Optimization of tertiary building passive parameters by forecasting energy consumption based on artificial intelligence models and using ANOVA variance analysis method

Lamya Lairgi; Rachid Lagtayi; Yassir Lairgi; Abdelmajid Daya; Rabie Elotmani; Ahmed Khouya; Mohammed Touzani; Lamya Lairgi; Rachid Lagtayi; Yassir Lairgi; Abdelmajid Daya; Rabie Elotmani; Ahmed Khouya; Mohammed Touzani

doi:10.3934/energy.2023039

AIMS Energy

2023, Volume 11, Issue 5: 795-809. doi: 10.3934/energy.2023039

Previous Article Next Article

Research article

Optimization of tertiary building passive parameters by forecasting energy consumption based on artificial intelligence models and using ANOVA variance analysis method

1.
Department of Physics, Laboratory M3ER, FSTE, Errachidia, Moulay Ismail University, Meknes, Morocco
2.
Laboratory of Innovative Technologies, National School for Applied Sciences, Abdelmalek Essaadi University, B.P. 1818 Tangier, Morocco
3.
Department of Data Science, MINES School Saint Etienne France
4.
Laboratory of Sciences for Energy LabSIPE, ENSAJ, Chouaib Doukkali University, El Jadida, Morocco

Received: 14 June 2023 Revised: 24 August 2023 Accepted: 10 September 2023 Published: 18 September 2023

Energy consumption in the tertial sector is largely attributed to cooling/heating energy consumption. Thus, forecasting the building's energy consumption has become a key factor in long-term decision-making, reducing the huge energy demand and future planning. This manuscript outlines to use of the variance analysis method (ANOVA) to study the building's passive parameters' effect, such as the orientation, insulation, and its thickness plus the glazing on energy savings through the forecasting of the heating/cooling energy consumption by applying the Seasonal Auto-Regressive Integrated Moving Average (SARIMA) and the Long Short-Term Memory (LSTM) models. The presented methodology compares the predicted consumed energy of a baseline building with another efficient building which includes all the passive parameters selected by the ANOVA approach. The results show that the improvement of passive parameters leads to a reduction of heating energy consumption by 1,739,640 kWh from 2021 to 2029, which is equivalent to a monthly energy consumption of 181.2 kWh for an administrative building with an area of 415 m². While the cooling energy consumption is diminished by 893,246 kWh from 2021 to 2029, which leads to save a monthly value of 93.05 kWh. Consequently, the passive parameters optimization efficiently reduces the consumed energy and minimizes its costs. This positively impacts our environment due to the reduction of gas emissions, air and soil pollution.
- cooling/heating energy consumption,
- passive parameters,
- SARIMA models,
- LSTM models,
- ANOVA
Citation: Lamya Lairgi, Rachid Lagtayi, Yassir Lairgi, Abdelmajid Daya, Rabie Elotmani, Ahmed Khouya, Mohammed Touzani. Optimization of tertiary building passive parameters by forecasting energy consumption based on artificial intelligence models and using ANOVA variance analysis method[J]. AIMS Energy, 2023, 11(5): 795-809. doi: 10.3934/energy.2023039

Related Papers:

Abstract

Energy consumption in the tertial sector is largely attributed to cooling/heating energy consumption. Thus, forecasting the building's energy consumption has become a key factor in long-term decision-making, reducing the huge energy demand and future planning. This manuscript outlines to use of the variance analysis method (ANOVA) to study the building's passive parameters' effect, such as the orientation, insulation, and its thickness plus the glazing on energy savings through the forecasting of the heating/cooling energy consumption by applying the Seasonal Auto-Regressive Integrated Moving Average (SARIMA) and the Long Short-Term Memory (LSTM) models. The presented methodology compares the predicted consumed energy of a baseline building with another efficient building which includes all the passive parameters selected by the ANOVA approach. The results show that the improvement of passive parameters leads to a reduction of heating energy consumption by 1,739,640 kWh from 2021 to 2029, which is equivalent to a monthly energy consumption of 181.2 kWh for an administrative building with an area of 415 m². While the cooling energy consumption is diminished by 893,246 kWh from 2021 to 2029, which leads to save a monthly value of 93.05 kWh. Consequently, the passive parameters optimization efficiently reduces the consumed energy and minimizes its costs. This positively impacts our environment due to the reduction of gas emissions, air and soil pollution.

