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  2. Vol 8, No 4 (2022)
  3. Abd Rahman

A Review of Optimization Techniques Application for Building Performance Analysis

N. A. Abd Rahman, S. N. Kamaruzzaman, F. W. Akashah

Abstract


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 1930s 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 2, 3, and 4 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: 10.28991/CEJ-2022-08-04-014

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Keywords


Building Application; Energy Performance; Comparative Analysis; Decision Making; Energy Efficiency; Normalisation Algorithm; Optimization Techniques.

References


Metropolis, N. (1987). The beginning of the Monte Carlo method. Los Alamos Science Special Issue, New Mexico, United States.

Kroese, D. P., Brereton, T., Taimre, T., & Botev, Z. I. (2014). Why the Monte Carlo method is so important today. Wiley Interdisciplinary Reviews: Computational Statistics, 6(6), 386–392. doi:10.1002/wics.1314.

Dewispelare, A. R. (1984). Vector Optimization Techniques, Technical rept., Air Force Institute of Technology, Wright-Patterson Air Force Base, Ohio, United States.

Meketon, M. S. (1987). Optimization in simulation: A survey of recent results. Proceedings of the 19th conference on Winter simulation, 58-67. doi:10.1145/318371.318384.

Fu, M. C., Glover, F. W., & April, J. (2005). Simulation optimization: a review, new developments, and applications. Proceedings of the Winter Simulation Conference, December 2005, Orlando, United States. doi:10.1109/WSC.2005.1574242.

Hong, L. J., & Nelson, B. L. (2009). A brief introduction to optimization via simulation. Proceedings - Winter Simulation Conference, 75–85. doi:10.1109/WSC.2009.5429321.

Ammeri, A., Hachicha, W., Chabchoub, H., & Masmoudi, F. (2011). A comprehensive literature review of mono-objective Simulation Optimization method. Advances in Production Engineering & Management, 6(4), 291–302.

Gallier, J., & Quaintance, J. (2018). Fundamentals of linear algebra and optimization. Dept. of Computer and Information Science, University of Pennsylvania. Philadelphia, PA, United States. Available online: https://www.seas.upenn.edu/~cis515/linalg-II.pdf (accessed on February 2022).

Di Somma, M., Yan, B., Bianco, N., Luh, P. B., Graditi, G., Mongibello, L., & Naso, V. (2016). Multi-objective operation optimization of a Distributed Energy System for a large-scale utility customer. Applied Thermal Engineering, 101, 752–761. doi:10.1016/j.applthermaleng.2016.02.027.

Ginchev, I., Guerraggio, A., & Rocca, M. (2006). From scalar to vector optimization. Applications of Mathematics, 51(1), 5–36. doi:10.1007/s10492-006-0002-1.

Jin, M., Zhou, X., Zhang, Z. M., & Tentzeris, M. M. (2012). Short-term power load forecasting using grey correlation contest modeling. Expert Systems with Applications, 39(1), 773–779. doi:10.1016/j.eswa.2011.07.072.

Suganthi, L., & Samuel, A. A. (2012). Energy models for demand forecasting - A review. Renewable and Sustainable Energy Reviews, 16(2), 1223–1240. doi:10.1016/j.rser.2011.08.014.

Fumo, N. (2014). A review on the basics of building energy estimation. Renewable and Sustainable Energy Reviews, 31, 53–60. doi:10.1016/j.rser.2013.11.040.

Kang, H. S., Kim, H., Lee, J., Lee, I., Kwak, B. Y., & Im, H. (2015). Optimization of pumping schedule based on water demand forecasting using a combined model of autoregressive integrated moving average and exponential smoothing. Water Science and Technology: Water Supply, 15(1), 188–195. doi:10.2166/ws.2014.104.

Zhou, D., Al-Durra, A., Zhang, K., Ravey, A., & Gao, F. (2019). A robust prognostic indicator for renewable energy technologies: A novel error correction grey prediction model. IEEE Transactions on Industrial Electronics, 66(12), 9312–9325. doi:10.1109/TIE.2019.2893867.

Yu, W., Li, B., Jia, H., Zhang, M., & Wang, D. (2015). Application of multi-objective genetic algorithm to optimize energy efficiency and thermal comfort in building design. Energy and Buildings, 88, 135–143. doi:10.1016/j.enbuild.2014.11.063.

