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Construction Management

Implementing a web-based optimized artificial intelligence system with metaheuristic optimization for improving building energy performance

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Pages 264-281 | Received 28 Nov 2022, Accepted 07 Jun 2023, Published online: 11 Jun 2023

References

  • Á, R.-L., D. L. González-Álvarez, M. A. Vega-Rodríguez, J. A. Gómez-Pulido, J. M. Sánchez-Pérez MO-ABC/DE - Multiobjective Artificial Bee Colony with Differential Evolution for Unconstrained Multiobjective Optimization. 2012 IEEE 13th International Symposium on Computational Intelligence and Informatics (CINTI) 2012, Budapest, Hungary. p. 157–162.
  • Aguila-Leon, J., C. Vargas-Salgado, C. Chiñas-Palacios, and D. Díaz-Bello. 2022. “Energy Management Model for a Standalone Hybrid Microgrid Through a Particle Swarm Optimization and Artificial Neural Networks Approach.” Energy Conversion and Management 267:115920. https://doi.org/10.1016/j.enconman.2022.115920.
  • Alobaidi, M. H., F. Chebana, and M. A. Meguid. 2018. “Robust Ensemble Learning Framework for Day-Ahead Forecasting of Household Based Energy Consumption.” Applied Energy 212:997–1012. https://doi.org/10.1016/j.apenergy.2017.12.054.
  • Aly, H. H. H. 2020. “A Proposed Intelligent Short-Term Load Forecasting Hybrid Models of ANN, WNN and KF Based on Clustering Techniques for Smart Grid.” Electric Power Systems Research 182:106191. https://doi.org/10.1016/j.epsr.2019.106191.
  • Amasyali, K., and N. M. El-Gohary. 2018. “A Review of Data-Driven Building Energy Consumption Prediction Studies.” Renewable and Sustainable Energy Reviews 81:1192–1205. https://doi.org/10.1016/j.rser.2017.04.095.
  • Amjady, N. 2001. “Short-Term Hourly Load Forecasting Using Time-Series Modeling with Peak Load Estimation Capability.” IEEE Transactions on Power Systems 16 (3): 498–505. https://doi.org/10.1109/59.932287.
  • Aws. 2021a. Amazon RDS for MySql. USA: Aws.
  • Aws. 2021b. Amazon Relational Database Service (RDS). USA: Aws.
  • Baba, A. 2021. “Advanced AI-Based Techniques to Predict Daily Energy Consumption: A Case Study.” Expert Systems with Applications 184:115508. https://doi.org/10.1016/j.eswa.2021.115508.
  • Belussi, L., B. Barozzi, A. Bellazzi, L. Danza, A. Devitofrancesco, C. Fanciulli, M. Ghellere, et al. 2019. “A Review of Performance of Zero Energy Buildings and Energy Efficiency Solutions.” Journal of Building Engineering 25:100772. https://doi.org/10.1016/j.jobe.2019.100772.
  • Biswas, M. A. R., M. D. Robinson, and N. Fumo. 2016. “Prediction of Residential Building Energy Consumption: A Neural Network Approach.” Energy 117:84–92. https://doi.org/10.1016/j.energy.2016.10.066.
  • Chen, B., Q. Liu, H. Chen, L. Wang, T. Deng, L. Zhang, X. Wu. et al. 2021. “Multiobjective Optimization of Building Energy Consumption Based on BIM-DB and LSSVM-NSGA-II.” Journal of Cleaner Production 294:126153. https://doi.org/10.1016/j.jclepro.2021.126153.
  • Chou, J.-S., and S.-M. Hsu. 2022. “Automated Prediction System of Household Energy Consumption in Cities Using Web Crawler and Optimized Artificial Intelligence.” International Journal of Energy Research 46 (1): 319–339. https://doi.org/10.1002/er.6742.
  • Chou, J.-S., and N.-T. Ngo. 2016. “Smart Grid Data Analytics Framework for Increasing Energy Savings in Residential Buildings.” Automation in Construction 72:247–257. https://doi.org/10.1016/j.autcon.2016.01.002.
  • Dubey, R., A. Gunasekaran, S. J. Childe, T. Papadopoulos, Z. Luo, S. F. Wamba, D. Roubaud. et al. 2019. “Can Big Data and Predictive Analytics Improve Social and Environmental Sustainability?” Technological Forecasting and Social Change 144:534–545. https://doi.org/10.1016/j.techfore.2017.06.020.
  • Elnour, M., Y. Himeur, F. Fadli, H. Mohammedsherif, N. Meskin, A. M. Ahmad, I. Petri, et al. 2022. “Neural Network-Based Model Predictive Control System for Optimizing Building Automation and Management Systems of Sports Facilities.” Applied Energy 318:119153. https://doi.org/10.1016/j.apenergy.2022.119153.
