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

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 mo...

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Format: Online
Sprog:engelsk
Udgivet: MDPI - Multidisciplinary Digital Publishing Institute 2022
Fag:
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
thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues
Online adgang:ONIX_20220111_9783036512075_81
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Summary: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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