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Data Analytics for Renewable Energy Integration. Technologies, Systems and Society

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Cover of 'Data Analytics for Renewable Energy Integration. Technologies, Systems and Society'

Table of Contents

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    Book Overview
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    Chapter 1 Mathematical Optimization of Design Parameters of Photovoltaic Module
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    Chapter 2 Fused Lasso Dimensionality Reduction of Highly Correlated NWP Features
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    Chapter 3 Sampling Strategies for Representative Time Series in Load Flow Calculations
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    Chapter 4 Probabilistic Graphs for Sensor Data-Driven Modelling of Power Systems at Scale
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    Chapter 5 Renewable Energy Integration: Bayesian Networks for Probabilistic State Estimation
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    Chapter 6 Deep Learning for Wave Height Classification in Satellite Images for Offshore Wind Access
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    Chapter 7 Machine Learning as Surrogate to Building Performance Simulation: A Building Design Optimization Application
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    Chapter 8 Clustering River Basins Using Time-Series Data Mining on Hydroelectric Energy Generation
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    Chapter 9 Short-Term Electricity Consumption Forecast Using Datasets of Various Granularities
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    Chapter 10 Intelligent Monitoring of Transformer Insulation Using Convolutional Neural Networks
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    Chapter 11 Nonintrusive Load Monitoring Based on Deep Learning
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    Chapter 12 Urban Climate Data Sensing, Warehousing, and Analysis: A Case Study in the City of Abu Dhabi, United Arab Emirates
Attention for Chapter 5: Renewable Energy Integration: Bayesian Networks for Probabilistic State Estimation
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Citations

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Chapter title
Renewable Energy Integration: Bayesian Networks for Probabilistic State Estimation
Chapter number 5
Book title
Data Analytics for Renewable Energy Integration. Technologies, Systems and Society
Published by
Springer, Cham, September 2018
DOI 10.1007/978-3-030-04303-2_5
Book ISBNs
978-3-03-004302-5, 978-3-03-004303-2
Authors

Ole J. Mengshoel, Priya K. Sundararajan, Erik Reed, Dongzhen Piao, Briana Johnson, Mengshoel, Ole J., Sundararajan, Priya K., Reed, Erik, Piao, Dongzhen, Johnson, Briana

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 3 33%
Student > Master 2 22%
Researcher 1 11%
Unknown 3 33%
Readers by discipline Count As %
Engineering 6 67%
Unknown 3 33%

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