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Wind power prediction in new stations based on knowledge of existing Stations: A cluster based multi source domain adaptation approach

机译：基于现有电站知识的新电站风电功率预测：基于集群的多源域自适应方法

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Historical wind power production figures are not available when a new wind farm goes into power production. It is thus difficult to forecast power productions of such wind farms that is required for demand management. Wind power is a function of weather variables and it is likely that weather patterns of the new station is similar to some existing operational wind farms. It will thus be interesting to investigate how the forecast/prediction models of the existing wind farms can be adapted to generate a prediction model for new stations. On this regard, we explore a particular branch of machine learning called Multi Source Domain Adaptation (MSDA). MSDA approaches identify a weighing mechanism to fuse the predictions from the source models (i.e. existing stations) to produce a prediction for the target (i.e. new station). The weights are computed based on similarity of data distributions between source and target. Conventional MSDA approaches utilise an instance based weighting scheme and we identified that fails to capture the data distribution of wind data sets appropriately. We thus propose a novel cluster based MSDA approach that captures wind data distribution in terms of natural groups that exist within data and compute distribution similarity (and source weight) in terms of cluster distributions. Experimental results demonstrate that cluster based MSDA approach can reduce regression error by 20.63% over instance based MSDA approach. (C) 2018 Elsevier B.V. All rights reserved.
机译：当新的风电场投入发电时，将无法获得历史性的风力发电量数据。因此，难以预测需求管理所需的这种风电场的发电量。风力是天气变量的函数，新电站的天气模式可能类似于某些现有的运营风电场。因此，有趣的是研究现有风电场的预测/预测模型如何适应以生成新电站的预测模型。在这方面，我们探索了机器学习的一个特定分支，称为多源域适配（MSDA）。 MSDA方法确定一种加权机制，以融合来自源模型（即现有站点）的预测以生成针对目标（即新站点）的预测。权重是基于源和目标之间数据分布的相似性来计算的。常规的MSDA方法利用基于实例的加权方案，因此我们确定无法正确捕获风数据集的数据分布。因此，我们提出了一种新颖的基于群集的MSDA方法，该方法可以根据数据中存在的自然组捕获风数据分布，并根据群集分布计算分布相似度（和源权重）。实验结果表明，与基于实例的MSDA方法相比，基于群集的MSDA方法可以将回归误差降低20.63％。 （C）2018 Elsevier B.V.保留所有权利。

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