Non-Linear and Linear Baseline Energy Modelling Comparative Studies in Educational Buildings

  • Authors

    • Rijalul Fahmi Mustapa
    • NY Dahlan
    • Ihsan Mohd Yassin
    • Atiqah Hamizah Mohd Nordin4, Mohd Ezwan Mahadan
    • Norlee Husnafeeza Ahmad
    • Norhalida Othman
    2018-11-30
    https://doi.org/10.14419/ijet.v7i4.22.22189
  • baseline energy, energy consumption, predict, linear regression, NARX-ANN,
  • Baseline energy model is a powerful tool in describing energy consumption for any buildings at a certain period of time. Baseline energy model main task is to predict the energy consumption if energy conservation measures were implemented for energy saving calculation purposes. A simple and practical approach to model a baseline energy utilizing linear regression method is undeniable to be inferior. Linear regression work best with energy consumption that behaves linearly but a practical and simple approach to model a baseline energy for energy consumption that behaves in a non-linear manner have to be establish. Thus, this paper main intention is to model a baseline energy using Non-Linear Auto Regressive with Exogenous Input Model with Artificial Neural Network (NARX-ANN) method as the model estimator. Two buildings in a University compound will be used to model the baseline energy consumption. Multiple regression model will be used in order to compare the results of the baseline energy models developed with NARX-ANN method. It is found that NARX-ANN performs better in terms of error measurement. It is hoped that a better baseline energy models can be developed in order to provide less error if energy saving calculation purpose will be implemented.

     

  • References

    1. [1] F. M. A. Rahman, N. Y. Dahlan, and N. S. Razali, "Modelling Adjusted Baseline Energy In an Office Building using Artificial Neural Network," Applied Mechanics and Materials, 2015.

      [2] D. Urge-Vorsatz, "Best Practice Policies For Low Carbon and Energy Buildings Based On Scenario Analysis " Central for Climate Change and Sustainable Energy Policy 2012.

      [3] S. M. Zaid, N. E. Myeda, N. Mahyudding, and R. Sulaiman, "Lack of Energy Efficiency Legislation in the Malaysian Building Sector Contributes to Malaysia's Growing GHG Emissions," 2014.

      [4] A. S. Ahmad, M. Y. Hassan, H. Abdullah, H. A. Rahman, M. S. Majid, and M. Bandi, "Energy Efficiency Measurements in a Malaysian Public University," in IEEE International Conference on Power Energy (PEcon), Kota Kinabalu Sabah, Malaysia, 2012.

      [5] Efficiency, Valuation, and Organization, International Performance Measurement and Verification Protocol: Core Concepts, June 2014.

      [6] M. John, "What is a Baseline ? And Why it is Critical to Good Energy Management," in EnerNOC Blog, ed, 2015.

      [7] I. M. Yassin, M. N. Taib, M. Z. A. Aziz, N. A. Rahim, N. M. Tahir, and A. Johari, "Identification of DC Motor Drive System Model using Radial Basis Function(RBF) Neural Network," in IEEE Symposium on Industrial Electronics and Applications, Langkawi Malaysia, 2011.

      [8] H. R. M. L. Pombeiro and C. A. S. Silva, "Linear, fuzzy and neural networks models for definition of baseline consumption: Early findings from two test beds in a University campus in Portugal," in 2014 Science and Information Conference, 2014, pp. 481-487.

      [9] M. Jafary, M. Wright, L. Shephard, J. Gomez, and R. U. Nair, "Understanding Campus Energy Consumption -- People, Buildings and Technology," in 2016 IEEE Green Technologies Conference (GreenTech), 2016, pp. 68-72.

      [10] S. M. Awan, Z. A. Khan, M. Aslam, W. Mahmood, and A. Ahsan, "Application of NARX based FFNN, SVR and ANN Fitting models for long term industrial load forecasting and their comparison," in 2012 IEEE International Symposium on Industrial Electronics, 2012, pp. 803-807.

      [11] J. Varanasi and M. M. Tripathi, "Artificial Neural Network based wind speed & power forecasting in US wind energy farms," in 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), 2016, pp. 1-6.

      [12] R. Mena, F. Rodríguez, M. Castilla, and M. R. Arahal, "A prediction model based on neural networks for the energy consumption of a bioclimatic building," Energy and Buildings, vol. 82, pp. 142-155, 2014/10/01/ 2014.

      [13] R. F. Mustapa, N. Y. Dahlan, I. M. Yassin, A. H. M. Nordin, and M. E. Mahadan, "Baseline energy modelling in an educational building campus for measurement and verification," in 2017 International Conference on Electrical, Electronics and System Engineering (ICEESE), 2017, pp. 67-72.

      [14] N. Tehlah, P. Kaewpradit, and I. M. Mujtaba, "Artificial neural network based modeling and optimization of refined palm oil process," Neurocomputing, vol. 216, pp. 489-501, 12/5/ 2016.

      [15] W. Yaïci, E. Entchev, M. Longo, M. Brenna, and F. Foiadelli, "Artificial neural network modelling for performance prediction of solar energy system," in 2015 International Conference on Renewable Energy Research and Applications (ICRERA), 2015, pp. 1147-1151.

      [16] M. T. Hagan and M. B. Menhaj, "Training feedforward networks with the Marquardt algorithm," IEEE Transactions on Neural Networks, vol. 5, pp. 989-993, 1994.

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  • How to Cite

    Fahmi Mustapa, R., Dahlan, N., Mohd Yassin, I., Hamizah Mohd Nordin4, Mohd Ezwan Mahadan, A., Husnafeeza Ahmad, N., & Othman, N. (2018). Non-Linear and Linear Baseline Energy Modelling Comparative Studies in Educational Buildings. International Journal of Engineering & Technology, 7(4.22), 61-66. https://doi.org/10.14419/ijet.v7i4.22.22189