Open Data and Energy Analytics

• Andere auteurs: Nastasi, Benedetto (Redacteur), Manfren, Massimiliano (Redacteur), Noussan, Michel (Redacteur)
• Formaat: Elektronisch Hoofdstuk
• Taal: Engels
• Gepubliceerd in: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2020
• Onderwerpen: Research & information: general; 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; data analytics; big data; forecasting; energy; polygeneration; clustering; kNN; pattern recognition; heating; building stock; heat map; spatial analysis; heat density map; building performance simulation; parametric modelling; energy management; model calibration; Passive House; energy planning; energy potential mapping; urban energy atlas; urban energy transition; energy data; data-aware planning; spatial planning; open data analytics; smart cities; open energy governance; urban database; energy mapping; building dataset; energy modelling
• Online toegang: DOAB: download the publication; DOAB: description of the publication

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,...