Electricity Supply Model of Conventional Residential Buildings in Tehran with Priority on Renewable Energy Using Adaptive Fuzzy-neural Inference System

Document Type : Original Article

Authors

Department of Civil Engineering, Roudehen branch, Islamic Azad University, Tehran, Iran

Abstract

Energy consumption in the building sector, especially in residential buildings, due to the development of urbanization, has taken the largest share among all consumption sectors. Therefore, it is very necessary to predict the energy consumption of buildings, which has been presented as a challenge in recent decades. In this research, adaptive fuzzy-neural inference system (ANFIS) and MATLAB software have been used for forecasting to supply electrical energy to residential buildings whit random data that collected based on the hourly electricity consumption of conventional residential buildings in Tehran. According to the applied settings for the solar and wind energy production has been done by solar panels and wind turbines. The use of renewable energy is one of the ways that can reduce the consumption of fossil fuels and also reduce environmental pollution. Statistical indicators  such az MSE, RMSE, µ, σ, and R were used to evaluate the model performance . The obtained values well show the ability of this model to foresee the generation and utilization of energy in privat reresidential buildings with tall exactness of about 96% and 90%, respectively. Therefore, this model well show the ability of to the needed estimates in the mentioned buildings with high accuracy.

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References

  1. Baheri, A., Najafi, M., Azimi, A. and Aghanajafi, C., "A simplified model to predict and optimize energy consumption of residential buildings in the cold climate regions of iran", Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, (2020), 1-19. https://doi.org/10.1080/15567036.2020.1859648
  2. Ekici, B.B. and Aksoy, U.T., "Prediction of building energy needs in early stage of design by using anfis", Expert Systems with Applications, Vol. 38, No. 5, (2011), 5352-5358. https://doi.org/10.1016/j.eswa.2010.10.021
  3. Deif, A. and Vivek, T., "Understanding ai application dynamics in oil and gas supply chain management and development: A location perspective", HighTech and Innovation Journal, Vol. 3, (2022), 1-14. doi: 10.28991/HIJ-SP2022-03-01.
  4. Alrwashdeh, S.S., Ammari, H., Madanat, M.A. and Al-Falahat, A.a.M., "The effect of heat exchanger design on heat transfer rate and temperature distribution", Emerging Science Journal, Vol. 6, No. 1, (2022), 128-137. http://dx.doi.org/10.28991/ESJ-2022-06-01-010
  5. Tsanas, A. and Xifara, A., "Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools", Energy and Buildings, Vol. 49, (2012), 560-567. https://doi.org/10.1016/j.enbuild.2012.03.003
  6. Platt, G., Li, J., Li, R., Poulton, G., James, G. and Wall, J., "Adaptive hvac zone modeling for sustainable buildings", Energy and Buildings, Vol. 42, No. 4, (2010), 412-421. https://doi.org/10.1016/j.enbuild.2009.10.009
  7. Seyedzadeh, S., Rahimian, F.P., Rastogi, P. and Glesk, I., "Tuning machine learning models for prediction of building energy loads", Sustainable Cities and Society, Vol. 47, (2019), 101484. https://doi.org/10.1016/j.scs.2019.101484
  8. Deb, C., Eang, L.S., Yang, J. and Santamouris, M., "Forecasting diurnal cooling energy load for institutional buildings using artificial neural networks", Energy and Buildings, Vol. 121, (2016), 284-297. https://doi.org/10.1016/j.enbuild.2015.12.050
  9. Yokoyama, R., Wakui, T. and Satake, R., "Prediction of energy demands using neural network with model identification by global optimization", Energy Conversion and Management, Vol. 50, No. 2, (2009), 319-327. https://doi.org/10.1016/j.enconman.2008.09.017
  10. Li, X., Wen, J. and Bai, E.-W., "Developing a whole building cooling energy forecasting model for on-line operation optimization using proactive system identification", Applied Energy, Vol. 164, (2016), 69-88. https://doi.org/10.1016/j.apenergy.2015.12.002
  11. Shaikh, P.H., Nor, N.B.M., Nallagownden, P. and Elamvazuthi, I., "Intelligent multi-objective optimization for building energy and comfort management", Journal of King Saud University-Engineering Sciences, Vol. 30, No. 2, (2018), 195-204. https://doi.org/10.1016/j.jksues.2016.03.001
  12. Delgarm, N., Sajadi, B., Kowsary, F. and Delgarm, S., "Multi-objective optimization of the building energy performance: A simulation-based approach by means of particle swarm optimization (pso)", Applied Energy, Vol. 170, (2016), 293-303. https://doi.org/10.1016/j.apenergy.2016.02.141
  13. Buratti, C., Lascaro, E., Palladino, D. and Vergoni, M., "Building behavior simulation by means of artificial neural network in summer conditions", Sustainability, Vol. 6, No. 8, (2014), 5339-5353. https://doi.org/10.3390/su6085339
  14. Biswas, M.R., Robinson, M.D. and Fumo, N., "Prediction of residential building energy consumption: A neural network approach", Energy, Vol. 117, (2016), 84-92. https://doi.org/10.1016/j.energy.2016.10.066
