AIMS Energy, 2020, 8(5): 783-801. doi: 10.3934/energy.2020.5.783.

Research article

Export file:


  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text


  • Citation Only
  • Citation and Abstract

Data-driven predictive models for daily electricity consumption of academic buildings

1 Department of Mechanical Engineering, Mirpur University of Science and Technology (MUST), Mirpur 10250, AJK, Pakistan
2 Faculty of Engineering, Science and the Built Environment, London South Bank University, London SE1 0AA, UK
3 Department of Electrical Engineering, Mirpur University of Science and Technology (MUST), Mirpur 10250, AJK, Pakistan
4 Department of Computer Systems Engineering, Mirpur University of Science and Technology (MUST), Mirpur 10250, AJK, Pakistan

Academic buildings in a typical university campus occupy 42% of the total space and are responsible for nearly 50 percent of the total energy use and carbon emissions of the campus. Forecasting of energy consumption in this energy intensive building category could help higher education institutions in taking energy saving initiatives and in revising their building operating strategies. Reliable predictive techniques does not only help in forecasting a building’ energy consumption, but also help in identifying a variety of factors affecting the energy consumption of that building. This study attempts to forecast and benchmark the daily electricity consumption of an academic building situated in London, United Kingdom using two different data-driven modeling techniques, i.e., Multiple Regression and Artificial Neural Network. Hourly dataset for the electricity consumption was available for the period 2007 to 2011 from the smart meter whereas hourly data of different factors such as ambient temperature, relative humidity, wind speed and solar radiation were downloaded from the website of environmental research group of Kings College London. The performances of the two predictive models have been critically analyzed by comparing their predicted consumption with a real dataset of the same building for the year 2012. A comparison shows that both Multiple Regression (MR) and Artificial Neural Network (ANN) perform reasonably well with a Mean Absolute Percentage Error (MAPE) of 3.34% and 2.44% for working days and 5.12% and 4.59% for non-working days respectively. ANN performs slightly better than MR. This energy consumption forecasting approach can easily be adapted for predicting energy use of similar buildings.
  Article Metrics

Keywords data-driven forecasting models; electricity consumption; academic buildings; ANN; MR

Citation: Bilal Akbar, Khuram Pervez Amber, Anila Kousar, Muhammad Waqar Aslam, Muhammad Anser Bashir, Muhammad Sajid Khan. Data-driven predictive models for daily electricity consumption of academic buildings. AIMS Energy, 2020, 8(5): 783-801. doi: 10.3934/energy.2020.5.783


