Artificial Intelligence for Energy Systems Driving Intelligent, Flexible and Optimal Energy Management /
| Main Authors: | , , |
|---|---|
| Corporate Author: | |
| Summary: | XVII, 266 p. 49 illus., 40 illus. in color. text |
| Language: | English |
| Published: |
Cham :
Springer Nature Switzerland : Imprint: Springer,
2025.
|
| Edition: | 1st ed. 2025. |
| Series: | Learning and Analytics in Intelligent Systems,
46 |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/978-3-031-85209-1 |
| Format: | Electronic Book |
Table of Contents:
- 1.The Climate Crisis and the Four Pillars of Energy Transition: Decarbonization, Digitization, Decentralization, and Democratization
- 2.The Role of Artificial Intelligence in Transforming the Energy Sector: A Comprehensive Review
- 3.Scalable Framework for Intelligent System Architecture to Address Challenges in the Energy Sector
- 4.Deep Learning Models for Short-Term Forecasting of Photovoltaic Energy Production
- 5.Machine Learning-Driven Energy Consumption Forecasting for Building Profiling
- 6.Meta-Learning Approaches for Assessing Energy Efficiency Investments in Buildings
- 7.Ensemble Machine Learning Models for Estimating Energy Savings from Efficiency Measures in Buildings
- 8.Optimization Model for Scheduling Flexible Loads to Mitigate Energy Peaks
- 9.Optimization Model for Electric Vehicle Integration and Energy Storage to Achieve Energy Autonomy
- 10.Future Directions of Intelligent Energy Management and the Role of Generative AI.