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Solar Power Forecasting: Using Time Series and Machine Learning
by Gautam, Natarajan
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ISBN: • Publisher: CRC Press • Year: 2027
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🌐 [color=#55acee]Language: English
📄 [color=#44bb44]Pages: 201
Description: This book takes an approach that leverages methods using time series analysis, machine learning, and stochastic models to effectively forecast solar power. The goal of this book is not only to produce an accurate forecast but also to make it conducive to be used for decision-making. Solar Power Forecasting: Using Time Series and Machine Learning combines traditional forecasting with recent advances in machine learning and data science. It uses a decision-making-oriented approach and provides probabilistic forecasts and methods, as well as explains the analytical underpinnings of accuracy metrics in detail. As it illustrates through examples of how forecasting can be used in planning and operations, the book also delivers a systems-level approach. The book is a single source of information for various aspects of solar forecasting such as data science methods, computational aspects, and mathematical foundations. It is useful for practitioners, students, and seasoned researchers not only in the solar power field but also for forecasting in general. Color figures can be found on Routledge.com/9781032515328
📦 [color=#ff9900]Download Info
Folder: Solar Power Forecasting Using Time Series And Machine Learning
Format: PDF
Total Size: 79.7 MB
📋 File List:
[size=2]
📌 Solar Power Forecasting.epub (Gautam, Natarajan) (2027) (20.22 MB)
📌 Solar Power Forecasting.pdf (Gautam, Natarajan) (59.48 MB)
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