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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

Pombeiro, Henrique ; Santos, Rodolfo ; Carreira, Paulo ; Silva, Carlos ; Sousa, João M.C.

Energy and buildings, 2017-07, Vol.146 (C), p.141-151 [Periódico revisado por pares]

Netherlands: Elsevier B.V

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  • Título:
    Comparative assessment of low-complexity models to predict electricity consumption in an institutional building: Linear regression vs. fuzzy modeling vs. neural networks
  • Autor: Pombeiro, Henrique ; Santos, Rodolfo ; Carreira, Paulo ; Silva, Carlos ; Sousa, João M.C.
  • Assuntos: Energy model ; Fuzzy systems ; Intelligent buildings ; Linear regressions ; Neural networks ; Occupancy estimation
  • É parte de: Energy and buildings, 2017-07, Vol.146 (C), p.141-151
  • Notas: USDOE Office of Nuclear Energy (NE), Nuclear Fuel Cycle and Supply Chain
  • Descrição: •We champion low complexity non-linear model design to accurately predict of building energy consumption.•Model performance is validated over real-word Intelligent Building data using time-of-day, weather data, and WiFi-based occupancy input variables.•Our results validate the superiority of fuzzy and neural network by comparison with linear regression models. Developing energy efficiency strategies for Intelligent Buildings requires a continuous monitoring of the impacts of energy efficiency measures against baseline scenarios. Since not all consumption events can be linearly described, non-linear models based on fuzzy systems and neural networks are successful where linear models either become inaccurate or too complex to be employed in practice. Herein, we study the adequacy of plain-designed non-linear models to predict energy consumption in a real-world Intelligent Building setting by using simple predictor variables such as time-of-day, weather conditions, and occupancy as estimated from WiFi traffic. The developed Fuzzy Systems and Neural Network models achieve considerably better performance and accuracy indexes than linear regression models, with VAFs equal to 79.3% and 75.7% respectively, and MAE of 5.9 and 7.1kWh, respectively, against 36.6% VAF and 12.8kWh MAE for linear regression models, thus demonstrating that low complexity non-linear models can be used to accurately describe energy consumption baselines in Intelligent Buildings.
  • Editor: Netherlands: Elsevier B.V
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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