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Deep Highway Networks and Tree-Based Ensemble for Predicting Short-Term Building Energy Consumption

Author

Listed:
  • Muhammad Waseem Ahmad

    (BRE Trust Centre for Sustainable Engineering, School of Engineering, Cardiff University, Cardiff CF24 3AA, UK)

  • Anthony Mouraud

    (Commissariat á l’énergie atomique et aux énergies alternatives (CEA), CEA Tech en Région (CTREG), Département Grand Ouest (DGDO), 44340 Bouguenais, France)

  • Yacine Rezgui

    (BRE Trust Centre for Sustainable Engineering, School of Engineering, Cardiff University, Cardiff CF24 3AA, UK)

  • Monjur Mourshed

    (BRE Trust Centre for Sustainable Engineering, School of Engineering, Cardiff University, Cardiff CF24 3AA, UK)

Abstract

Predictive analytics play a significant role in ensuring optimal and secure operation of power systems, reducing energy consumption, detecting fault and diagnosis, and improving grid resilience. However, due to system nonlinearities, delay, and complexity of the problem because of many influencing factors (e.g., climate, occupants’ behaviour, occupancy pattern, building type), it is a challenging task to get accurate energy consumption prediction. This paper investigates the accuracy and generalisation capabilities of deep highway networks (DHN) and extremely randomized trees (ET) for predicting hourly heating, ventilation and air conditioning (HVAC) energy consumption of a hotel building. Their performance was compared with support vector regression (SVR), a most widely used supervised machine learning algorithm. Results showed that both ET and DHN models marginally outperform the SVR algorithm. The paper also details the impact of increasing the deep highway network’s complexity on its performance. The paper concludes that all developed models are equally applicable for predicting hourly HVAC energy consumption. Possible reasons for the minimum impact of DHN complexity and future research work are also highlighted in the paper.

Suggested Citation

  • Muhammad Waseem Ahmad & Anthony Mouraud & Yacine Rezgui & Monjur Mourshed, 2018. "Deep Highway Networks and Tree-Based Ensemble for Predicting Short-Term Building Energy Consumption," Energies, MDPI, vol. 11(12), pages 1-21, December.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:12:p:3408-:d:188132
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    Cited by:

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    2. Lara Ramadan & Isam Shahrour & Hussein Mroueh & Fadi Hage Chehade, 2021. "Use of Machine Learning Methods for Indoor Temperature Forecasting," Future Internet, MDPI, vol. 13(10), pages 1-18, September.
    3. Razak Olu-Ajayi & Hafiz Alaka & Hakeem Owolabi & Lukman Akanbi & Sikiru Ganiyu, 2023. "Data-Driven Tools for Building Energy Consumption Prediction: A Review," Energies, MDPI, vol. 16(6), pages 1-20, March.
    4. Kasım Zor & Özgür Çelik & Oğuzhan Timur & Ahmet Teke, 2020. "Short-Term Building Electrical Energy Consumption Forecasting by Employing Gene Expression Programming and GMDH Networks," Energies, MDPI, vol. 13(5), pages 1-24, March.
    5. Dongsu Kim & Jongman Lee & Sunglok Do & Pedro J. Mago & Kwang Ho Lee & Heejin Cho, 2022. "Energy Modeling and Model Predictive Control for HVAC in Buildings: A Review of Current Research Trends," Energies, MDPI, vol. 15(19), pages 1-30, October.

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