Zhao, H. X., & Magoulès, F. (2012). A review on the prediction of building energy consumption. Renewable and Sustainable Energy Reviews, 16(6), 3586-3592. doi:10.1016/j.rser.2012.02.049.

Pereira, I., & Madureira, A. (2013). Self-Optimization module for Scheduling using Case-based Reasoning. Applied Soft Computing Journal, 13(3), 1419–1432. doi:10.1016/j.asoc.2012.02.009.

González-Briones, A., Prieto, J., De La Prieta, F., Herrera-Viedma, E., & Corchado, J. M. (2018). Energy optimization using a case-based reasoning strategy. Sensors, 18(3), 865. doi:10.3390/s18030865.

Faia, R., Pinto, T., Abrishambaf, O., Fernandes, F., Vale, Z., & Corchado, J. M. (2017). Case based reasoning with expert system and swarm intelligence to determine energy reduction in buildings energy management. Energy and Buildings, 155, 269–281. doi:10.1016/j.enbuild.2017.09.020.

Jivani, A. G., Shah, K., Koul, S., & Naik, V. (2016). The Adept K-Nearest Neighbour Algorithm - An optimization to the Conventional K-Nearest Neighbour Algorithm (M. Learn, trans.). Transactions on Machine Learning and Artificial Intelligence, 4(1), 52. doi:10.14738/tmlai.41.1876.

Wang, Q., Li, S., & Li, R. (2018). Forecasting energy demand in China and India: Using single-linear, hybrid-linear, and non-linear time series forecast techniques. Energy, 161, 821–831. doi:10.1016/j.energy.2018.07.168.

Zou, Y., Zhan, Q., & Xiang, K. (2021). A comprehensive method for optimizing the design of a regular architectural space to improve building performance. Energy Reports, 7, 981–996. doi:10.1016/j.egyr.2021.01.097.

Rabani, M., Bayera Madessa, H., & Nord, N. (2021). Achieving zero-energy building performance with thermal and visual comfort enhancement through optimization of fenestration, envelope, shading device, and energy supply system. Sustainable Energy Technologies and Assessments, 44, 101020. doi:10.1016/j.seta.2021.101020.

Liu, J., Chen, X., Yang, H., & Li, Y. (2020). Energy storage and management system design optimization for a photovoltaic integrated low-energy building. Energy, 190. doi:10.1016/j.energy.2019.116424.

Williams, K. T., & Gomez, J. D. (2016). Predicting future monthly residential energy consumption using building characteristics and climate data: A statistical learning approach. Energy and Buildings, 128, 1–11. doi:10.1016/j.enbuild.2016.06.076.

White, J. A., & Reichmuth, R. (1996). Simplified method for predicting building energy consumption using average monthly temperatures. IECEC 96. Proceedings of the 31st Intersociety Energy Conversion Engineering Conference, 3, 1834-1839. IEEE. doi:10.1109/iecec.1996.553381.

Westphal, F. S., & Lamberts, R. (2004). The use of simplified weather data to estimate thermal loads of non-residential buildings. Energy and Buildings, 36(8), 847–854. doi:10.1016/j.enbuild.2004.01.007.

Albatayneh, A. (2021). Optimization of building envelope parameters in a semi-arid and warm Mediterranean climate zone. Energy Reports, 7, 2081-2093. doi:10.1016/j.egyr.2021.04.011.

Ng, L. C., Persily, A. K., & Emmerich, S. J. (2015). IAQ and energy impacts of ventilation strategies and building envelope airtightness in a big box retail building. Building and Environment, 92, 627–634. doi:10.1016/j.buildenv.2015.05.038.

Raji, B., Tenpierik, M. J., & Van Den Dobbelsteen, A. (2016). An assessment of energy-saving solutions for the envelope design of high-rise buildings in temperate climates: A case study in the Netherlands. Energy and Buildings, 124, 210–221. doi:10.1016/j.enbuild.2015.10.049.

Cao, X., Liu, J., Cao, X., Li, Q., Hu, E., & Fan, F. (2015). Study of the thermal insulation properties of the glass fiber board used for interior building envelope. Energy and Buildings, 107, 49–58. doi:10.1016/j.enbuild.2015.08.007.

