Hybrid Neuro-Evolutionary Method for Predicting Wind Turbine Power Output
The University of Adelaide（阿德莱德大学）；Sapienza University of Rome（罗马大学）；The Australian Institute for Machine Learning（澳大利亚机器学习研究院）；KTH Royal Institute of Technology（瑞典皇家理工学院）
Reliable wind turbine power prediction is imperative to the planning, scheduling and control of wind energy farms for stable power production. In recent years Machine Learning (ML) methods have been successfully applied in a wide range of domains, including renewable energy. However, due to the challenging nature of power prediction in wind farms, current models are far short of the accuracy required by industry. In this paper, we deploy a composite ML approach--namely a hybrid neuro-evolutionary algorithm--for accurate forecasting of the power output in wind-turbine farms. We use historical data in the supervisory control and data acquisition (SCADA) systems as input to estimate the power output from an onshore wind farm in Sweden. At the beginning stage, the k-means clustering method and an Autoencoder are employed, respectively, to detect and filter noise in the SCADA measurements. Next, with the prior knowledge that the underlying wind patterns are highly non-linear and diverse, we combine a self-adaptive differential evolution (SaDE) algorithm as a hyper-parameter optimizer, and a recurrent neural network (RNN) called Long Short-term memory (LSTM) to model the power curve of a wind turbine in a farm. Two short time forecasting horizons, including ten-minutes ahead and one-hour ahead, are considered in our experiments. We show that our approach outperforms its counterparts.
An Evolutionary Deep Learning Method for Short-term Wind Speed Prediction: A Case Study of the Lillgrund Offshore Wind Farm
The University of Adelaide（阿德莱德大学）；Sapienza University of Rome（罗马大学）；The Australian Institute for Machine Learning（澳大利亚机器学习研究所）；KTH Royal Institute of Technology（瑞典皇家理工学院）
Neuro-Evolutionary Approach to Physics-Aware Symbolic Regression
Czech Institute of Informatics, Robotics, and Cybernetics CTU in Prague（捷克信息学、机器人学和自动化研究中心CTU布拉格分校）；Dept. of Cognitive Robotics, TU Delft（认知机器人学系代尔夫特理工大学）；CIIRC, CTU in Prague（智能信息研究中心CTU布拉格分校）
Sparsity through evolutionary pruning prevents neuronal networks from overfitting
Friedrich Alexander University Erlangen-Nürnberg (FAU)（埃尔朗根-纽伦堡弗里德里希-亚历山大大学（FAU））；Tel Aviv University (TAU)（特拉维夫大学（TAU））；University Hospital Erlangen（埃尔朗根大学医院）；University of Groningen（格罗宁根大学）；University Medical Center Groningen (UMCG)（格罗宁根大学医学中心（UMCG））
Evolutionary Mapping of Neural Networks to Spatial Accelerators
LMU Munich（慕尼黑大学）；Intel（英特尔）；Intel Corporation（英特尔公司）；SEAS, Harvard University（哈佛大学工程与应用科学学院）；L3S Research Center, Leibniz University Hannover（汉诺威莱布尼茨大学L3S研究中心）；MCML, LMU Munich（慕尼黑大学机器学习中心）；DFKI（德国人工智能研究中心）