References

[1]	Regulatory framework. Moroccan Agency For Energy Efficiency (AMEE). Available from: https://www.amee.ma/fr/cadre-reglementaire.
[2]	Lairgi L, Daya A, Elotmani R, et al. (2019) Contribution to the study of the energy needs for a building in the city of errachidia influence of orientation and local materials. Key Eng Mater 820: 18–28. https://doi.org/10.4028/www.scientific.net/KEM.820.18 doi: 10.4028/www.scientific.net/KEM.820.18
[3]	Idchabani R, Khyad A, El Ganaoui M (2017) Optimizing insulation thickness of external walls in cold region of Morocco based on life cycle cost analysis. Energy Procedia 139: 117–121. https://doi.org/10.1016/J.EGYPRO.2017.11.183 doi: 10.1016/j.egypro.2017.11.183
[4]	Louanate A, Otmani RE, Kandoussi K, et al. (2021) Energy saving potential of phase change materials-enhanced building envelope considering the six Moroccan climate zones. J Build Phys 45: 482–506. https://doi.org/10.1177/17442591211006444 doi: 10.1177/17442591211006444
[5]	Khan MY, Baqi A, Talib A (2020) Energy efficiency analysis of a building envelope. In: Bose, M., Modi, A., Proceedings of the 7th International Conference on Advances in Energy Research, Singapore: Springer, 1691–1702. https://doi.org/10.1007/978-981-15-5955-6_160
[6]	Lairgi L, Lagtayi R, Daya A, et al. (2021) The impact of arid climate on the indoor thermal comfort in the south-east of Morocco. Int J Photoenergy 2021. https://doi.org/10.1155/2021/5554629 doi: 10.1155/2021/5554629
[7]	Lagtayi R, Lairgi L, Khouya A, et al. (2021) Acoustic and thermal correlation for a building's envelope in a Mediterranean climate in Morocco'. Int J Renewable Energy Res 11: 1064–1074.
[8]	Ozarisoy B, Altan H (2022) Significance of occupancy patterns and habitual household adaptive behaviour on home-energy performance of post-war social-housing estate in the south-eastern Mediterranean climate: Energy policy design. Energy 244: 122904. https://doi.org/10.1016/j.energy.2021.122904 doi: 10.1016/j.energy.2021.122904
[9]	Dai B, Wang Q, Liu S, et al. (2023) Novel configuration of dual-temperature condensation and dual-temperature evaporation high-temperature heat pump system: Carbon footprint, energy consumption, and financial assessment. Energy Convers Manage 292: 117360. https://doi.org/10.1016/j.enconman.2023.117360 doi: 10.1016/j.enconman.2023.117360
[10]	Miyata S, Lim J, Akashi Y, et al. (2020) Optimal set-point regulation in HVAC system for controllability and energy efficiency. Adv Build Energy Res 14: 160–170. https://doi.org/10.1080/17512549.2019.1578263 doi: 10.1080/17512549.2019.1578263
[11]	Gao G, Li J, Wen Y (2020) DeepComfort: Energy-efficient thermal comfort control in buildings via reinforcement learning. IEEE Internet Things 7: 8472–8484. https://doi.org/10.1109/JIOT.2020.2992117 doi: 10.1109/JIOT.2020.2992117
[12]	Zhang Z, Chong A, Pan Y, et al. (2019) Whole building energy model for HVAC optimal control: A practical framework based on deep reinforcement learning. Energy Build 199: 472–490. https://doi.org/10.1016/j.enbuild.2019.07.029 doi: 10.1016/j.enbuild.2019.07.029
[13]	Jin Y, Yan D, Zhang X, et al. (2021) A data-driven model predictive control for lighting system based on historical occupancy in an office building: Methodology development. Build Simul 14: 219–235. https://doi.org/10.1007/s12273-020-0638-x doi: 10.1007/s12273-020-0638-x
[14]	Awang M, Tham CS, Ruddin NMB, et al. (2020) Assessment of energy saving potential and lighting system in teaching building. J Adv Res Fluid Mech Therm Sci 65: 159–169.
[15]	Yan K, Li W, Ji Z, et al. (2019) A hybrid LSTM neural network for energy consumption forecasting of individual households. IEEE Access 7: 157633–157642. https://doi.org/10.1109/ACCESS.2019.2949065 doi: 10.1109/ACCESS.2019.2949065
[16]	Silva J, Praça I, Pinto T, et al. (2020) Energy consumption forecasting using ensemble learning algorithms. In: Herrera-Viedma, E., Vale, Z., Nielsen, P., Martin Del Rey, A., Casado Vara, R., Distributed Computing and Artificial Intelligence, 16th International Conference, Special Sessions. DCAI 2019. Advances in Intelligent Systems and Computing, Springer: Cham, 5–13. https://doi.org/10.1007/978-3-030-23946-6_1