Naylor, S., Gillott, M., & Lau, T. (2018). A review of occupant-centric building control strategies to reduce building energy use. Renewable and Sustainable Energy Reviews, 96, 1–10. doi:10.1016/j.rser.2018.07.019.

Zahiri, S., & Elsharkawy, H. (2018). Towards energy-efficient retrofit of council housing in London: Assessing the impact of occupancy and energy-use patterns on building performance. Energy and Buildings, 174, 672–681. doi:10.1016/j.enbuild.2018.07.010.

Balvedi, B. F., Ghisi, E., & Lamberts, R. (2018). A review of occupant behaviour in residential buildings. Energy and Buildings, 174, 495–505. doi:10.1016/j.enbuild.2018.06.049.

Wang, Y., & Shao, L. (2017). Understanding occupancy pattern and improving building energy efficiency through Wi-Fi based indoor positioning. Building and Environment, 114, 106–117. doi:10.1016/j.buildenv.2016.12.015.

Yang, J., Santamouris, M., Lee, S. E., & Deb, C. (2016). Energy performance model development and occupancy number identification of institutional buildings. Energy and Buildings, 123, 192–204. doi:10.1016/j.enbuild.2015.12.018.

Deb, C., Zhang, F., Yang, J., Lee, S. E., & Shah, K. W. (2017). A review on time series forecasting techniques for building energy consumption. Renewable and Sustainable Energy Reviews, 74, 902–924. doi:10.1016/j.rser.2017.02.085.

Lee, S. H., Hong, T., Piette, M. A., & Taylor-Lange, S. C. (2015). Energy retrofit analysis toolkits for commercial buildings: A review. Energy, 89, 1087–1100. doi:10.1016/j.energy.2015.06.112.

Peng, Z., Deng, W., & Hong, Y. (2019). Materials consumption, indoor thermal comfort and associated energy flows of urban residential buildings: Case studies from the cold climate zone of China. International Journal of Building Pathology and Adaptation, 37(5), 579–596. doi:10.1108/IJBPA-01-2019-0007.

Zhou, Y., Zheng, S., Liu, Z., Wen, T., Ding, Z., Yan, J., & Zhang, G. (2020). Passive and active phase change materials integrated building energy systems with advanced machine-learning based climate-adaptive designs, intelligent operations, uncertainty-based analysis and optimizations: A state-of-the-art review. Renewable and Sustainable Energy Reviews, 130, 109889. doi:10.1016/j.rser.2020.109889.

Petri, I., Li, H., Rezgui, Y., Yang, C., Yuce, B., & Jayan, B. (2014). A modular optimization model for reducing energy consumption in large scale building facilities. Renewable and Sustainable Energy Reviews, 38, 990–1002. doi:10.1016/j.rser.2014.07.044.

Yang, C., Li, H., Rezgui, Y., Petri, I., Yuce, B., Chen, B., & Jayan, B. (2014). High throughput computing based distributed genetic algorithm for building energy consumption optimization. Energy and Buildings, 76, 92–101. doi:10.1016/j.enbuild.2014.02.053.

Beccali, M., Ciulla, G., Lo Brano, V., Galatioto, A., & Bonomolo, M. (2017). Artificial neural network decision support tool for assessment of the energy performance and the refurbishment actions for the non-residential building stock in Southern Italy. Energy, 137, 1201–1218. doi:10.1016/j.energy.2017.05.200.

Sghiouri, H., Charai, M., & Mezrhab, A. (2020). Optimization in Building Performance Simulation and Obstacles Facing Its Widespread Use. 2020 1st International Conference on Innovative Research in Applied Science, Engineering and Technology, IRASET 2020, 978–1–4673–7863–5. doi:10.1109/IRASET48871.2020.9091993.

Azari, R., Garshasbi, S., Amini, P., Rashed-Ali, H., & Mohammadi, Y. (2016). Multi-objective optimization of building envelope design for life cycle environmental performance. Energy and Buildings, 126, 524–534. doi:10.1016/j.enbuild.2016.05.054.

Bamdad Masouleh, K. (2018). Building energy optimization using machine learning and metaheuristic algorithms, PhD Thesis, Queensland University of Technology, Brisbane, Australia. doi:10.5204/thesis.eprints.120281

McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 5(4), 115-133. doi:10.1007/BF02478259.

