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Springer Prediction Techniques for Renewable Energy Generation

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£96.32 £36.69 £49.70 £62.71 £75.72 £88.73 £101.74 14 March 2026 01 April 2026 20 April 2026 08 May 2026 27 May 2026

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Description

Enhance your technical knowledge with Prediction Techniques for Renewable Energy Generation and Load Demand Forecasting. Published by Springer as part of the Lecture Notes in Electrical Engineering series (volume 956), this book serves as a specialized resource for professionals and students in the field of electrical engineering and computer science. This text addresses the complexities of energy forecasting by focusing on the intersection of renewable energy sources and load demand prediction. It is a valuable addition to any academic or professional library focused on AI and machine learning applications within the energy sector. By studying these methodologies, readers can better understand the computational approaches used to manage modern power systems and energy distribution.

Key Features

Part of the prestigious Springer Lecture Notes in Electrical Engineering series, volume 956.

Focuses on specialized prediction techniques for renewable energy generation and load demand.

Provides technical insights suitable for the AI and machine learning computer science category.

Developed by Springer to support advanced studies in electrical engineering and energy forecasting.

Serves as a targeted resource for researchers and engineers working with renewable energy systems.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
21 January 2024
Listed Since
26 December 2023

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