A new multi-objective ensemble wind speed forecasting system: Mixed-frequency interval-valued modeling paradigm

Abstract

Improving wind speed prediction is essential for increasing the use of wind energy and promoting sustainable utilization of resources. Most previous studies relied on single-valued data with a common frequency, resulting in limited wind energy utilization. To fill this research gap and strengthen the precision of wind speed prediction, this study proposes a new multi-objective ensemble forecasting system based on a mixed-frequency interval-valued modeling paradigm for power system decision-making. The system comprises three modules: interval-valued data preprocessing module, mixed-frequency interval-valued forecasting module, and multi-objective ensemble module. Firstly, to reduce the data complexity by preprocessing the mixed-frequency interval-valued wind speed data, an interval-valued data preprocessing module is designed. Then, different types of prediction models, including a mixed data sampling model, two artificial intelligence models, and a traditional statistical model, are used to predict each subsequence in the mixed-frequency interval-valued forecasting module. Finally, a multi-objective ensemble module is designed to ensemble the interval-valued prediction results of each sub-model, and the final interval-valued wind speed forecast results are obtained. The proposed system obtained IMAE values of 0.4634 and 0.3452 at sites from Inner Mongolia and Penglai, China, respectively; this provides a significant tool for wind speed prediction.