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Data Analytics for Renewable Energy Integration: Informing the Generation and Distribution of Renewable Energy

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Cover of 'Data Analytics for Renewable Energy Integration: Informing the Generation and Distribution of Renewable Energy'

Table of Contents

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    Book Overview
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    Chapter 1 Solar Energy Forecasting and Optimization System for Efficient Renewable Energy Integration
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    Chapter 2 Gradient Boosting Models for Photovoltaic Power Estimation Under Partial Shading Conditions
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    Chapter 3 Multi-objective Optimization for Power Load Recommendation Considering User’s Comfort
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    Chapter 4 An Approach for Erosion and Power Loss Prediction of Wind Turbines Using Big Data Analytics
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    Chapter 5 Minimizing Grid Interaction of Solar Generation and DHW Loads in nZEBs Using Model-Free Reinforcement Learning
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    Chapter 6 Improving Time-Series Rule Matching Performance for Detecting Energy Consumption Patterns
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    Chapter 7 Probabilistic Wind Power Forecasting by Using Quantile Regression Analysis
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    Chapter 8 Identifying Representative Load Time Series for Load Flow Calculations
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    Chapter 9 Scalable Gaussian Process Models for Solar Power Forecasting
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    Chapter 10 Wind Speed Forecasting Using Statistical and Machine Learning Methods: A Case Study in the UAE
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    Chapter 11 NWP Ensembles for Wind Energy Uncertainty Estimates
Attention for Chapter 4: An Approach for Erosion and Power Loss Prediction of Wind Turbines Using Big Data Analytics
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