Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This SpringerBrief presents spatio-temporal data analytics for wind energy integration using stochastic modeling and optimization methods. It explores techniques for efficiently integrating renewable energy generation into bulk power grids. The operational challenges of wind, and its variability are carefully examined. A spatio-temporal analysis approach enables the authors to develop Markov-chain-based short-term forecasts of wind farm power generation. To deal with the wind ramp dynamics, a support vector machine enhanced Markov model is introduced. The stochastic optimization of economic dispatch (ED) and interruptible load management are investigated as well. Spatio-Temporal Data Analytics for Wind Energy Integration is valuable for researchers and professionals working towards renewable energy integration. Advanced-level students studying electrical, computer and energy engineering should also find the content useful.

Wind power industry --- Wind power --- Wind energy --- Windpower --- Power resources --- Renewable energy sources --- Windmills --- Energy industries --- Renewable energy sources. --- Data mining. --- Renewable and Green Energy. --- Data Mining and Knowledge Discovery. --- Energy Policy, Economics and Management. --- Energy Systems. --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Alternate energy sources --- Alternative energy sources --- Energy sources, Renewable --- Sustainable energy sources --- Renewable natural resources --- Agriculture and energy --- Renewable energy resources. --- Energy policy. --- Energy systems. --- Energy and state --- State and energy --- Industrial policy --- Energy conservation --- Government policy --- Energy and state.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This SpringerBrief presents spatio-temporal data analytics for wind energy integration using stochastic modeling and optimization methods. It explores techniques for efficiently integrating renewable energy generation into bulk power grids. The operational challenges of wind, and its variability are carefully examined. A spatio-temporal analysis approach enables the authors to develop Markov-chain-based short-term forecasts of wind farm power generation. To deal with the wind ramp dynamics, a support vector machine enhanced Markov model is introduced. The stochastic optimization of economic dispatch (ED) and interruptible load management are investigated as well. Spatio-Temporal Data Analytics for Wind Energy Integration is valuable for researchers and professionals working towards renewable energy integration. Advanced-level students studying electrical, computer and energy engineering should also find the content useful.

Thermodynamics --- Relation between energy and economics --- Relation between energy and economics --- Electrical engineering --- Applied physical engineering --- Environmental protection. Environmental technology --- Information systems --- energiebeheer (technologie) --- energiemanagement (economie) --- thermodynamica --- windenergie --- energiebeleid --- duurzame energie --- energie-economie --- hernieuwbare energie --- energie (technologie) --- energietechniek --- database management --- elektrotechniek --- data acquisition