Paradarami, T. K., Bastian, N. D., & Wightman, J. L. (2017). A hybrid recommender system using artificial neural networks. Expert Systems with Applications, 83, 300–313. doi:10.1016/j.eswa.2017.04.046.

Jaddi, N. S., Abdullah, S., & Hamdan, A. R. (2015). Multi-population cooperative bat algorithm-based optimization of artificial neural network model. Information Sciences, 294, 628–644. doi:10.1016/j.ins.2014.08.050.

Schenker, B. G. E. (1996). Prediction and control using feedback neural networks and partial models. PhD Thesis, Swiss Federal Institute of Technology Zurich (ETH Zurich), Zurich, Switzerland. doi:10.3929/ethz-a-001710141.

Karatasou, S., Santamouris, M., & Geros, V. (2006). Modeling and predicting building’s energy use with artificial neural networks: Methods and results. Energy and Buildings, 38(8), 949–958. doi:10.1016/j.enbuild.2005.11.005.

Nasr, G. E., Badr, E. A., & Younes, M. R. (2002). Neural networks in forecasting electrical energy consumption: Univariate and multivariate approaches. International Journal of Energy Research, 26(1), 67–78. doi:10.1002/er.766.

Javeed Nizami, S., & Al-Garni, A. Z. (1995). Forecasting electric energy consumption using neural networks. Energy Policy, 23(12), 1097–1104. doi:10.1016/0301-4215(95)00116-6.

Kaytez, F., Taplamacioglu, M. C., Cam, E., & Hardalac, F. (2015). Forecasting electricity consumption: A comparison of regression analysis, neural networks and least squares support vector machines. International Journal of Electrical Power and Energy Systems, 67, 431–438. doi:10.1016/j.ijepes.2014.12.036.

Panapakidis, I. P., & Dagoumas, A. S. (2016). Day-ahead electricity price forecasting via the application of artificial neural network based models. Applied Energy, 172, 132–151. doi:10.1016/j.apenergy.2016.03.089.

Dyvia, H. A., & Arif, C. (2021). Analysis of thermal comfort with predicted mean vote (PMV) index using artificial neural network. IOP Conference Series: Earth and Environmental Science, 622(1), 012019. doi:10.1088/1755-1315/622/1/012019.

Platon, R., Dehkordi, V. R., & Martel, J. (2015). Hourly prediction of a building’s electricity consumption using case-based reasoning, artificial neural networks and principal component analysis. Energy and Buildings, 92, 10–18. doi:10.1016/j.enbuild.2015.01.047.

Çunkaş, M., & Altun, A. A. (2010). Long term electricity demand forecasting in Turkey using artificial neural networks. Energy Sources, Part B: Economics, Planning and Policy, 5(3), 279–289. doi:10.1080/15567240802533542.

Azadeh, A., Ghaderi, S. F., & Sohrabkhani, S. (2008). Annual electricity consumption forecasting by neural network in high energy consuming industrial sectors. Energy Conversion and management, 49(8), 2272-2278. doi:10.1016/j.enconman.2008.01.035.

Ekonomou, L. (2010). Greek long-term energy consumption prediction using artificial neural networks. Energy, 35(2), 512–517. doi:10.1016/j.energy.2009.10.018.

Rahman, M. M., Shakeri, M., Tiong, S. K., Khatun, F., Amin, N., Pasupuleti, J., & Hasan, M. K. (2021). Prospective methodologies in hybrid renewable energy systems for energy prediction using artificial neural networks. Sustainability, 13(4), 2393. doi:10.3390/su13042393.

Neto, A. H., & Fiorelli, F. A. S. (2008). Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption. Energy and Buildings, 40(12), 2169–2176. doi:10.1016/j.enbuild.2008.06.013.

Li, A., Xiao, F., Zhang, C., & Fan, C. (2021). Attention-based interpretable neural network for building cooling load prediction. Applied Energy, 299, 117238. doi:10.1016/j.apenergy.2021.117238.

García-Gonzalo, E., Fernández-Muñiz, Z., Nieto, P. J. G., Sánchez, A. B., & Fernández, M. M. (2016). Hard-rock stability analysis for span design in entry-type excavations with learning classifiers. Materials, 9(7), 1–19. doi:10.3390/ma9070531.

Vapnik, V. (2000). Statistics for engineering and information science. The Nature of Statistical Learning Theory, Springer, New York, United States. doi:10.1007/978-1-4757-3264-1.