[17]	Wei N, Li C, Peng X, et al. (2019) Conventional models and artificial intelligence-based models for energy consumption forecasting: A review. J Pet Sci Eng 181: 106187. https://doi.org/10.1016/j.petrol.2019.106187 doi: 10.1016/j.petrol.2019.106187
[18]	Wang R, Lu S, Feng W (2020) A novel improved model for building energy consumption prediction based on model integration. Appl Energy 262: 114561. https://doi.org/10.1016/j.apenergy.2020.114561 doi: 10.1016/j.apenergy.2020.114561
[19]	Daut MAM, Hassan MY, Abdullah H, et al. (2017) Building electrical energy consumption forecasting analysis using conventional and artificial intelligence methods: A review. Renewable Sustainable Energy Rev 70: 1108–1118. https://doi.org/10.1016/j.rser.2016.12.015 doi: 10.1016/j.rser.2016.12.015
[20]	Chou JS, Bui DK (2014) Modeling heating and cooling loads by artificial intelligence for energy-efficient building design. Energy Build 82: 437–446. https://doi.org/10.1016/j.enbuild.2014.07.036 doi: 10.1016/j.enbuild.2014.07.036
[21]	Newsham GR, Birt BJ (2010) Building-level occupancy data to improve ARIMA-based electricity use forecasts. Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building, 13–18. https://doi.org/10.1145/1878431.1878435 doi: 10.1145/1878431.1878435
[22]	Bilgili M, Pinar E (2023) Gross electricity consumption forecasting using LSTM and SARIMA approaches: A case study of Türkiye. Energy 284: 128575. https://doi.org/10.1016/j.energy.2023.128575 doi: 10.1016/j.energy.2023.128575
[23]	Blázquez-García A, Conde A, Milo A, et al. (2020) Short-term office building elevator energy consumption forecast using SARIMA. J Build Perform Simul 13: 69–78. https://doi.org/10.1080/19401493.2019.1698657 doi: 10.1080/19401493.2019.1698657
[24]	Wang X, Meng M (2012) A hybrid neural network and ARIMA model for energy consumption forcasting. J Comput 7: 1184–1190. https://doi.org/10.4304/jcp.7.5.1184-1190 doi: 10.4304/jcp.7.5.1184-1190
[25]	Kandananond K (2011) Forecasting electricity demand in Thailand with an artificial neural network approach. Energies 4: 1246–1257. https://doi.org/10.3390/en4081246 doi: 10.3390/en4081246
[26]	Wang JQ, Du Y, Wang J (2020) LSTM based long-term energy consumption prediction with periodicity. Energy 197: 117197. https://doi.org/10.1016/j.energy.2020.117197 doi: 10.1016/j.energy.2020.117197
[27]	Chujai P, Kerdprasop N, Kerdprasop K (2013) Time series analysis of household electric consumption with ARIMA and ARMA models. Proceedings of the International Multiconference of Engineers and Computer Scientists, Hong Kong: IAENG, 1: 295–300.
[28]	Liu J, Zhao Z, Zhong Y, et al. (2022) Prediction of the dissolved gas concentration in power transformer oil based on SARIMA model. Energy Rep 8: 1360–1367. https://doi.org/10.1016/j.egyr.2022.03.020 doi: 10.1016/j.egyr.2022.03.020
[29]	Camara A, Feixing W, Xiuqin L (2016) Energy consumption forecasting using seasonal ARIMA with artificial neural networks models. Int J Bus Manage 11: 231–243. https://doi.org/10.5539/ijbm.v11n5p231 doi: 10.5539/ijbm.v11n5p231
[30]	Dhakal S, Gautam Y, Bhattarai A (2021) Exploring a deep LSTM neural network to forecast daily PM 2.5 concentration using meteorological parameters in Kathmandu Valley, Nepal. Air Qual Atmos Health 14: 83–96. https://doi.org/10.1007/s11869-020-00915-6 doi: 10.1007/s11869-020-00915-6
[31]	Khan ZA, Hussain T, Ullah A, et al. (2020) Towards efficient electricity forecasting in residential and commercial buildings: A novel hybrid CNN with a LSTM-AE based framework. Sensors 20: 1399. https://doi.org/10.3390/s20051399 doi: 10.3390/s20051399
[32]	Kim EG, Akhtar MS, Yang OB (2023) Designing solar power generation output forecasting methods using time series algorithms. Electr Power Syst Res 216: 109073. https://doi.org/10.1016/j.epsr.2022.109073 doi: 10.1016/j.epsr.2022.109073
[33]	Siami-Namini S, Tavakoli N, Namin AS (2018) A comparison of ARIMA and LSTM in forecasting time series. In 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), IEEE, 1394–1401. https://doi.org/10.1109/ICMLA.2018.00227

Reader Comments

Your name:*

Email:*
© 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)