind speed forecasting model to improve wind speed forecasting accuracy. The core idea is to adaptively determine the variable support segment of the future wind speed by a self-attention mechanism. Historical wind speed series are first decomposed into several components by variational mode decomposition (VMD). Then, the future values of each component are forecast using a modified Transformer model. Finally, the forecasting values of these components are summed to obtain the future wind speed forecasting values. Wind speed data collected from a wind farm were employed to validate the performance of the proposed model. The mean absolute error of the proposed model in spring, summer, autumn, and winter is 0.25, 0.33, 0.31, and 0.29, respectively. Experimental results show that the proposed model achieves significant accuracy and that the modified Transformer model has good performance.

Application of Artificial Intelligence in Power System Monitoring and Fault Diagnosis

“…This section provides a summary of this Special Issue of Energies, covering published articles [1][2][3][4][5][6][7][8][9][10] which address several topics related to AI technologies in power system performance monitoring.…”

“…Finally, in [10], Zhang et al constructed a short-term wind speed prediction model based on variable support segments (VSS). At first, the method decomposes the historical wind speed series into several components using the variational mode decomposition method.…”

Methods of calculating electric power generation by wind turbines and their influence on wind speed

“…Одним из таких источников является ветер. Скорость и направление ветра невозможно предсказать с высокой точностью на длительный период времени, хотя для выявления этих данных и существуют многочисленные методики: численное прогнозирование погоды (NWP модели) [1,2]; использование статистических моделей [1,2]; метод фильтров Калмана [2]; с помощью интегрированной модели авторегрессии (скользящего среднего) для анализа временных рядов ARIMA и ее разновидностей [2]; с помощью сети Байеса [2]; использование нечеткой системной модели [1,2]; использование нейронных сетей [1,2]; комбинированная модель, основанная на модифицированной модели Трансформера и вариационных методах разложения [3,4]; метод выбора характеристик с помощью пакета Boruta при машинной обработке данных [5] и др. В связи с этим затрудняется прогнозирование генерации электрической энергии ветроэнергетическими установками (ВЭУ).…”