Zhang, F., Deb, C., Lee, S. E., Yang, J., & Shah, K. W. (2016). Time series forecasting for building energy consumption using weighted Support Vector Regression with differential evolution optimization technique. Energy and Buildings, 126, 94–103. doi:10.1016/j.enbuild.2016.05.028.

Li, M., Wang, W., De, G., Ji, X., & Tan, Z. (2018). Forecasting carbon emissions related to energy consumption in Beijing-Tianjin-Hebei region based on grey prediction theory and extreme learning machine optimized by support vector machine algorithm. Energies, 11(9). doi:10.3390/en11092475.

Zhou, D., Al-Durra, A., Zhang, K., Ravey, A., & Gao, F. (2019). A Robust Prognostic Indicator for Renewable Energy Technologies: A Novel Error Correction Grey Prediction Model. IEEE Transactions on Industrial Electronics, 66(12), 9312–9325. doi:10.1109/tie.2019.2893867.

Ju-Long, D. (1982). Control problems of grey systems. Systems and Control Letters, 1(5), 288–294. doi:10.1016/S0167-6911(82)80025-X.

Liu, S., & Lin, Y. (2010). Introduction to Grey Systems Theory. Grey Systems, 1–18. doi:10.1007/978-3-642-16158-2_1.

Hu, Y. C. (2017). A genetic-algorithm-based remnant grey prediction model for energy demand forecasting. PLoS One, 12(10), e0185478. doi:10.1371/journal.pone.0185478

Tsai, S. B., Xue, Y., Zhang, J., Chen, Q., Liu, Y., Zhou, J., & Dong, W. (2017). Models for forecasting growth trends in renewable energy. Renewable and Sustainable Energy Reviews, 77, 1169–1178. doi:10.1016/j.rser.2016.06.001.

Pao, H. T., Fu, H. C., & Tseng, C. L. (2012). Forecasting of CO2 emissions, energy consumption and economic growth in China using an improved grey model. Energy, 40(1), 400–409. doi:10.1016/j.energy.2012.01.037.

Wang, J., & Hu, J. (2015). A robust combination approach for short-term wind speed forecasting and analysis - Combination of the ARIMA (Autoregressive Integrated Moving Average), ELM (Extreme Learning Machine), SVM (Support Vector Machine) and LSSVM (Least Square SVM) forecasts using a GPR (Gaussian Process Regression) model. Energy, 93, 41–56. doi:10.1016/j.energy.2015.08.045.

Ozturk, S., & Ozturk, F. (2018). Forecasting Energy Consumption of Turkey by Arima Model. Journal of Asian Scientific Research, 8(2), 52–60. doi:10.18488/journal.2.2018.82.52.60.

Zhuang, J., Chen, Y., Shi, X., & Wei, D. (2015). Building cooling load prediction based on time series method and neural networks. International Journal of Grid and Distributed Computing, 8(4), 105–114. doi:10.14257/ijgdc.2015.8.4.10.

Yasmeen, F., & Sharif, M. (2014). Forecasting Electricity Consumption for Pakistan. International Journal of Emerging Technology and Advanced Engineering, 4(4), 496–503.

Wang, Y., Wang, J., Zhao, G., & Dong, Y. (2012). Application of residual modification approach in seasonal ARIMA for electricity demand forecasting: A case study of China. Energy Policy, 48, 284–294. doi:10.1016/j.enpol.2012.05.026.

Iredi, S., Merkle, D., & Middendorf, M. (2001). Bi-criterion optimization with multi colony ant algorithms. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 1993, 359–372. doi:10.1007/3-540-44719-9_25.

Dorigo, M., & Blum, C. (2005). Ant colony optimization theory: A survey. Theoretical computer science, 344(2-3), 243-278. doi:10.1016/j.tcs.2005.05.020.

Bamdad, K., Cholette, M. E., Guan, L., & Bell, J. (2017). Ant colony algorithm for building energy optimization problems and comparison with benchmark algorithms. Energy and Buildings, 154, 404–414. doi:10.1016/j.enbuild.2017.08.071.

Yuan, Y., Yuan, J., Du, H., & Li, L. (2012). An improved multi-objective ant colony algorithm for building life cycle energy consumption optimization. International Journal of Computer Applications in Technology, 43(1), 60–66. doi:10.1504/IJCAT.2012.045842.

