A framework for data mining in wind power time series

TY - GEN

T1 - A framework for data mining in wind power time series

AU - Kramer, Oliver

AU - Gieseke, Fabian Cristian

AU - Heinermann, Justin

AU - Poloczek, Jendrik

AU - Treiber, Nils André

N1 - Conference code: 2

PY - 2014

Y1 - 2014

N2 - Wind energy is playing an increasingly important part for ecologically friendly power supply. The fast growing infrastructure of wind turbines can be seen as large sensor system that screens the wind energy at a high temporal and spatial resolution. The resulting databases consist of huge amounts of wind energy time series data that can be used for prediction, controlling, and planning purposes. In this work, we describe WindML, a Python-based framework for wind energy related machine learning approaches. The main objective of WindML is the continuous development of tools that address important challenges induced by the growing wind energy information infrastructures. Various examples that demonstrate typical use cases are introduced and related research questions are discussed. The different modules of WindML reach from standard machine learning algorithms to advanced techniques for handling missing data and monitoring high-dimensional time series.

AB - Wind energy is playing an increasingly important part for ecologically friendly power supply. The fast growing infrastructure of wind turbines can be seen as large sensor system that screens the wind energy at a high temporal and spatial resolution. The resulting databases consist of huge amounts of wind energy time series data that can be used for prediction, controlling, and planning purposes. In this work, we describe WindML, a Python-based framework for wind energy related machine learning approaches. The main objective of WindML is the continuous development of tools that address important challenges induced by the growing wind energy information infrastructures. Various examples that demonstrate typical use cases are introduced and related research questions are discussed. The different modules of WindML reach from standard machine learning algorithms to advanced techniques for handling missing data and monitoring high-dimensional time series.

U2 - 10.1007/978-3-319-13290-7_8

DO - 10.1007/978-3-319-13290-7_8

M3 - Article in proceedings

SN - 978-3-319-13289-1

T3 - Lecture notes in computer science

SP - 97

EP - 107

BT - Data analytics for renewable energy integration

A2 - Woon, Wei Lee

A2 - Aung, Zeyar

A2 - Madnick, Stuart

PB - Springer

T2 - Second International Workshop on Data Analytics for Renewable Energy Integration

Y2 - 19 September 2014 through 19 September 2014

ER -