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Comparative assessment of low-complexity models to predict electricity consumption in an institutional building: Linear regression vs. fuzzy modeling vs. neural networks

https://doi.org/10.1016/j.enbuild.2017.04.032
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Outgoing claims: the papers this work cites, builds on, or argues with.

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New artificial neural network prediction method for electrical consumption forecasting based on building end-uses
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ENERNET: Studying the dynamic relationship between building occupancy and energy consumption
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inferredcites →https://doi.org/10.1016/j.enbuild.2015.09.042openalex-snapshot
Investigation of cooling load reduction in buildings by passive cooling options applied on roof
inferredcites →https://doi.org/10.1016/j.energy.2009.06.034openalex-snapshot
Electricity consumption forecasting in Italy using linear regression models
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Greek long-term energy consumption prediction using artificial neural networks
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3I Buildings: Intelligent, Interactive and Immersive Buildings
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inferredcites →https://doi.org/10.1016/j.rser.2014.07.053openalex-snapshot
Optimisation of energy management in commercial buildings with weather forecasting inputs: A review
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Building thermal energy modeling with loss minimization
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inferredcites →https://doi.org/10.1109/cec.2000.870268openalex-snapshot
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inferredcites →https://doi.org/10.1109/icdmw40635.2016openalex-snapshot
2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW)

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