  • 1. Gul MS, Patidar S (2015) Understanding the energy consumption and occupancy of a multi-purpose academic building. Energy Build 87: 155-165.    
  • 2. Amber KP, Aslam MW, Mahmood A, et al. (2017) Energy consumption forecasting for university sector buildings. Energies 10: 1579.    
  • 3. Amasyali K, El-Gohary NM (2018) A review of data-driven building energy consumption prediction studies. Renewable Sustainable Energy Rev 81: 1192-1205.    
  • 4. Ghedamsi R, Settou N, Gouareh A, et al. (2016) Modeling and forecasting energy consumption for residential buildings in Algeria using bottom-up approach. Energy Build 121: 309-317.    
  • 5. Kaytez F, Taplamacioglu MC, Cam E, et al. (2015) Forecasting electricity consumption: A comparison of regression analysis, neural networks and least squares support vector machines. Int J Electr Power Energy Syst 67: 431-438.    
  • 6. Li C, Ding Z, Zhao D, et al. (2017) Building energy consumption prediction: An extreme deep learning approach. Energies 10: 1525.    
  • 7. Lü X, Lu T, Kibert CJ, et al. (2015) Modeling and forecasting energy consumption for heterogeneous buildings using a physical-statistical approach. Appl Energy 144: 261-275.    
  • 8. Zeyu W, Ravi SS (2017) A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models. Renewable Sustainable Energy Rev 75: 796-808.    
  • 9. Zhao HX, Magoulès F (2012) A review on the prediction of building energy consumption. Renewable Sustainable Energy Rev 16: 3586-3592.    
  • 10. Tardioli G, Kerrigan R, Oates M, et al. (2015) Data driven approaches for prediction of building energy consumption at urban level. Energy Procedia 78: 3378-3383.    
  • 11. Jin H, Zhang L, Ma HZ, et al. (2017) Machine learning for complex EMI prediction, optimization. 2017 IEEE Electrical Design of Advanced Packaging and Systems Symposium (EDAPS), Haining, 1-3.
  • 12. Yedra RM, Díaz FR, Del-Mar-Castilla-Nieto M, et al. (2014). A neural network model for energy consumption prediction of CIESOL bioclimatic building. In: International Joint Conference SOCO'13-CISIS'13-ICEUTE'13. Advances in Intelligent Systems and Computing, 239. Springer, Cham.
  • 13. Biswas MR, Robinson MD, Fumo N (2016) Prediction of residential building energy consumption: a neural network approach. Energy 117: 84-92.    
  • 14. Ahmad T, Chen H, Guo Y, et al. (2018) A comprehensive overview on the data driven and large scale based approaches for forecasting of building energy demand: A review. Energy Build 165: 301-320.    
  • 15. Amber KP, Aslam MW, Hussain SK (2015) Electricity consumption forecasting models for administration buildings of the UK higher education sector. Energy Build 90: 127-136.    
  • 16. Martellotta F, Ayr U, Stefanizzi P, et al. (2017) On the use of artificial neural networks to model household energy consumptions. Energy Procedia 126: 250-257.    
  • 17. Deb C, Lee SE, Santamouris M (2018) Using artificial neural networks to assess HVAC related energy saving in retrofitted office buildings. Sol Energy 163: 32-44.    
  • 18. Ekici BB, Aksoy UT (2009) Prediction of building energy consumption by using artificial neural networks. Adv Eng Software 40: 356-362.    
  • 19. Deb C, Eang LS, Yang J, et al. (2016) Forecasting diurnal cooling energy load for institutional buildings using Artificial Neural Networks. Energy Build 121: 284-297.    
  • 20. Park SK, Moon HJ, Min KC, et al. (2018) Application of a multiple linear regression and an artificial neural network model for the heating performance analysis and hourly prediction of a large-scale ground source heat pump system. Energy Build 165: 206-215.    
  • 21. Aranda A, Ferreira G, Mainar-Toledo MD, et al. (2012) Multiple regression models to predict the annual energy consumption in the Spanish banking sector. Energy Build 49: 380-387.    
  • 22. Bianco V, Oronzio M, Sergio N (2009) Electricity consumption forecasting in Italy using linear regression models. Energy 34: 1413-1421.    
  • 23. Bingchun L, Chuanchuan F, Arlene B, et al. (2017) Forecasting of chinese primary energy consumption in 2021 with GRU artificial neural network. Energies 10: 1453-1467.    
  • 24. Joseph CL, Kevin KWW, Dalong L, et al. (2010) Multiple regression models for energy use in air-conditioned office buildings in different climates. Energy Convers Manage 51: 2692-2697.    
  • 25. Mohammad M, Atefeh M, Shideh SA, et al. (2015) Multi-linear regression models to predict the annual energy consumption of an office building with different shapes. Procedia Eng 118: 622-629.    
  • 26. Amber KP, Ahmad R, Aslam MW, et al. (2018) Intelligent techniques for forecasting electricity consumption of buildings. Energy 157: 886-893.    
  • 27. Alireza K, Reza AR, Dominic M (1998) ANNSTLF-Artificial neural network short-term load forecaster-generation three. IEEE Trans Power Syst 13: 1413-1422.    
  • 28. Ferlito S, Atrigna M, Graditi G, et al. (2015) Predictive models for building's energy consumption: an Artificial Neural Network (ANN) approach. In XVIII AISEM Annual Conference.
  • 29. Gamze O, Omer FD, Selim Z (2012) Forecasting electricity consumption with neural networks and support vector regression. Procedia-Soc Behav Sci 58: 1576-1585.    
  • 30. Hawkins D, Hong SM, Raslan R, et al. (2012) Determinants of energy use in UK higher education buildings using statistical and artificial neural network methods. Int J Sustainable Built Environ 1: 50-63.    
  • 31. Samani BH, Jafari HH, Zareiforoush H (2017) Artificial neural networks, genetic algorithm and response surface methods: The energy consumption of food and beverage industries in Iran. J AI Data Min 5: 79-88.
  • 32. Erba S, Causone F, Armani R (2017) The effect of weather datasets on building energy simulation outputs. Energy Procedia 134: 545-554.    
  • 33. Fikru MG, Gautier L (2015) The impact of weather variation on energy consumption in residential houses. Appl Energy 144: 19-30.    
  • 34. Kaufmann RK, Gopal S, Tang X, et al. (2013) Revisiting the weather effect on energy consumption: Implications for the impact of climate change. Energy Policy 62: 1377-1384.    
  • 35. Kim YS, Srebric J (2017) Impact of occupancy rates on the building electricity consumption in commercial buildings. Energy Build 138: 591-600.    
  • 36. Ioannidis D, Tropios P, Krinidis S, et al. (2016) Occupancy driven building performance assessment. J Innovation Digital Ecosyst 3: 57-69.    


Reader Comments

your name: *   your email: *  

© 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (

Download full text in PDF

Export Citation

Copyright © AIMS Press All Rights Reserved