Improving Energy Efficiency through Data-Driven Modeling, Simulation and Optimization
Overview & Synopsis
In October 2014, the EU leaders agreed upon three key targets for the year 2030: a reduction by at least 40% in greenhouse gas emissions, savings of at least 27% for renewable energy, and improvements by at least 27% in energy efficiency. The increase in computational power combined with advanced modeling and simulation tools makes it possible to derive new technological solutions that can enhance the energy efficiency of systems and that can reduce the ecological footprint. This book compiles 10 novel research works from a Special Issue that was focused on data-driven approaches, machine learning, or artificial intelligence for the modeling, simulation, and optimization of energy systems.
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Document Information
| Document Title | Improving Energy Efficiency through Data-Driven Modeling, Simulation and Optimization |
|---|---|
| Author / Organization | DOAB Contributor |
| Subject & Discipline | passive house |
| Categories | Open Access Books |
| Publication Year | 2022 |
| License / Rights | Open Access / Creative Commons |
| Resource Verification | Listed PDF (Verification Pending) |
| Original Host Website |
Directory of Open Access Books
(www.doabooks.org)
|
| File Format | application/pdf (Portable Document Format) |
| Language | EN |
| Catalog ID | DOC-292749 |
| Original Source Link | Visit Source Page on www.doabooks.org » |