Sun, Y., Dong, W., & Chen, Y. (2017). An Improved Routing Algorithm Based on Ant Colony Optimization in Wireless Sensor Networks. IEEE Communications Letters, 21(6), 1317–1320. doi:10.1109/LCOMM.2017.2672959.

Blas, N. G., de Mingo López, L. F., Aslanyan, L., & Ryazanov, V. (2011). Self-organizing Routing Algorithm for Wireless Sensors Networks (WSN) using Ant Colony Optimization (ACO) with Tinyos. International Journal on Information Technologies and Knowledge, 5(2), 142–151.

Aamodt, A. & E. Plaza (1994). Foundational issues, methodological variations, and system approaches. AI communications, 7(1), 39-59. doi:10.3233/AIC-1994-7104.

Monfet, D., Corsi, M., Choinière, D., & Arkhipova, E. (2014). Development of an energy prediction tool for commercial buildings using case-based reasoning. Energy and Buildings, 81, 152–160. doi:10.1016/j.enbuild.2014.06.017.

Cover, T. M., & Hart, P. E. (1967). Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory, 13(1), 21–27. doi:10.1109/TIT.1967.1053964.

Bhavsar, H., Jivani, A., Bhatt, T., Patel, N., & Shiledarbaxi, N. (2020). Bi-Iterative Optimized K-Nearest Neighbours Algorithm. ICT for Competitive Strategies (pp. 173-184). CRC Press, Boca Raton, United States. doi:10.1201/9781003052098-19.

Kristianto, A. W., Wijaya, F. D., & Tumiran. (2018). Fault location estimation in LCC HVDC transmission lines using k-nearest neighbors. Proceedings - 2018 3rd International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2018, 374–379. doi:10.1109/ICITISEE.2018.8720955.

Haerani, E., Apriyanti, L., & Wardhani, L. K. (2016). Application of unsupervised K Nearest Neighbor (UNN) and Learning Vector Quantization (LVQ) methods in predicting Rupiah to Dollar. Proceedings of 2016 4th International Conference on Cyber and IT Service Management, CITSM 2016, 4–7. doi:10.1109/CITSM.2016.7577579.

Chantakamo, A., & Ketcham, M. (2015). The multi vehicle recognition using hybrid blob analysis and feature-based. Proceedings - 2015 7th International Conference on Information Technology and Electrical Engineering: Envisioning the Trend of Computer, Information and Engineering, ICITEE 2015, 453–457. doi:10.1109/ICITEED.2015.7408989.

Diez, Y., Fort, M., & Sellarès, J. A. (2009). Solving Reverse k-Nearest Queries on Road Networks with the GPU. Actas XIII Encuentros de Geometr’ia Computacional, January, 117–124.

Lachut, D., Banerjee, N., & Rollins, S. (2015). Predictability of energy use in homes. 2014 International Green Computing Conference, IGCC 2014, 1–10,. doi:10.1109/IGCC.2014.7039146.

Reynolds, J., Rezgui, Y., Kwan, A., & Piriou, S. (2018). A zone-level, building energy optimization combining an artificial neural network, a genetic algorithm, and model predictive control. Energy, 151, 729–739. doi:10.1016/j.energy.2018.03.113.

Jung, H. C., Kim, J. S., & Heo, H. (2015). Prediction of building energy consumption using an improved real coded genetic algorithm based least squares support vector machine approach. Energy and Buildings, 90, 76–84. doi:10.1016/j.enbuild.2014.12.029.

Carlucci, S., Cattarin, G., Causone, F., & Pagliano, L. (2015). Multi-objective optimization of a nearly zero-energy building based on thermal and visual discomfort minimization using a non-dominated sorting genetic algorithm (NSGA-II). Energy and Buildings, 104, 378–394. doi:10.1016/j.enbuild.2015.06.064.

Eniola, V., Suriwong, T., Sirisamphanwong, C., Ungchittrakool, K., & Fasipe, O. (2021). Validation of Genetic Algorithm Optimized Hidden Markov Model for Short-term Photovoltaic Power Prediction. International Journal of Renewable Energy Research, 11(2), 796–807. doi:10.20508/ijrer.v11i2.11976.g8200.


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DOI: 10.28991/CEJ-2022-08-04-014

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