  • Fan, C., F. Xiao, C. Yan, C. Liu, Z. Li, and J. Wang. 2019. “A Novel Methodology to Explain and Evaluate Data-Driven Building Energy Performance Models Based on Interpretable Machine Learning.” Applied Energy 235:1551–1560. https://doi.org/10.1016/j.apenergy.2018.11.081.
  • Firefly Algorithm, Y. X.-S. 2008. Nature-inspired Metaheuristic Algorithms. Bristol, UK: Luniver Press.
  • Funde, N. A., M. M. Dhabu, A. Paramasivam, and P. S. Deshpande. 2019. “Motif-Based Association Rule Mining and Clustering Technique for Determining Energy Usage Patterns for Smart Meter Data.” Sustainable Cities and Society 46:101415. https://doi.org/10.1016/j.scs.2018.12.043.
  • Guo, Y., J. Wang, H. Chen, G. Li, J. Liu, C. Xu, R. Huang, et al. 2018. “Machine Learning-Based Thermal Response Time Ahead Energy Demand Prediction for Building Heating Systems.” Applied Energy 221:16–27. https://doi.org/10.1016/j.apenergy.2018.03.125.
  • Himeur, Y., A. Alsalemi, F. Bensaali, and A. Amira. 2021. “Smart Power Consumption Abnormality Detection in Buildings Using Micromoments and Improved K-Nearest Neighbors.” International Journal of Intelligent Systems 36 (6): 2865–2894. https://doi.org/10.1002/int.22404.
  • Himeur, Y., K. Ghanem, A. Alsalemi, F. Bensaali, and A. Amira. 2021. “Artificial Intelligence Based Anomaly Detection of Energy Consumption in Buildings: A Review, Current Trends and New Perspectives.” Applied Energy 287:116601. https://doi.org/10.1016/j.apenergy.2021.116601.
  • Hønsi, T., G. Hjetland, A. Nesse, A. Jorunn Fjærestad, C. Vasseng, and L.-F. Berntsen. 2021. “Highcharts Library.“ Accessed Apr 17, 2021. https://www.highcharts.com/
  • Jahani, E., K. Cetin, and I. H. Cho. 2020. “City-Scale Single Family Residential Building Energy Consumption Prediction Using Genetic Algorithm-Based Numerical Moment Matching Technique.” Building and Environment 172:106667. https://doi.org/10.1016/j.buildenv.2020.106667.
  • Jeong, J., T. Hong, C. Ji, J. Kim, M. Lee, K. Jeong, C. Koo, et al. 2017. “Development of a Prediction Model for the Cost Saving Potentials in Implementing the Building Energy Efficiency Rating Certification.” Applied Energy 189:257–270. https://doi.org/10.1016/j.apenergy.2016.12.024.
  • JetBrains. 2021. JetBrains. IntelliJ IDEA. https://www.jetbrains.com/idea/
  • Khan, I., A. Capozzoli, S. P. Corgnati, and T. Cerquitelli. 2013. “Fault Detection Analysis of Building Energy Consumption Using Data Mining Techniques.” Energy Procedia 42:557–566. https://doi.org/10.1016/j.egypro.2013.11.057.
  • Kim, J.-Y., and S.-B. Cho. 2021. “Explainable Prediction of Electric Energy Demand Using a Deep Autoencoder with Interpretable Latent Space.” Expert Systems with Applications 186:115842. https://doi.org/10.1016/j.eswa.2021.115842.
  • Linder, L., D. Vionnet, J.-P. Bacher, and J. Hennebert. 2017. “Big Building Data - a Big Data Platform for Smart Buildings.” Energy Procedia 122:589–594. https://doi.org/10.1016/j.egypro.2017.07.354.
  • Liu, Y., H. Chen, L. Zhang, X. Wu, and W. X-J. 2020. “Energy Consumption Prediction and Diagnosis of Public Buildings Based on Support Vector Machine Learning: A Case Study in China.” Journal of Cleaner Production 272:122542. https://doi.org/10.1016/j.jclepro.2020.122542.
  • Li, J., C. Zhang, Y. Zhao, W. Qiu, Q. Chen, and X. Zhang. 2022. “Federated Learning-Based Short-Term Building Energy Consumption Prediction Method for Solving the Data Silos Problem.” Building Simulation 15 (6): 1145–1159. https://doi.org/10.1007/s12273-021-0871-y.
