Local-pattern-aware forecast of regional wind power: Adaptive partition and long-short-term matching

Abstract Importance for the accurate forecast of wind region with multiple wind farms is gradually emerging. As influenced by the geographical features of the wind region, the power output from each wind farm is closely correlated to the local-patterns of its covered weather. However, modeling the highly time-varying nature of the local-patterns’ spatial distribution remains the key challenge to regional wind power forecast. For this purpose, a sub-region is proposed to represent the spatial scale of wind farms covered by the same local-pattern. All wind farms in the wind region are divided into multiple sub-regions. This classification is defined as the partition which represents a typical state of the wind region. To deal with the time-varying nature, partitions are considered on the adaptive process. In this paper, a regional wind power forecasting method based on adaptive partition and long-short-term matching is proposed. First, a refined partition set of wind region is determined by the Regional Hierarchical Clustering algorithm. Second, to identify the current states of the wind region, the partition with minimum forecasting error is chosen as Optimal Partition. Third, the long-short-term matching strategy is proposed to find the adaptive partition among the refined partition set with the indication of recent and historical Optimal Partitions. Eventually, for each time horizon, the forecasted power of each sub-regions in the adaptive partition is aggregated to achieve the final regional wind power forecasting results. The superior performance and robustness of the proposed methods are validated with actual wind generation data from a wind region which contains nine wind farms in China. The ability to capture wind farm local-pattern of the proposed method is also approved.