Examining the Application of Artificial Neural Networks (ANNs) for Advancing Energy Efficiency in Building: A Comprehensive Reviews-Journal of Sustainability Research-CSCIED科技核心评价数据库-手机版

Examining the Application of Artificial Neural Networks (ANNs) for Advancing Energy Efficiency in Building: A Comprehensive Reviews

Examining the Application of Artificial Neural Networks (ANNs) for Advancing Energy Efficiency in Building: A Comprehensive Reviews

ES评分 0 浏览量：305 下载量：0

全文阅读下载引用收藏

DOI	10.20900/jsr20240001
刊名	Journal of Sustainability Research JSR
年，卷(期)	2024, 6(1)
作者	Meryem El Alaoui*,Mohamed Rougui,
作者单位	LGCE, Civil Engineering and Environment Laboratory, High school of technology (EST)-Sale, Mohammed V University, PO. Box 227, Rabat Sale 11000, Morocco
Abstract	In light of contemporary challenges in the realm of energy, the management of energy has emerged as a critical concern, particularly within the construction sector, an eminent contributor to global energy consumption. With the growing urgency to optimize energy utilization, the ability to forecast energy consumption patterns takes on paramount importance in devising sustainable energy management protocols. Consequently, the present paper seeks to present a comprehensive review concerning the application of artificial neural networks (ANNs) in the prediction of building energy consumption. The subject of the review is explored in depth, with a thorough analysis conducted on how various ANN architectures are commonly used across different situations. Furthermore, the examination expands to include a variety of training algorithms that are employed, shedding light on how each of them contributes to making predictions more accurate. Additionally, the study evaluates the different training functions that are used within ANN models, highlighting their role in improving the accuracy of predictions. By bringing together these different aspects, this paper aims to provide insights that help us understand how ANNs are used to predict building energy usage, showing us how they have evolved, the trends they follow, and the challenges they face. This effort ultimately leads to a better grasp of the important role ANNs play in shaping effective energy management approaches.
KeyWord	sustainability; energy management; energy prediction; artificial neural networks
基金项目
页码	-