  • Luo, X. J., L. O. Oyedele, A. O. Ajayi, C. G. Monyei, O. O. Akinade, and L. A. Akanbi. 2019. “Development of an IoT-Based Big Data Platform for Day-Ahead Prediction of Building Heating and Cooling Demands.” Advanced Engineering Informatics 41:100926. https://doi.org/10.1016/j.aei.2019.100926.
  • Ngo, N.-T., and T. T-T-H. 2019. “Forecasting Time Series Data Using Moving-Window Swarm Intelligence-Optimised Machine Learning Regression.” International Journal of Intelligent Engineering Informatics 7:422–440. https://doi.org/10.1504/IJIEI.2019.103625.
  • Olu-Ajayi, R., H. Alaka, I. Sulaimon, F. Sunmola, and S. Ajayi. 2022. “Building Energy Consumption Prediction for Residential Buildings Using Deep Learning and Other Machine Learning Techniques.” Journal of Building Engineering 45:103406. https://doi.org/10.1016/j.jobe.2021.103406.
  • Pham, A.-D., N.-T. Ngo, T. T. Ha Truong, N.-T. Huynh, and N.-S. Truong. 2020. “Predicting Energy Consumption in Multiple Buildings Using Machine Learning for Improving Energy Efficiency and Sustainability.” Journal of Cleaner Production 260:121082. https://doi.org/10.1016/j.jclepro.2020.121082.
  • Rajabi, A., M. Eskandari, M. J. Ghadi, L. Li, J. Zhang, and P. Siano. 2020. “A Comparative Study of Clustering Techniques for Electrical Load Pattern Segmentation.” Renewable and Sustainable Energy Reviews 120:109628. https://doi.org/10.1016/j.rser.2019.109628.
  • Ruiz, L. G. B., M. C. Pegalajar, R. Arcucci, and M. Molina-Solana. 2020. “A Time-Series Clustering Methodology for Knowledge Extraction in Energy Consumption Data.” Expert Systems with Applications 160:113731. https://doi.org/10.1016/j.eswa.2020.113731.
  • Ruiz, L. G. B., R. Rueda, M. P. Cuéllar, and M. C. Pegalajar. 2018. “Energy Consumption Forecasting Based on Elman Neural Networks with Evolutive Optimization.” Expert Systems with Applications 92:380–389. https://doi.org/10.1016/j.eswa.2017.09.059.
  • Sayed, A., Y. Himeur, A. Alsalemi, F. Bensaali, and A. Amira. 2022. “Intelligent Edge-Based Recommender System for Internet of Energy Applications.” IEEE Systems Journal 16 (3): 5001–5010. https://doi.org/10.1109/JSYST.2021.3124793.
  • Sha, H., P. Xu, C. Yan, Y. Ji, K. Zhou, and F. Chen. 2022. “Development of a Key-Variable-Based Parallel HVAC Energy Predictive Model.” Building Simulation 15 (7): 1193–1208. https://doi.org/10.1007/s12273-021-0885-0.
  • Sholahudin, N., P. Satrio, T. M. I. Mahlia, N. Giannetti, K. Saito, and K. Saito. 2019. “Optimization of HVAC System Energy Consumption in a Building Using Artificial Neural Network and Multi-Objective Genetic Algorithm.” Sustainable Energy Technologies and Assessments 35:48–57. https://doi.org/10.1016/j.seta.2019.06.002.
  • Srivastava, C., Z. Yang, and R. K. Jain. 2019. “Understanding the Adoption and Usage of Data Analytics and Simulation Among Building Energy Management Professionals: A Nationwide Survey.” Building and Environment 157:139–164. https://doi.org/10.1016/j.buildenv.2019.04.016.
  • Terroso-Saenz, F., A. González-Vidal, A. P. Ramallo-González, and A. F. Skarmeta. 2019. “An Open IoT Platform for the Management and Analysis of Energy Data.” Future Generation Computer Systems 92:1066–1079. https://doi.org/10.1016/j.future.2017.08.046.
  • Tian, W., C. Zhu, Y. Sun, Z. Li, and B. Yin. 2021. “Energy Characteristics of Urban Buildings: Assessment by Machine Learning.” Building Simulation 14 (1): 179–193. https://doi.org/10.1007/s12273-020-0608-3.
  • Tseng, F.-M., and G.-H. Tzeng. 2002. “A Fuzzy Seasonal ARIMA Model for Forecasting.” Fuzzy Sets and Systems 126 (3): 367–376. https://doi.org/10.1016/S0165-0114(01)00047-1.
