A Review of Optimization Techniques Application for Building Performance Analysis

سال انتشار: 1401
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 41

فایل این مقاله در 20 صفحه با فرمت PDF قابل دریافت می باشد

استخراج به نرم افزارهای پژوهشی:

لینک ثابت به این مقاله:

شناسه ملی سند علمی:

JR_CEJ-8-4_014

تاریخ نمایه سازی: 1 اردیبهشت 1403

چکیده مقاله:

Optimization techniques (OT) are tools to find the best solution during a decision making process. Each of these techniques has its own advantages and disadvantages depending on their objectives and focus areas. Early application of OT was recorded as early as the ۱۹۳۰s with the introduction of the Monte Carlo Method, which is widely used in business studies. The idea was conceived due to poly-objectives, or multi-objectives, in identifying the best solution. OT theories and methods have evolved to cover various fields of study. This paper aims to provide a brief review of OT through a comparison of the pros and cons of each OT technique. The findings emphasize the suitability of each technique for different applications in various fields of study. Finally, this study aims to select the most suitable OT for building performance for energy optimization. In conclusion, the summary of findings and recommendations from Tables ۲, ۳, and ۴ need to be combined during the process of selecting the most suitable OT. Regardless of the various categories and their multiple applications, it is the summary of characteristics of each optimization technique that determines their suitability for adoption depending on the research objectives, strength of the researcher, and availability of data. ANN is suitable for optimization for building energy performance covering energy use, energy cost and energy prediction which offer a high level of accuracy. However, it is a complex model that requires historical data input and can produce only short-term predictions. For broader optimization objectives which cover energy load, a hybrid of ANN and kNN is recommended. Doi: ۱۰.۲۸۹۹۱/CEJ-۲۰۲۲-۰۸-۰۴-۰۱۴ Full Text: PDF

مراجع و منابع این مقاله:

