Improving Energy Efficiency through Data-Driven Modeling, Simulation and Optimization

doi:10.3390/books978-3-0365-1206-8

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https://mdpi.com/books/pdfview/book/3770

Contributor(s)

Deschrijver, Dirk (editor)

Language

English

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Abstract

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.

URI

https://directory.doabooks.org/handle/20.500.12854/76345

Keywords

passive house; enclosure structure; heat transfer coefficient; energy consumption; turbo-propeller; regional; fuel; weight; range; design; CO2 reduction; multi-objective combinatorial optimization; meta-heuristics; ant colony optimization; non-intrusive load monitoring; appliance classification; appliance feature; recurrence graph; weighted recurrence graph; V–I trajectory; convolutional neural network; energy baselines; machine learning; clustering; neural methods; smart intelligent systems; building energy consumption; building load forecasting; energy efficiency; thermal improved of buildings; anti-icing; heat and mass transfer; heating power distribution; heat load reduction; optimization method; experimental validation; big data process; predictive maintenance; fracturing roofs to maintain entry (FRME); field measurement; numerical simulation; side abutment pressure; strata movement; energy; manufacturing; prediction; forecasting; modelling; n/a