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Improving Energy Efficiency through Data-Driven Modeling, Simulation and Optimization
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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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Keywords
- ant colony optimization
- anti-icing
- appliance classification
- appliance feature
- big data process
- building energy consumption
- building load forecasting
- Clustering
- CO2 reduction
- convolutional neural network
- Design
- enclosure structure
- energy
- energy baselines
- Energy consumption
- Energy Efficiency
- experimental validation
- field measurement
- Forecasting
- fracturing roofs to maintain entry (FRME)
- Fuel
- heat and mass transfer
- heat load reduction
- heat transfer coefficient
- heating power distribution
- Machine learning
- manufacturing
- meta-heuristics
- modelling
- multi-objective combinatorial optimization
- n/a
- neural methods
- non-intrusive load monitoring
- Numerical Simulation
- optimization method
- passive house
- prediction
- predictive maintenance
- range
- recurrence graph
- Regional
- side abutment pressure
- smart intelligent systems
- strata movement
- Technology, engineering, agriculture
- Technology: general issues
- thermal improved of buildings
- turbo-propeller
- V–I trajectory
- weight
- weighted recurrence graph
Links
DOI: 10.3390/books978-3-0365-1206-8Editions