参考文献
相关文献

1. Park YS, Lek S. Chapter 7—Artificial Neural Networks: Multilayer Perceptron for Ecological Modeling. In: Jørgensen SE, editor. Developments in Environmental Modelling. Amsterdam (Netherlands): Elsevier; 2016. p. 123‑40. 2. Pérez-Ortiz JA, Gers FA, Eck D, Schmidhuber J. Kalman filters improve LSTM network performance in problems unsolvable by traditional recurrent nets. Neural Netw. 2003;16(2):241-50. 3. Gers FA, Schmidhuber J. Recurrent nets that time and count. Available from: https://ieeexplore.ieee.org/abstract/document/861302. Accessed 2024 Jan 16. 4. Harpham C, Dawson CW. The effect of different basis functions on a radial basis function network for time series prediction: A comparative study. Neurocomputing. 2006;69(16–18):2161-70. 5. Leung H, Lo T, Wang S. Prediction of noisy chaotic time series using an optimal radial basis function neural network. Available from: https://ieeexplore.ieee.org/abstract/document/950144. Accessed 2024 Jan 16. 6. Li Q, Meng Q, Cai J, Yoshino H, Mochida A. Predicting hourly cooling load in the building: A comparison of support vector machine and different artificial neural networks. Energy Convers Manag. 2009;50(1):90-6. 7. Sholahudin, Alam AG, Baek CI, Han H. Prediction and analysis of building energy efficiency using artificial neural network and design of experiments. Appl Mech Mater. 2016;819:541-5. 8. Karatasou S, Santamouris M, Geros V. Modeling and predicting building’s energy use with artificial neural networks: Methods and results. Energy Build. 2006;38(8):949-58. 9. Ferlito S, Atrigna M, Graditi G, De Vito S, Salvato M, Buonanno A, et al. Predictive models for building’s energy consumption: An Artificial Neural Network (ANN) approach. Available from: https://ieeexplore.ieee.org/abstract/document/7066836. Accessed 2024 Jan 16. 10. Ascione F, Bianco N, De Stasio C, Mauro GM, Vanoli GP. Artificial neural networks to predict energy performance and retrofit scenarios for any member of a building category: A novel approach. Energy. 2017;118:999-1017. 11. Benedetti M, Cesarotti V, Introna V, Serranti S. Energy consumption control automation using Artificial Neural Networks and adaptive algorithms: Proposal of a new methodology and case study. Appl Energy. 2016;165:60-71. 12. McCulloch WS, Pitts W. A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys. 1943;5:115-33. 13. Rosenblatt F. The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev. 1958;65(6):386. 14. Mena R, Rodríguez F, Castilla M, Arahal MR. A prediction model based on neural networks for the energy consumption of a bioclimatic building. Energy Build. 2014;82:142-55. 15. Mihalakakou G, Santamouris M, Tsangrassoulis A. On the energy consumption in residential buildings. Energy Build. 2002;34(7):727-36. 16. Gonzalez PA, Zamarreno JM. Prediction of hourly energy consumption in buildings based on a feedback artificial neural network. Energy Build. 2005;37(6):595-601. 17. Li K, Hu C, Liu G, Xue W. Building's electricity consumption prediction using optimized artificial neural networks and principal component analysis. Energy Build. 2015;108:106-13. 18. Platon R, Dehkordi VR, Martel J. Hourly prediction of a building's electricity consumption using case-based reasoning, artificial neural networks and principal component analysis. Energy Build. 2015;92:10-8. 19. Hong SM, Paterson G, Mumovic D, Steadman P. Improved benchmarking comparability for energy consumption in schools. Build Res Inf. 2014;42(1):47-61. 20. Wong SL, Wan KKW, Lam TNT. Artificial neural networks for energy analysis of office buildings with daylighting. Appl Energy. 2010;87(2):551-7. 21. Lundin M, Andersson S, Östin R. Development and validation of a method aimed at estimating building performance parameters. Energy Build. 2004;36(9):905-14. 22. Khayatian F, Sarto L. Application of neural networks for evaluating energy performance certificates of residential buildings. Energy Build. 2016;125:45-54. 23. Kalogirou SA, Florides GA, Neocleous C, Schizas CN. Estimation of the daily heating and cooling loads using artificial neural networks. Available from: https://ktisis.cut.ac.cy/handle/20.500.14279/2523. Accessed 2024 Jan 16. 24. Dombaycı ÖA. The prediction of heating energy consumption in a model house by using artificial neural networks in Denizli–Turkey. Adv Eng Softw. 2010;41(2):141-7. 25. Kialashaki A, Reisel JR. Modeling of the energy demand of the residential sector in the United States using regression models and artificial neural networks. Appl Energy. 2013;108:271-80. 26. Antanasijević D, Pocajt V, Ristić M, Perić-Grujić A. Modeling of energy consumption and related GHG (greenhouse gas) intensity and emissions in Europe using general regression neural networks. Energy. 