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210512 ||| eng |
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|a books978-3-03936-219-6
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|a 9783039362189
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|a 9783039362196
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| 100 |
1 |
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|a Nastasi, Benedetto
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| 245 |
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|a Open Data and Energy Analytics
|h Elektronische Ressource
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| 260 |
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|a Basel, Switzerland
|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2020
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| 300 |
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|a 1 electronic resource (218 p.)
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| 653 |
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|a machine learning
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| 653 |
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|a data analytics
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| 653 |
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|a data mining
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| 653 |
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|a domestic hot water
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| 653 |
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|a heat density map
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| 653 |
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|a building dataset
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| 653 |
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|a random forest
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| 653 |
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|a energy performance certificate
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| 653 |
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|a forecasting
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| 653 |
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|a building stock
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| 653 |
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|a district heating
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| 653 |
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|a open energy governance
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| 653 |
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|a kNN
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| 653 |
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|a urban energy atlas
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| 653 |
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|a energy consumption
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| 653 |
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|a big data
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| 653 |
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|a urban database
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| 653 |
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|a energy efficiency
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| 653 |
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|a energy mapping
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| 653 |
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|a open modelling and data
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| 653 |
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|a EU28
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| 653 |
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|a data envelopment analysis
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| 653 |
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|a electrification modelling
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| 653 |
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|a data pre- and post-processing
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| 653 |
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|a social media
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| 653 |
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|a smart cities
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| 653 |
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|a energy planning
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| 653 |
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|a artificial neural network
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| 653 |
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|a heat map
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| 653 |
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|a spatial planning
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| 653 |
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|a open data analytics
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| 653 |
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|a energy potential mapping
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| 653 |
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|a ontology
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| 653 |
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|a market assessment
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| 653 |
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|a space heating
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| 653 |
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|a clustering
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| 653 |
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|a heating
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| 653 |
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|a buildings
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| 653 |
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|a Kohonen self-organizing maps
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| 653 |
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|a open data
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| 653 |
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|a energy management
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| 653 |
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|a Passive House
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| 653 |
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|a reproducibility
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| 653 |
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|a regression
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| 653 |
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|a support vector machine
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| 653 |
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|a energy
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| 653 |
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|a data-aware planning
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| 653 |
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|a heating energy demand
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| 653 |
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|a factor analysis
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| 653 |
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|a urban energy transition
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| 653 |
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|a MESSAGEix
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| 653 |
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|a classification
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| 653 |
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|a energy data
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| 653 |
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|a energy modelling
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| 653 |
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|a building performance simulation
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| 653 |
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|a OnSSET
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| 653 |
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|a decision tree
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| 653 |
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|a Malawi
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| 653 |
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|a data-handling
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| 653 |
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|a pattern recognition
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| 653 |
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|a integrated assessment modelling
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| 653 |
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|a spatial analysis
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| 653 |
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|a collaborative work
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| 653 |
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|a polygeneration
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| 653 |
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|a energy-consuming activities
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| 653 |
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|a Research and information: general / bicssc
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| 653 |
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|a multiple regression
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| 653 |
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|a parametric modelling
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| 653 |
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|a model calibration
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| 700 |
1 |
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|a Manfren, Massimiliano
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| 700 |
1 |
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|a Noussan, Michel
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| 700 |
1 |
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|a Nastasi, Benedetto
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b DOAB
|a Directory of Open Access Books
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| 500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
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| 028 |
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|a 10.3390/books978-3-03936-219-6
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| 856 |
4 |
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|u https://www.mdpi.com/books/pdfview/book/2449
|7 0
|x Verlag
|3 Volltext
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| 856 |
4 |
2 |
|u https://directory.doabooks.org/handle/20.500.12854/68684
|z DOAB: description of the publication
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|a 000
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|a 333
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|a 320
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|a 700
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|a 340
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| 520 |
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|a Open data and policy implications coming from data-aware planning entail collection and pre- and postprocessing as operations of primary interest. Before these steps, making data available to people and their decision-makers is a crucial point. Referring to the relationship between data and energy, public administrations, governments, and research bodies are promoting the construction of reliable and robust datasets to pursue policies coherent with the Sustainable Development Goals, as well as to allow citizens to make informed choices. Energy engineers and planners must provide the simplest and most robust tools to collect, process, and analyze data in order to offer solid data-based evidence for future projections in building, district, and regional systems planning. This Special Issue aims at providing the state-of-the-art on open-energy data analytics; its availability in the different contexts, i.e., country peculiarities; and its availability at different scales, i.e., building, district, and regional for data-aware planning and policy-making. For all the aforementioned reasons, we encourage researchers to share their original works on the field of open data and energy analytics. Topics of primary interest include but are not limited to the following: 1. Open data and energy sustainability; 2. Open data science and energy planning; 3. Open science and open governance for sustainable development goals; 4. Key performance indicators of data-aware energy modelling, planning, and policy; 5. Energy, water, and sustainability database for building, district, and regional systems; 6. Best practices and case studies.
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