TY  - GEN
AU  - Deschrijver,Dirk
AU  - Deschrijver,Dirk
TI  - Improving Energy Efficiency through Data-Driven Modeling, Simulation and Optimization
SN  - books978-3-0365-1206-8
PY  - 2021///
CY  - Basel, Switzerland
PB  - MDPI - Multidisciplinary Digital Publishing Institute
KW  - Technology: general issues
KW  - bicssc
KW  - passive house
KW  - enclosure structure
KW  - heat transfer coefficient
KW  - energy consumption
KW  - turbo-propeller
KW  - regional
KW  - fuel
KW  - weight
KW  - range
KW  - design
KW  - CO2 reduction
KW  - multi-objective combinatorial optimization
KW  - meta-heuristics
KW  - ant colony optimization
KW  - non-intrusive load monitoring
KW  - appliance classification
KW  - appliance feature
KW  - recurrence graph
KW  - weighted recurrence graph
KW  - V-I trajectory
KW  - convolutional neural network
KW  - energy baselines
KW  - machine learning
KW  - clustering
KW  - neural methods
KW  - smart intelligent systems
KW  - building energy consumption
KW  - building load forecasting
KW  - energy efficiency
KW  - thermal improved of buildings
KW  - anti-icing
KW  - heat and mass transfer
KW  - heating power distribution
KW  - heat load reduction
KW  - optimization method
KW  - experimental validation
KW  - big data process
KW  - predictive maintenance
KW  - fracturing roofs to maintain entry (FRME)
KW  - field measurement
KW  - numerical simulation
KW  - side abutment pressure
KW  - strata movement
KW  - energy
KW  - manufacturing
KW  - prediction
KW  - forecasting
KW  - modelling
KW  - n/a
N1  - Open Access
N2  - 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
UR  - https://mdpi.com/books/pdfview/book/3770
UR  - https://directory.doabooks.org/handle/20.500.12854/76345
ER  -