Search results for "Building energy demand"

showing 2 items of 2 documents

Artificial Neural Networks to assess energy and environmental performance of buildings: An Italian case study

2019

Abstract Approximately 40% of the European energy consumption and a large proportion of environmental impacts are related to the building sector. However, the selection of adequate and correct designs can provide considerable energy savings and reduce environmental impacts. To achieve this objective, a simultaneous energy and environmental assessment of a building's life cycle is necessary. To date, the resolution of this complex problem is entrusted to numerous software and calculation algorithms that are often complex to use. They involve long diagnosis phases and are characterised by the lack of a common language. Despite the efforts by the scientific community in the building sector, th…

Artificial neural networkDecision support systemSettore ICAR/12 - Tecnologia dell'ArchitetturaDecision support toolComputer science020209 energyStrategy and ManagementSettore ICAR/11 - Produzione EdiliziaEnergy balance02 engineering and technologyBuilding energy demandNetwork topologyIndustrial and Manufacturing EngineeringEnvironmental dataEnvironmental impactLife cycle assessmentSoftware0202 electrical engineering electronic engineering information engineeringEnvironmental impact assessmentLife-cycle assessment0505 lawGeneral Environmental ScienceArtificial neural networkRenewable Energy Sustainability and the Environmentbusiness.industry05 social sciencesEnergy consumptionEnvironmental impactsIndustrial engineeringArtificial neural network; Building energy demand; Decision support tool; Energy balance; Environmental impacts; Life cycle assessment050501 criminologybusinessJournal of Cleaner Production
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Building energy performance forecasting: A multiple linear regression approach

2019

Abstract Different ways to evaluate the building energy balance can be found in literature, including comprehensive techniques, statistical and machine-learning methods and hybrid approaches. The identification of the most suitable approach is important to accelerate the preliminary energy assessment. In the first category, several numerical methods have been developed and implemented in specialised software using different mathematical languages. However, these tools require an expert user and a model calibration. The authors, in order to overcome these limitations, have developed an alternative, reliable linear regression model to determine building energy needs. Starting from a detailed …

Decision support systemComputer scienceCalibration (statistics)020209 energy02 engineering and technologyManagement Monitoring Policy and LawBuilding energy demandsymbols.namesake020401 chemical engineeringLinear regression0202 electrical engineering electronic engineering information engineeringSensitivity (control systems)0204 chemical engineeringReliability (statistics)Multiple linear regressionSettore ING-IND/11 - Fisica Tecnica AmbientaleMechanical EngineeringBuilding and ConstructionIndustrial engineeringPearson product-moment correlation coefficientDynamic simulationIdentification (information)Black box methodGeneral EnergysymbolsForecast methodSensitivity analysisDynamic simulation
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