Prediction techniques for renewable energy generation and load demand forecasting / Anuradha Tomar, Prerna Gaur, Xiaolong Jin, editors.
This book provides an introduction to forecasting methods for renewable energy sources integrated with existing grid. It consists of two sections; the first one is on the generation side forecasting methods, while the second section deals with the different ways of load forecasting. It broadly inclu...
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| Online Access: |
Full Text (via Springer) |
|---|---|
| Other Authors: | , , |
| Format: | eBook |
| Language: | English |
| Published: |
Singapore :
Springer,
[2023]
|
| Series: | Lecture notes in electrical engineering ;
v. 956. |
| Subjects: |
MARC
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| 245 | 0 | 0 | |a Prediction techniques for renewable energy generation and load demand forecasting / |c Anuradha Tomar, Prerna Gaur, Xiaolong Jin, editors. |
| 264 | 1 | |a Singapore : |b Springer, |c [2023] | |
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| 490 | 1 | |a Lecture notes in electrical engineering ; |v volume 956. | |
| 504 | |a Includes bibliographical references. | ||
| 505 | 0 | |a Artificial Intelligence for renewable energy prediction -- Solar Power Forecasting in Photovoltaic Cells using Machine Learning -- Hybrid techniques for renewable energy prediction -- A Deep Learning-based Islanding Detection Approach by Considering the Load Demand of DGsunder Different Grid Conditions -- Quantitative forecasting techniques-Comparison of PV power production estimation methods under non-homogenous temperature distribution for CPVT systems -- Renewable Energy Predictions: Worldwide Research Trends and Future perspective -- Models in Load forecasting -- Machine Learning techniques for Load forecasting -- Hybrid techniques for Load forecasting-Time Load Forecasting: A smarter expertise through modern methods -- Deep Learning techniques for Load forecasting. | |
| 520 | |a This book provides an introduction to forecasting methods for renewable energy sources integrated with existing grid. It consists of two sections; the first one is on the generation side forecasting methods, while the second section deals with the different ways of load forecasting. It broadly includes artificial intelligence, machine learning, hybrid techniques and other state-of-the-art techniques for renewable energy and load predictions. The book reflects the state of the art in distributed generation system and future microgrids and covers theory, algorithms, simulations and case studies. It offers invaluable insights through this valuable resource to students and researchers working in the fields of renewable energy, integration of renewable energy with existing grid and electrical distribution network. | ||
| 588 | |a Description based on online resource; title from digital title page (viewed on March 08, 2023) | ||
| 650 | 0 | |a Electric power production |x Forecasting. | |
| 650 | 0 | |a Electric power production |x Data processing. | |
| 650 | 0 | |a Renewable energy sources |x Forecasting. | |
| 650 | 0 | |a Renewable energy sources |x Data processing. | |
| 650 | 0 | |a Electric power systems |x Mathematical models. |0 http://id.loc.gov/authorities/subjects/sh2008102904. | |
| 650 | 0 | |a Electric power systems |x Load dispatching. |0 http://id.loc.gov/authorities/subjects/sh85041927. | |
| 700 | 1 | |a Tomar, Anuradha, |e editor. |0 http://id.loc.gov/authorities/names/no2021001451 |1 https://isni.org/isni/0000000493303915. |1 http://isni.org/isni/0000000493303915. | |
| 700 | 1 | |a Gaur, Prerna, |e editor. | |
| 700 | 1 | |a Jin, Xiaolong |c (Electrical engineer), |e editor. |0 http://id.loc.gov/authorities/names/no2022003465. | |
| 776 | 0 | 8 | |i Print version: |t Prediction techniques for renewable energy generation and load demand forecasting. |d Singapore : Springer Nature Singapore, 2023 |z 9789811964893 |w (OCoLC)1359608617. |
| 830 | 0 | |a Lecture notes in electrical engineering ; |v v. 956. |0 http://id.loc.gov/authorities/names/no2007099709. | |
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