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Utilizing Physics-Based Input Features within a Machine Learning Model to Predict Wind Speed Forecasting Error

机译：利用机器学习模型中基于物理的输入特征预测风速预测误差

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Daniel Vassallo; Raghavendra Krishnamurthy; Harindra J. S. Fernando;
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1. Utilizing physics-based input features within a machine learning model to predict wind speed forecasting error [J] . Vassallo Daniel, Krishnamurthy Raghavendra, Fernando Harindra J. S. Wind Energy Science . 2021,第1期

机译：利用机器学习模型中基于物理的输入特征，以预测风速预测误差
2. A robust combination approach for short-term wind speed forecasting and analysis - Combination of the ARIMA (Autoregressive Integrated Moving Average), ELM (Extreme Learning Machine), SVM (Support Vector Machine) and LSSVM (Least Square SVM) forecasts using a GPR (Gaussian Process Regression) model [J] . Wang Jianzhou, Hu Jianming Energy . 2015,第DECaPTa1期

机译：一种用于短期风速预测和分析的强大组合方法-使用GPR（ARIMA（自回归综合移动平均值），ELM（极限学习机），SVM（支持向量机）和LSSVM（最小二乘SVM）预测进行组合高斯过程回归模型
3. A hybrid structure of an extreme learning machine combined with feature selection, signal decomposition and parameter optimization for short-term wind speed forecasting [J] . Sun Sizhou, Fu Jingqi, Zhu Feng, Transactions of the Institute of Measurement and Control . 2020,第1期

机译：极端学习机的混合结构与特征选择，信号分解和参数优化结合短期风速预测
4. Short Term Wind Speed Forecasting : A Machine Learning Based Predictive Analytics [C] . Annael J. Domingo, Felan Carlo Garcia, Mary Lai Salvaña, IEEE Region 10 Conference . 2018

机译：短期风速预测：基于机器学习的预测分析
5. Forecasting Wind Turbine Failures and Associated Costs: Investigating Failure Causes, Effects and Criticalities, Modeling Reliability and Predicting Time-to-Failure, Time-to-Repair and Cost of Failures for Wind Turbines Using Reliability Methods and Machine Learning Techniques [D] . Ozturk, Samet 2019

机译：预测风力涡轮机故障和相关成本：调查故障原因，影响和严重性，建模可靠性并使用可靠性方法和机器学习技术预测风力涡轮机的故障时间，维修时间和故障成本
6. An efficient computational method for predicting drug-target interactions using weighted extreme learning machine and speed up robot features [O] . Ji-Yong An, Fan-Rong Meng, Zi-Ji Yan 2021

机译：一种有效的计算方法用于使用加权极限学习机预测药物目标交互加快机器人特征
7. Utilizing physics-based input features within a machine learning model to predict wind speed forecasting error [O] . Daniel Vassallo, Raghavendra Krishnamurthy, Harindra J. S. Fernando 2021

机译：利用机器学习模型中基于物理的输入特征预测风速预测误差

Utilizing Physics-Based Input Features within a Machine Learning Model to Predict Wind Speed Forecasting Error

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