Titlebook: Data Analytics for Renewable Energy Integration: Informing the Generation and Distribution of Renewa; 5th ECML PKDD Worksh Wei Lee Woon,Zey

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An Approach for Erosion and Power Loss Prediction of Wind Turbines Using Big Data Analytics,In this paper, we propose the Wind Turbine Erosion Predictor (WTEP) System that uses big data analytics to handle the data volume, variety, and veracity and estimate the turbines erosion rate, in addition to the total power loss. WTEP proposes an optimized flexible multiple regression technique. Exp

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,Improving Time-Series Rule Matching Performance for Detecting Energy Consumption?Patterns,Euclidean distance to search candidate rules occurrences. However this distance is not adapted for energy consumption data because occurrences of patterns should have different duration. We propose to use more “elastic” distance measures. In this paper we will compare the detection performance of pr

ostrish

Probabilistic Wind Power Forecasting by Using Quantile Regression Analysis,esents a probabilistic wind power forecasting method based on local quantile regression with Gaussian distribution. The method is applied to obtain probabilistic wind power forecasts, within the course of the Wind Power Monitoring and Forecast Center for Turkey (R?TM) project, which has been realize

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I. Gohberg,M. A. Kaashoek,S. Goldbergire the resulting energy consumption, self-consumption, and self-sufficiency. The results show an increase of individual self-consumption between 17% and 348% and self-sufficiency between 18% and 72%. This results in an additional monetary benefit for the occupants based on the transition proposals

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I. Gohberg,M. A. Kaashoek,S. GoldbergEuclidean distance to search candidate rules occurrences. However this distance is not adapted for energy consumption data because occurrences of patterns should have different duration. We propose to use more “elastic” distance measures. In this paper we will compare the detection performance of pr

Cocker

I. Gohberg,M. A. Kaashoek,S. Goldbergesents a probabilistic wind power forecasting method based on local quantile regression with Gaussian distribution. The method is applied to obtain probabilistic wind power forecasts, within the course of the Wind Power Monitoring and Forecast Center for Turkey (R?TM) project, which has been realize