تطبيق الذكاء الاصطناعي للتنبؤ بالطاقة المستهلكة في كلية الهندسة الخوارزمي ، بغداد العراق
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كيفية الاقتباس

تطبيق الذكاء الاصطناعي للتنبؤ بالطاقة المستهلكة في كلية الهندسة الخوارزمي ، بغداد العراق. (2024). مجلة الخوارزمي الهندسية, 20(1), 76-88. https://doi.org/10.22153/kej.2024.10.005

تواريخ المنشور

الملخص

على الصعيد العالمي ، تستهلك المباني حوالي 40٪ من الطاقة. تؤثر العديد من العناصر ، مثل الخصائص الفيزيائية للهيكل ، وكفاءة نظام التبريد والتدفئة ، ونشاط شاغلي المبنى ، واستدامة المبنى ، على استهلاك الطاقة للمبنى. في الواقع ، من الصعب حقًا التنبؤ بكمية الطاقة التي يحتاجها المبنى. من أجل تحسين استدامة المبنى وإنشاء مصادر طاقة مستدامة لتقليل غاز ثاني أكسيد الكربون المنبعث من احتراق الوقود الأحفوري ، من الضروري إجراء تقدير للطاقة المستخدمة في المبنى. تتناول هذه الورقة شرحاً للطاقة المستهلكة في مبنى المحاضرات بكلية الهندسة خوارزمي ، جامعة بغداد ، العراق. تم جمع بيانات الطقس لفترة زمنية محددة ومعلومات تشييد المبنى ووضعت في مجموعة بيانات محددة. تم استخدام هذه البيانات لمعرفة قيمة استهلاك الطاقة في المبنى المذكور أعلاه ، باستخدام الذكاء الاصطناعي وتحليل البيانات. تكمن أهمية هذا العمل في تقليل كمية الطاقة المستهلكة. يمكن استخدام نتائج هذا العمل للتنبؤ بالطاقة التي يستهلكها أي مبنى قبل بنائه. توضح المنهجية المستخدمة القدرة على التنبؤ بأداء الطاقة في بناية تعليمية باستخدام النتائج السابقة وتدريب النموذج عليها ، وتعتمد دقة التنبؤ على كمية البيانات المتاحة للتدريب في خطوات الذكاء الاصطناعي لإعطاء أعلى دقة. تم التحقق من التنبؤ باستخدام جذر متوسط الخطأ التربيعي ومعامل التحديد ووصلنا إلى 0.16 و 0.97 لـ متوسط الخطأ التربيعي ومعامل التحديد على التوالي.

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المراجع

D. Larcher, and J. M. Tarascon, "Science for Environment Policy Science for Environment Policy Towards the battery of the future.," science for environment policy, vol. 20, no. 7, pp. 19-29, 2018

Y. Himeur, A. Alsalemi, F. Bensaali and A. Amira, , "Building power consumption datasets: Survey, taxonomy and future directions.," Energy and Buildings, vol. 227, p. 110404, 2020.

X. Liang, T. Hong and . G. Shen, "Improving the accuracy of energy baseline models for commercial buildings with occupancy data.," Applied energy, vol. 179, pp. 247-260, 2016.

J. Chou and D. Bui, "Modeling heating and cooling loads by artificial intelligence for energy-efficient building design.," Energy and Buildings, vol. 82, pp. 437-446, 2014.

E. P. a. Council, "Directive 2010/31/EU of the European Parlia-ment and of the Council of 19 May on the energy performance of buildings(recast).," Official Journal of the European Union L153 (0) , 2010.

A. Kwok and N. Rajkovich, "Addressing climate change in comfort standards.," Building and environment, vol. 45, no. 1, pp. 18-22, 2010.

R. Yao, B. Li and . K. Steemers, "Energy policy and standard for built environment in China.," Renewable Energy, vol. 30, no. 13, pp. 1973-1988, 2005.

M. Leung, C. Norman, . L. Lai and T. Chow, "The use of occupancy space electrical power demand in building cooling load prediction.," Energy and Buildings, vol. 55, pp. 151-163, 2012.

M. Gul and S. Patidar, "Understanding the energy consumption and occupancy of a multi-purpose academic building.," Energy and Buildings, vol. 87, pp. 155-165, 2015.

X. Zhang, K. Grolinger, M. Capretz and L. Seewald , " Forecasting residential energy consumption: Single household perspective.," In 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 110-117, 17 December 2018.

S. Makonin, B. Ellert, I. Bajic and F. Popowich, "Electricity, water, and natural gas consumption of a residential house in Canada from 2012 to 2014.," Sci Data, vol. 3, no. 1, p. 160037, 2016.

T. Walter, P. Price and . M. Sohn, "Uncertainty estimation improves energy measurement and verification procedures.," Applied Energy, vol. 130, pp. 230-236, 2014.

D. Zhu, T. Hong, D. Yan and . C. Wang, "September. A detailed loads comparison of three building energy modeling programs: EnergyPlus, DeST and DOE-2.1 E. In Building Simulation," Springer Berlin Heidelberg, vol. 6, pp. 323-335, 2013.

K. Li, X. Xie, . W. Xue, X. Dai, . X. Chen and X. Yang , "A hybrid teaching-learning artificial neural network for building electrical energy consumption prediction.," Energy and Buildings, vol. 174, pp. 323-334, 2018.