2015;84:816-24. 27. Neto AH, Fiorelli FAS. Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption. Energy Build. 2008;40(12):2169-76. 28. Popescu D, Ungureanu F, Hernández-Guerrero A. Simulation models for the analysis of space heat consumption of buildings. Energy. 2009;34(10):1447-53. 29. Deb C, Eang LS, Yang J, Santamouris M. Forecasting diurnal cooling energy load for institutional buildings using Artificial Neural Networks. Energy Build. 2016;121:284-97. 30. Olofsson T, Andersson S. Long-term energy demand predictions based on short-term measured data. Energy Build. 2001;33(2):85-91. 31. Ekici BB, Aksoy UT. Prediction of building energy consumption by using artificial neural networks. Adv Eng Softw. 2009;40(5):356-62. 32. Paudel S, Elmtiri M, Kling WL, Le Corre O, Lacarrière B. Pseudo dynamic transitional modeling of building heating energy demand using artificial neural network. Energy Build. 2014;70:81-93. 33. Ben-Nakhi AE, Mahmoud MA. Cooling load prediction for buildings using general regression neural networks. Energy Convers Manag. 2004;45(13–14):2127-41. 34. Hou Z, Lian Z, Yao Y, Yuan X. Cooling-load prediction by the combination of rough set theory and an artificial neural-network based on data-fusion technique. Appl Energy. 2006;83(9):1033-46. 35. Cheng-wen Y, Jian Y. Application of ANN for the prediction of building energy consumption at different climate zones with HDD and CDD. Available from: https://ieeexplore.ieee.org/abstract/document/5497626. Accessed 2024 Jan 16. 36. Biswas MAR, Robinson MD, Fumo N. Prediction of residential building energy consumption: A neural network approach. Energy. 2016;117:84-92. 37. Aydinalp M, Ugursal VI, Fung AS. Modeling of the appliance, lighting, and space-cooling energy consumptions in the residential sector using neural networks. Appl Energy. 2002;71(2):87-110. 38. Aydinalp M, Ugursal VI, Fung AS. Modeling of the space and domestic hot-water heating energy-consumption in the residential sector using neural networks. Appl Energy. 2004;79(2):159-78. 39. Azadeh MA, Sohrabkhani S. Annual Electricity Consumption Forecasting with Neural Network in High Energy Consuming Industrial Sectors of Iran. Available from: https://ieeexplore.ieee.org/abstract/document/4237894. Accessed 2024 Jan 16. 40. Azadeh A, Ghaderi SF, Sohrabkhani S. Annual electricity consumption forecasting by neural network in high energy consuming industrial sectors. Energy Convers Manag. 2008;49(8):2272-8. 41. Kialashaki A, Reisel JR. Development and validation of artificial neural network models of the energy demand in the industrial sector of the United States. Energy. 2014;76:749-60. 42. El Alaoui M, Rougui M, Lamrani A, Mouhat M. Building energy prediction using artificial neural networks and analysis of covariance in the six thermal zones of Morocco. Available from: https://www.sciencedirect.com/science/article/pii/S2214785323036222. Accessed 2024 Jan 16. 43. El Alaoui M, Chahidi LO, Rougui M, Lemrani A, Mechaqrane A. Prediction of Energy Consumption of an Administrative Building using Machine Learning and Statistical Methods. Civ Eng J. 2023;9(5):1007-22. 44. Zhang H, Feng H, Hewage K, Arashpour M. Artificial neural network for predicting building energy performance: a surrogate energy retrofits decision support framework. Buildings. 2022;12(6):829. 45. Irfan M, Faizir R, Widianto W, Faruq A. Prediction of Residential Building Energy Efficiency Performance using Deep Neural Network. Available from: https://www.iaeng.org/IJCS/issues_v48/issue_3/IJCS_48_3_31.pdf. Accessed 2024 Jan 16. 46. Amasyali K, El-Gohary N. Machine learning for occupant-behavior-sensitive cooling energy consumption prediction in office buildings. Renew Sustain Energy Rev. 2021;142:110714. 47. Barzola-Monteses J, Espinoza-Andaluz M, Mite-León M, Flores-Morán M. Energy Consumption of a Building by using Long Short-Term Memory Network: A Forecasting Study. Available from: https://ieeexplore.ieee.org/abstract/document/9281234. Accessed 2024 Jan 16. 48. Yang S, Wan MP, Chen W, Ng BF, Dubey S. Model predictive control with adaptive machine-learning-based model for building energy efficiency and comfort optimization. Appl Energy. 2020;271:115147. 49. Waseem M, Lin Z, Yang L. Data-Driven Load Forecasting of Air Conditioners for Demand Response Using Levenberg-Marquardt Algorithm-Based ANN. Big Data Cogn Comput. 2019;3(3):36.

引用本文

Meryem El Alaoui*,Mohamed Rougui. Examining the Application of Artificial Neural Networks (ANNs) for Advancing Energy Efficiency in Building: A Comprehensive Reviews, Journal of Sustainability Research. 2024; 6; (1). https://doi.org/10.20900/jsr20240001.

文献评论

相关学者

相关机构