  • Varlamis, I., C. Sardianos, C. Chronis, G. Dimitrakopoulos, Y. Himeur, A. Alsalemi, F. Bensaali, et al. 2022. “Smart Fusion of Sensor Data and Human Feedback for Personalized Energy-Saving Recommendations.” Applied Energy 305:117775. https://doi.org/10.1016/j.apenergy.2021.117775.
  • Walker, S., W. Khan, K. Katic, W. Maassen, and W. Zeiler. 2020. “Accuracy of Different Machine Learning Algorithms and Added-Value of Predicting Aggregated-Level Energy Performance of Commercial Buildings.” Energy and Buildings 209:109705. https://doi.org/10.1016/j.enbuild.2019.109705.
  • Wang, Y., J. Wang, G. Zhao, and Y. Dong. 2012. “Application of Residual Modification Approach in Seasonal ARIMA for Electricity Demand Forecasting: A Case Study of China.” Energy Policy 48:284–294. https://doi.org/10.1016/j.enpol.2012.05.026.
  • Wen, L., K. Zhou, and S. Yang. 2019. “A Shape-Based Clustering Method for Pattern Recognition of Residential Electricity Consumption.” Journal of Cleaner Production 212:475–488. https://doi.org/10.1016/j.jclepro.2018.12.067.
  • Wilcox, T., N. Jin, P. Flach, and J. Thumim. 2019. “A Big Data Platform for Smart Meter Data Analytics.” Computers in Industry 105:250–259. https://doi.org/10.1016/j.compind.2018.12.010.
  • Wong, C. H. H., M. Cai, C. Ren, Y. Huang, C. Liao, and S. Yin. 2021. “Modelling Building Energy Use at Urban Scale: A Review on Their Account for the Urban Environment.” Building and Environment 205:108235. https://doi.org/10.1016/j.buildenv.2021.108235.
  • Wu, W., W. Lin, C.-H. Hsu, and L. He. 2018. “Energy-Efficient Hadoop for Big Data Analytics and Computing: A Systematic Review and Research Insights.” Future Generation Computer Systems 86:1351–1367. https://doi.org/10.1016/j.future.2017.11.010.
  • Xu, C., and H. Chen. 2020. “A Hybrid Data Mining Approach for Anomaly Detection and Evaluation in Residential Buildings Energy Data.” Energy and Buildings 215:109864. https://doi.org/10.1016/j.enbuild.2020.109864.
  • Yang, J., C. Ning, C. Deb, F. Zhang, D. Cheong, S. E. Lee, C. Sekhar. et al. 2017. “K-Shape Clustering Algorithm for Building Energy Usage Patterns Analysis and Forecasting Model Accuracy Improvement.” Energy and Buildings 146:27–37. https://doi.org/10.1016/j.enbuild.2017.03.071.
  • Yang, J., J. Zhao, F. Wen, and Z. Dong. 2019. “A Model of Customizing Electricity Retail Prices Based on Load Profile Clustering Analysis.” IEEE Transactions on Smart Grid 10 (3): 3374–3386. https://doi.org/10.1109/TSG.2018.2825335.
  • Yeo, J., Y. Wang, A. K. An, and L. Zhang. 2019. “Estimation of Energy Efficiency for Educational Buildings in Hong Kong.” Journal of Cleaner Production 235:453–460. https://doi.org/10.1016/j.jclepro.2019.06.339.
  • Yuan, X., Y. Pan, J. Yang, W. Wang, and Z. Huang. 2021. “Study on the Application of Reinforcement Learning in the Operation Optimization of HVAC System.” Building Simulation 14 (1): 75–87. https://doi.org/10.1007/s12273-020-0602-9.
  • Zekić-Sušac, M., S. Mitrović, and A. Has. 2020. “Machine Learning Based System for Managing Energy Efficiency of Public Sector as an Approach Towards Smart Cities.” International Journal of Information Management 58:102074. https://doi.org/10.1016/j.ijinfomgt.2020.102074.
  • Zhao, T., J. Xu, C. Zhang, and P. Wang. 2021. “A Monitoring Data Based Bottom-Up Modeling Method and Its Application for Energy Consumption Prediction of Campus Building.” Journal of Building Engineering 35:101962. https://doi.org/10.1016/j.jobe.2020.101962.
  • Zhou, K., C. Fu, and S. Yang. 2016. “Big Data Driven Smart Energy Management: From Big Data to Big Insights.” Renewable and Sustainable Energy Reviews 56:215–225. https://doi.org/10.1016/j.rser.2015.11.050.
  • Zhu, J., Y. Shen, Z. Song, D. Zhou, Z. Zhang, and A. Kusiak. 2019. “Data-Driven Building Load Profiling and Energy Management.” Sustainable Cities and Society 49:101587. https://doi.org/10.1016/j.scs.2019.101587.