  15. Amara, F., Agbossou, K., Dubé, Y., Kelouwani, S., Cardenas, A. and Hosseini, S.S., "A residual load modeling approach for household short-term load forecasting application", Energy and Buildings, Vol. 187, (2019), 132-143. https://doi.org/10.1016/j.enbuild.2019.01.009
  16. Chabaud, A., Eynard, J. and Grieu, S., "A new approach to energy resources management in a grid-connected building equipped with energy production and storage systems: A case study in the south of france", Energy and Buildings, Vol. 99, (2015), 9-31. http://dx.doi.org/10.1016/j.enbuild.2015.04.007
  17. Ciancio, V., Falasca, S., Golasi, I., Curci, G., Coppi, M. and Salata, F., "Influence of input climatic data on simulations of annual energy needs of a building: Energyplus and wrf modeling for a case study in rome (italy)", Energies, Vol. 11, No. 10, (2018), 2835. https://doi.org/10.3390/en11102835
  18. Alobaidi, M.H., Chebana, F. and Meguid, M.A., "Robust ensemble learning framework for day-ahead forecasting of household based energy consumption", Applied Energy, Vol. 212, (2018), 997-1012. https://doi.org/10.1016/j. apenergy.2017.12.054
  19. Nilashi, M., Dalvi-Esfahani, M., Ibrahim, O., Bagherifard, K., Mardani, A. and Zakuan, N., "A soft computing method for the prediction of energy performance of residential buildings", Measurement, Vol. 109, (2017), 268-280. https://doi.org/10.1016/j.measurement.2017.05.048
  20. Ullah, I., Ahmad, R. and Kim, D., "A prediction mechanism of energy consumption in residential buildings using hidden markov model", Energies, Vol. 11, No. 2, (2018), 358. https://doi.org/10.3390/en11020358
  21. Mocanu, E., Nguyen, P.H., Kling, W.L. and Gibescu, M., "Unsupervised energy prediction in a smart grid context using reinforcement cross-building transfer learning", Energy and Buildings, Vol. 116,  (2016), 646-655. https://doi.org/10.1016/j.enbuild.2016.01.030
  22. Jang, J., Baek, J. and Leigh, S.-B., "Prediction of optimum heating timing based on artificial neural network by utilizing bems data", Journal of Building Engineering, Vol. 22, (2019), 66-74. https://doi.org/10.1016/j.jobe.2018.11.012
  23. Qiao, Q., Yunusa-Kaltungo, A. and Edwards, R.E., "Towards developing a systematic knowledge trend for building energy consumption prediction", Journal of Building Engineering, Vol. 35, (2021), 101967. https://doi.org/10.1016/j.jobe.2020.101967
  24. Naji, S., Shamshirband, S., Basser, H., Keivani, A., Alengaram, U.J., Jumaat, M.Z. and Petković, D., "Application of adaptive neuro-fuzzy methodology for estimating building energy consumption", Renewable and Sustainable Energy Reviews, Vol. 53, (2016), 1520-1528. https://doi.org/10.1016/j.rser.2015.09.062
  25. Najafi, G., Ghobadian, B., Mamat, R., Yusaf, T. and Azmi, W., "Solar energy in iran: Current state and outlook", Renewable and Sustainable Energy Reviews, Vol. 49, (2015), 931-942. https://doi.org/10.1016/j.rser.2015.04.056
  26. Keyhani, A., Ghasemi-Varnamkhasti, M., Khanali, M. and Abbaszadeh, R., "An assessment of wind energy potential as a power generation source in the capital of iran, tehran", Energy, Vol. 35, No. 1, (2010), 188-201. https://doi.org/10.1016/j.energy.2009.09.009
  27. Fazelpour, F., Soltani, N. and Rosen, M.A., "Economic analysis of standalone hybrid energy systems for application in tehran, iran", International Journal of Hydrogen Energy, Vol. 41, No. 19, (2016), 7732-7743. https://doi.org/10.1016/j.ijhydene.2016.01.113
  28. Tahani, M., Babayan, N. and Pouyaei, A., "Optimization of pv/wind/battery stand-alone system, using hybrid fpa/sa algorithm and cfd simulation, case study: Tehran", Energy Conversion and Management, Vol. 106, (2015), 644-659. https://doi.org/10.1016/j.enconman.2015.10.011
  29. Shivam, K., Tzou, J.-C. and Wu, S.-C., "A multi-objective predictive energy management strategy for residential grid-connected pv-battery hybrid systems based on machine learning technique", Energy Conversion and Management, Vol. 237, (2021), 114103. https://doi.org/10.1016/j.enconman.2021.114103
  30. Taghavifar, H. and Zomorodian, Z.S., "Techno-economic viability of on grid micro-hybrid pv/wind/gen system for an educational building in iran", Renewable and Sustainable Energy Reviews, Vol. 143, (2021), 110877. https://doi.org/10.1016/j.rser.2021.110877
  31. Asrami, R.F., Sohani, A., Saedpanah, E. and Sayyaadi, H., "Towards achieving the best solution to utilize photovoltaic solar panels for residential buildings in urban areas", Sustainable Cities and Society, Vol. 71, (2021), 102968. https://doi.org/10.1016/j.scs.2021.102968
  32. Ehyaei, M. and Bahadori, M., "Selection of micro turbines to meet electrical and thermal energy needs of residential buildings in iran", Energy and Buildings, Vol. 39, No. 12, (2007), 1227-1234. https://doi.org/10.1016/j.enbuild.2007.01.006
International Journal of Engineering
Volume 36, Issue 10
TRANSACTIONS A: Basics
October 2023
Pages 1793-1814

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