Short-Term Wind Energy Forecasting with Independent daytime/Nighttime machine Learning Models

Abstract

Many renewable energy resources, including wind energy, are uncertain and often unavailable when needed, with high variability and dependency on atmospheric and climatic conditions. Variability and uncertainty of wind energy follow those wind speed and occur at multiple timescales; that is, from seconds to minutes and then to hours. They also require movement of other resources to ensure the balance between generation and load. This requires an accurate wind speed prediction. However, most existing wind speed forecasting models are based on data that do not take into account the difference between day and night, which can limit the accuracy of wind speed forecasting. Therefore, to improve the prediction accuracy, this study proposes three scenarios for wind speed prediction: first, the prediction model does not take into account the difference between day and night. The second model uses only data recorded during the day, and the third model deals with data measured during the night only. A comparative analysis and comprehensive evaluation will be given at the end of this work to verify the proposed hypothesis. The recorded climate data and measured wind speed at AUMET station will be used in this study.