Reprint

Open Data and Energy Analytics

Edited by
June 2020
218 pages
  • ISBN978-3-03936-218-9 (Paperback)
  • ISBN978-3-03936-219-6 (PDF)

This book is a reprint of the Special Issue Open Data and Energy Analytics that was published in

Chemistry & Materials Science
Engineering
Environmental & Earth Sciences
Physical Sciences
Summary
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.
Format
  • Paperback
License
© 2020 by the authors; CC BY-NC-ND license
Keywords
data envelopment analysis; Kohonen self-organizing maps; factor analysis; multiple regression; energy efficiency; social media; energy-consuming activities; energy consumption; machine learning; ontology; energy performance certificate; heating energy demand; buildings; data mining; classification; regression; decision tree; support vector machine; random forest; artificial neural network; open data; electrification modelling; Malawi; OnSSET; MESSAGEix; reproducibility; collaborative work; open modelling and data; data-handling; integrated assessment modelling; data pre- and post-processing; space heating; domestic hot water; market assessment; EU28; district heating; open data; data analytics; big data; forecasting; energy; polygeneration; clustering; kNN; pattern recognition; open data; heating; building stock; heat map; spatial analysis; heat density map; building performance simulation; parametric modelling; energy management; model calibration; energy efficiency; Passive House; energy planning; energy potential mapping; urban energy atlas; urban energy transition; energy data; data-aware planning; spatial planning; open data analytics; energy planning; smart cities; open energy governance; urban database; energy mapping; building dataset; energy modelling; data mining; machine learning