Forecasting Time-Series Energy Data in Buildings Using an Additive Artificial Intelligence Model for Improving Energy Efficiency

Building energy efficiency is important because buildings consume a significant energy amount. The study proposed additive artificial neural networks (AANNs) for predicting energy use in residential buildings. A dataset in hourly resolution was used to evaluate the AANNs model, which was collected f...

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Veröffentlicht in:	Computational intelligence and neuroscience 2021, Vol.2021 (1), p.6028573-6028573
Hauptverfasser:	Truong, Ngoc-Son, Ngo, Ngoc-Tri, Pham, Anh-Duc
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Air conditioning Architecture and energy conservation Art galleries & museums Artificial Intelligence Artificial neural networks Carbon Comparative analysis Conservation of Energy Resources Design Electricity Energy consumption Energy efficiency Energy industry Energy management Energy management systems Energy use Engineering Forecasting Green buildings HVAC Investigations Literature reviews Mathematical models Methods Model accuracy Neural networks Neural Networks, Computer Regression models Residential buildings Residential energy Support vector machines Time series Ventilation
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