M. Shen, H. Sun and . Y. Lu, "Household electricity consumption prediction under multiple behavioural intervention strategies using support vector regression.," Energy Procedia, vol. 142, pp. 2734-2739, 2017.

R. Jain, . K. Smith, . P. Culligan and J. Taylor, "Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy.," Applied Energy, vol. 123, pp. 168-178, 2014.

A. Rahman, V. Srikumar and . A. Smith, "Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks.," Applied energy, vol. 212, pp. 372-385, 2018.

Z. Wang, X. Liu, H. Shen, Y. Wang and . H. Li, "Energy performance prediction of vapor-injection air source heat pumps in residential buildings using a neural network model.," Energy and Buildings, vol. 228, p. 110499, 2020.

S. E. M. C. S. N. P. K. R. L. B. a. L. C. O. Paudel, " A relevant data selection method for energy consumption prediction of low energy building based on support vector machine.," Energy and Buildings, vol. 138, pp. 240-256, 2017.

T. Kim and S. Cho, " Predicting residential energy consumption using CNN-LSTM neural networks.," Energy, vol. 182, pp. 72-81, 2019.

D. Bui, T. Nguyen, . T. Ngo and H. Nguyen-Xuan, "[24] Bui, D.K., Nguyen, T.N., Ngo, T.D. and Nguyen-Xuan, H., 2020. An artificial neural network (ANN) expert system enhanced with the electromagnetism-based firefly algorithm (EFA) for predicting the energy consumption in buildings.," Energy, vol. 190, p. 116370, 2020.

Z. Guo, H. Moayedi, . L. Foong and . M. Bahiraei, "Optimal modification of heating, ventilation, and air conditioning system performances in residential buildings using the integration of metaheuristic optimization and neural computing.," Energy and Building, vol. 214, p. 109866, 2020.

J. Valbé, M. Martí, . P. Casanovas, A. Jakulin, D. Mladenic and B. fortuna, "Stemming and lemmatisation: improving knowledge management through language processing techniques.," Stemming and Lemmatisation, pp. 1000-1016, 2007.

B. Chegari, . M. Tabaa, E. Simeu, F. Moutaouakkil and H. Medromi, "Multi-objective optimization of building energy performance and indoor thermal comfort by combining artificial neural networks and metaheuristic algorithms.," Energy and Building, vol. 239, p. 110839, 2021.

K. Das and . R. Behera, "A survey on machine learning: concept, algorithms and applications.," International Journal of Innovative Research in Computer and Communication Engineering, vol. 5, no. 2, pp. 1301-1309, 2017.

V. V.K., The Hundred-Page Machine Learning Book., Quebec City, Canada: Andriy Burkov, 2020.

"user guide of scikit learn," [Online]. Available: https://scikit-learn.org/0.21/_downloads/scikit-learn-docs.pdf.

" user guide of tenser flow," [Online]. Available: https://www.tutorialspoint.com/tensorflow/tensorflow_tutorial.pdf.

"user guide of pytorch," [Online]. Available: https://web.cs.ucdavis.edu/~yjlee/teaching/ecs289gwinter2018/Pytorch_Tutorial.pdf .

"user guide of Keras," [Online]. Available: https://www.tutorialspoint.com/keras/keras_tutorial.pdf.

Iraqiclimate," [Online]. Available: https://www.climatestotravel.com/climate/iraq#baghdad.

Y. Geng, W. Ji, B. Lin, J. Hong and Y. Zhu, "Building energy performance diagnosis using energy bills and weather data.," Energy and Buildings, vol. 172, pp. 181-191, 2018.

J. Kočí, V. Kočí, J. Maděra and . R. Černý, "Effect of applied weather data sets in simulation of building energy demands: Comparison of design years with recent weather data.," Renewable and Sustainable Energy Reviews, vol. 100, pp. 22-32, 2019.

" scikit learn documentation," [Online]. Available: 1. Supervised learning — scikit-learn 1.2.2 documentation.

S. Asadi, S. Amiri and M. Mottahedi, "On the development of multi-linear regression analysis to assess energy consumption in the early stages of building design.," Energy and Buildings, vol. 85, pp. 246-255, 2014.

X. Liu, N. Iftikhar, H. Huo, . R. Li and P. Nielsen, "[35] Liu, X., Iftikhar, N., Huo, H., Li, R. and Nielsen, P.S., . Two approaches for synthesizing scalable residential energy consumption data.," Future Generation Computer Systems, vol. 95, pp. 586-600, 2019.

Y. Guo, Z. Tan, H. Chen, G. Li, J. Wang, R. Huang, J. Liu, and T. Ahmad, "Deep learning-based fault diagnosis of variable refrigerant flow air-conditioning system for building energy saving.," Applied Energy, vol. 225, pp. 732-745, 2018.

N. Ngo, " Early predicting cooling loads for energy-efficient design in office buildings by machine learning.," Energy and Buildings, vol. 182, pp. 264-273, 2019.

H. Moraliyage, . N. Mills, P. Rathnayake, D. De Silva and A. Jennings, "UNICON: An Open Dataset of Electricity, Gas and Water Consumption in a Large Multi-Campus University Setting.," in In 2022 15th International Conference on Human System Interaction(HSI), 2022, July..

A. Cameron and F. Windmeijer, "An R-squared measure of goodness of fit for some common nonlinear regression models.," Journal of econometrics, vol. 77, no. 2, pp. 329-342, 1997.

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