Abstract Renewable energy has taken a center stage in sustainable and environmentally safe power generation. In this work, a novel model free Reinforcement Learning based wind speed forecasting technique has been proposed. Our technique uses modified fuzzy Q learning (MFQL) framework to accurately predict 1-min ahead wind speed from the publicly available data online. Empirical Mode Decomposition (EMD) and Pearson’s correlation coefficient have been used in the pre-processing stages to identify seven most relevant intrinsic mode functions (IMF) from the raw wind speed data. These IMFs form the inputs to an MFQL based forecaster which accurately forecasts wind speed using a reward/punishment approach. MFQL predictor is put to test on wind speed data obtained from National Institute of Wind Energy and Wind Resource Assessment data portal for 10 Indian cities, i.e., Bhogat, Chandori, Kotada, Charanka, Gandhi Nagar, Jambua, Keshod, Sadodar, Surat and Vartej located in the state of Gujarat, India. Our predictor is able to achieve an accuracy of 96.23% for Gandhi Nagar, 94.84% for Bhogat, 94.12% for Kotada and similar results are obtained for other locations. MFQL approach has been compared with SVR and k-NN. Results show that MFQL approach has higher accuracy than SVR and k-NN techniques.

中文翻译：

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基于改进模糊Q学习的风速预测

摘要 可再生能源已成为可持续和环境安全发电的中心舞台。在这项工作中，提出了一种新的基于无模型强化学习的风速预测技术。我们的技术使用改进的模糊 Q 学习 (MFQL) 框架从在线公开数据中准确预测 1 分钟前方风速。经验模式分解 (EMD) 和 Pearson 相关系数已用于预处理阶段，以从原始风速数据中识别七个最相关的固有模式函数 (IMF)。这些 IMF 形成基于 MFQL 的预测器的输入，该预测器使用奖励/惩罚方法准确预测风速。MFQL 预测器用于测试从印度国家风能研究所和风资源评估数据门户获得的风速数据，包括 Bhogat、Chandori、Kotada、Charanka、Gandhi Nagar、Jambua、Keshod、Sadodar、Surat 和 Vartej位于印度古吉拉特邦。我们的预测器能够实现 Gandhi Nagar 的 96.23%、Bhogat 的 94.84%、Kotada 的 94.12% 的准确率，其他位置也获得了类似的结果。MFQL 方法已与 SVR 和 k-NN 进行了比较。结果表明，MFQL 方法比 SVR 和 k-NN 技术具有更高的准确性。Kotada 为 12%，其他位置也获得了类似的结果。MFQL 方法已与 SVR 和 k-NN 进行了比较。结果表明，MFQL 方法比 SVR 和 k-NN 技术具有更高的准确性。Kotada 为 12%，其他位置也获得了类似的结果。MFQL 方法已与 SVR 和 k-NN 进行了比较。结果表明，MFQL 方法比 SVR 和 k-NN 技术具有更高的准确性。