لیست زیر مراجع و منابع استفاده شده در این مقاله را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود مقاله لینک شده اند :
  • Metropolis, N. (1987). The beginning of the Monte Carlo method. ...
  • Kroese, D. P., Brereton, T., Taimre, T., & Botev, Z. ...
  • Dewispelare, A. R. (1984). Vector Optimization Techniques, Technical rept., Air ...
  • Meketon, M. S. (1987). Optimization in simulation: A survey of ...
  • Fu, M. C., Glover, F. W., & April, J. (2005). ...
  • Hong, L. J., & Nelson, B. L. (2009). A brief ...
  • Ammeri, A., Hachicha, W., Chabchoub, H., & Masmoudi, F. (2011). ...
  • Gallier, J., & Quaintance, J. (2018). Fundamentals of linear algebra ...
  • Di Somma, M., Yan, B., Bianco, N., Luh, P. B., ...
  • Ginchev, I., Guerraggio, A., & Rocca, M. (2006). From scalar ...
  • Jin, M., Zhou, X., Zhang, Z. M., & Tentzeris, M. ...
  • Suganthi, L., & Samuel, A. A. (2012). Energy models for ...
  • Fumo, N. (2014). A review on the basics of building ...
  • Kang, H. S., Kim, H., Lee, J., Lee, I., Kwak, ...
  • Zhou, D., Al-Durra, A., Zhang, K., Ravey, A., & Gao, ...
  • Yu, W., Li, B., Jia, H., Zhang, M., & Wang, ...
  • Zhao, H. X., & Magoulès, F. (2012). A review on ...
  • Pereira, I., & Madureira, A. (2013). Self-Optimization module for Scheduling ...
  • González-Briones, A., Prieto, J., De La Prieta, F., Herrera-Viedma, E., ...
  • Faia, R., Pinto, T., Abrishambaf, O., Fernandes, F., Vale, Z., ...
  • Jivani, A. G., Shah, K., Koul, S., & Naik, V. ...
  • Wang, Q., Li, S., & Li, R. (2018). Forecasting energy ...
  • Zou, Y., Zhan, Q., & Xiang, K. (2021). A comprehensive ...
  • Rabani, M., Bayera Madessa, H., & Nord, N. (2021). Achieving ...
  • Liu, J., Chen, X., Yang, H., & Li, Y. (2020). ...
  • Williams, K. T., & Gomez, J. D. (2016). Predicting future ...
  • White, J. A., & Reichmuth, R. (1996). Simplified method for ...
  • Westphal, F. S., & Lamberts, R. (2004). The use of ...
  • Albatayneh, A. (2021). Optimization of building envelope parameters in a ...
  • Ng, L. C., Persily, A. K., & Emmerich, S. J. ...
  • Raji, B., Tenpierik, M. J., & Van Den Dobbelsteen, A. ...
  • Cao, X., Liu, J., Cao, X., Li, Q., Hu, E., ...
  • Naylor, S., Gillott, M., & Lau, T. (2018). A review ...
  • Zahiri, S., & Elsharkawy, H. (2018). Towards energy-efficient retrofit of ...
  • Balvedi, B. F., Ghisi, E., & Lamberts, R. (2018). A ...
  • Wang, Y., & Shao, L. (2017). Understanding occupancy pattern and ...
  • Yang, J., Santamouris, M., Lee, S. E., & Deb, C. ...
  • Deb, C., Zhang, F., Yang, J., Lee, S. E., & ...
  • Lee, S. H., Hong, T., Piette, M. A., & Taylor-Lange, ...
  • Peng, Z., Deng, W., & Hong, Y. (2019). Materials consumption, ...
  • Zhou, Y., Zheng, S., Liu, Z., Wen, T., Ding, Z., ...
  • Petri, I., Li, H., Rezgui, Y., Yang, C., Yuce, B., ...
  • Yang, C., Li, H., Rezgui, Y., Petri, I., Yuce, B., ...
  • Beccali, M., Ciulla, G., Lo Brano, V., Galatioto, A., & ...
  • Sghiouri, H., Charai, M., & Mezrhab, A. (2020). Optimization in ...
  • Azari, R., Garshasbi, S., Amini, P., Rashed-Ali, H., & Mohammadi, ...
  • Bamdad Masouleh, K. (2018). Building energy optimization using machine learning ...
  • McCulloch, W. S., & Pitts, W. (1943). A logical calculus ...
  • Paradarami, T. K., Bastian, N. D., & Wightman, J. L. ...
  • Jaddi, N. S., Abdullah, S., & Hamdan, A. R. (2015). ...
  • Schenker, B. G. E. (1996). Prediction and control using feedback ...
  • Karatasou, S., Santamouris, M., & Geros, V. (2006). Modeling and ...
  • Nasr, G. E., Badr, E. A., & Younes, M. R. ...
  • Javeed Nizami, S., & Al-Garni, A. Z. (1995). Forecasting electric ...
  • Kaytez, F., Taplamacioglu, M. C., Cam, E., & Hardalac, F. ...
  • Panapakidis, I. P., & Dagoumas, A. S. (2016). Day-ahead electricity ...
  • Dyvia, H. A., & Arif, C. (2021). Analysis of thermal ...
  • Platon, R., Dehkordi, V. R., & Martel, J. (2015). Hourly ...
  • Çunkaş, M., & Altun, A. A. (2010). Long term electricity ...
  • Azadeh, A., Ghaderi, S. F., & Sohrabkhani, S. (2008). Annual ...
  • Ekonomou, L. (2010). Greek long-term energy consumption prediction using artificial ...
  • Rahman, M. M., Shakeri, M., Tiong, S. K., Khatun, F., ...
  • Neto, A. H., & Fiorelli, F. A. S. (2008). Comparison ...
  • Li, A., Xiao, F., Zhang, C., & Fan, C. (2021). ...
  • García-Gonzalo, E., Fernández-Muñiz, Z., Nieto, P. J. G., Sánchez, A. ...
  • Vapnik, V. (2000). Statistics for engineering and information science. The ...
  • Zhang, F., Deb, C., Lee, S. E., Yang, J., & ...
  • Li, M., Wang, W., De, G., Ji, X., & Tan, ...
  • Zhou, D., Al-Durra, A., Zhang, K., Ravey, A., & Gao, ...
  • Ju-Long, D. (1982). Control problems of grey systems. Systems and ...
  • Liu, S., & Lin, Y. (2010). Introduction to Grey Systems ...
  • Hu, Y. C. (2017). A genetic-algorithm-based remnant grey prediction model ...
  • Tsai, S. B., Xue, Y., Zhang, J., Chen, Q., Liu, ...
  • Pao, H. T., Fu, H. C., & Tseng, C. L. ...
  • Wang, J., & Hu, J. (2015). A robust combination approach ...
  • Ozturk, S., & Ozturk, F. (2018). Forecasting Energy Consumption of ...
  • Zhuang, J., Chen, Y., Shi, X., & Wei, D. (2015). ...
  • Yasmeen, F., & Sharif, M. (2014). Forecasting Electricity Consumption for ...
  • Wang, Y., Wang, J., Zhao, G., & Dong, Y. (2012). ...
  • Iredi, S., Merkle, D., & Middendorf, M. (2001). Bi-criterion optimization ...
  • Dorigo, M., & Blum, C. (2005). Ant colony optimization theory: ...
  • Bamdad, K., Cholette, M. E., Guan, L., & Bell, J. ...
  • Yuan, Y., Yuan, J., Du, H., & Li, L. (2012). ...
  • Sun, Y., Dong, W., & Chen, Y. (2017). An Improved ...
  • Blas, N. G., de Mingo López, L. F., Aslanyan, L., ...
  • Aamodt, A. & E. Plaza (1994). Foundational issues, methodological variations, ...
  • Monfet, D., Corsi, M., Choinière, D., & Arkhipova, E. (2014). ...
  • Cover, T. M., & Hart, P. E. (1967). Nearest Neighbor ...
  • Bhavsar, H., Jivani, A., Bhatt, T., Patel, N., & Shiledarbaxi, ...
  • Kristianto, A. W., Wijaya, F. D., & Tumiran. (2018). Fault ...
  • Haerani, E., Apriyanti, L., & Wardhani, L. K. (2016). Application ...
  • Chantakamo, A., & Ketcham, M. (2015). The multi vehicle recognition ...
  • Diez, Y., Fort, M., & Sellarès, J. A. (2009). Solving ...
  • Lachut, D., Banerjee, N., & Rollins, S. (2015). Predictability of ...
  • Reynolds, J., Rezgui, Y., Kwan, A., & Piriou, S. (2018). ...
  • Jung, H. C., Kim, J. S., & Heo, H. (2015). ...
  • Carlucci, S., Cattarin, G., Causone, F., & Pagliano, L. (2015). ...
  • Eniola, V., Suriwong, T., Sirisamphanwong, C., Ungchittrakool, K., & Fasipe, ...
  • نمایش کامل مراجع