Big Data Analysis to Predict Consumption Patterns in Smart Cities
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Big Data, Consumption Patterns, Predictive Analytics, Resource Management, Smart Cities
Abstract
The rapid development of smart cities has increased the demand for efficient resource management and personalized services, where understanding consumption patterns is crucial. Big data analysis offers a powerful tool for predicting these patterns, enabling city planners and service providers to make data-driven decisions to enhance urban living quality. This study aims to utilize big data analytics to predict consumption patterns across various sectors in smart cities, including energy, water, and transportation. By leveraging large datasets, this research seeks to provide actionable insights for optimizing resource allocation and anticipating future consumption demands. The methodology involves collecting and analyzing data from multiple sources, such as IoT sensors, public utility records, and social media, to identify consumption trends. Machine learning algorithms, including time series analysis and clustering, were applied to detect patterns and forecast demand. Results indicate that big data analytics can accurately predict consumption fluctuations, with an 85% accuracy in energy demand forecasting and a 78% accuracy in water usage prediction. The findings highlight correlations between demographic factors and consumption, providing a comprehensive understanding of urban needs. The study concludes that big data analysis is a valuable approach to managing resources effectively in smart cities. By predicting consumption patterns, city planners can proactively address demand surges, reduce waste, and improve resource distribution, ultimately supporting sustainable urban growth. Implementing these insights could significantly enhance smart city efficiency and resilience.
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References
Abassi, A. (2023). Moroccan Consumer Energy Consumption Itemsets and Inter-Appliance Associations Using Machine Learning Algorithms and Data Mining Techniques. Journal of Engineering for Sustainable Buildings and Cities, 4(1). https://doi.org/10.1115/1.4062113
Abdel-Basset, M. (2022). STLF-Net: Two-stream deep network for short-term load forecasting in residential buildings. Journal of King Saud University - Computer and Information Sciences, 34(7), 4296–4311. https://doi.org/10.1016/j.jksuci.2022.04.016
Agaev, N. B. (2020). Using a Fuzzy Prognostic Model in the Operative-Dispatch Analysis of Heat-Supply Systems’ Operation. Thermal Engineering, 67(9), 680–683. https://doi.org/10.1134/S0040601520090013
Anitha, T. (2023). Real-Time Innovative Power Consumption Surveillance System for Residential and Industrial Application. 7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023 - Proceedings, Query date: 2025-03-01 10:04:30, 105–111. https://doi.org/10.1109/ICECA58529.2023.10395085
Aurangzeb, K. (2019). Short Term Power Load Forecasting using Machine Learning Models for energy management in a smart community. 2019 International Conference on Computer and Information Sciences (ICCIS), 1–6. https://doi.org/10.1109/ICCISci.2019.8716475
Badra, Y. (2024). PhD school: Comprehensive Energy Consumption Analysis in Mobile Networks: Integrating Base Station and User Equipment Measurements. International Conference on Embedded Wireless Systems and Networks, 1(Query date: 2025-03-01 10:04:30). https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85218240119&origin=inward
Balci, A. M. (2023). A Cross-Domain Energy Optimization Approach for End-to-End Cloud Environments. 7th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2023 - Proceedings, Query date: 2025-03-01 10:04:30. https://doi.org/10.1109/ISMSIT58785.2023.10304985
Batra, P. (2022). An Evaluation of Intelligent Network Data Analytics Based on Machine Learning in 5G Data Networks. 2022 International Conference on Futuristic Technologies, INCOFT 2022, Query date: 2025-03-01 10:04:30. https://doi.org/10.1109/INCOFT55651.2022.10094324
Bhende, M. (2024). A critical analysis in identifying the major factors of big data analytics in enhancing the supply chain management process for sustainable development—A machine learning approach. AIP Conference Proceedings, 3139(1). https://doi.org/10.1063/5.0224541
Camera, B. F. (2019). Historical assumptions about the predation patterns of yellow anacondas (eunectes notaeus): Are they infrequent feeders? Journal of Herpetology, 53(1), 47–52. https://doi.org/10.1670/18-089
Chen, H. (2022). Irrigation Scheduling Optimization for Ecological Security Water and Eco-Environment Relationship. ICEIEC 2022 - Proceedings of 2022 IEEE 12th International Conference on Electronics Information and Emergency Communication, Query date: 2025-03-01 10:04:30, 255–258. https://doi.org/10.1109/ICEIEC54567.2022.9835044
Choi, S. G. (2021). Adaptive granularity based last-level cache prefetching method with edram prefetch buffer for graph processing applications. Applied Sciences (Switzerland), 11(3), 1–24. https://doi.org/10.3390/app11030991
Crowson, P. (2024). Non-ferrous metal inventories and the London metal exchange: A commentary. Mineral Economics, 37(2), 343–357. https://doi.org/10.1007/s13563-023-00396-w
D’Attoma, I. (2024). A new composite index to assess environmental consciousness using survey data and big data: Empirical evidence from European consumers. Socio-Economic Planning Sciences, 95(Query date: 2025-03-01 10:04:30). https://doi.org/10.1016/j.seps.2024.102038
Dave, R. (2022). Integrating Data Mining Methods Across all Domains of a Smart City. Journal of Computer Science, 18(5), 396–414. https://doi.org/10.3844/jcssp.2022.396.414
Dayapule, S. (2019). PowerStar: Improving power efficiency in heterogenous processors for bursty workloads with approximate computing. Proceedings of the International Conference on Cloud Computing Technology and Science, CloudCom, 2019(Query date: 2025-03-01 10:04:30), 175–182. https://doi.org/10.1109/CloudCom.2019.00035
Fan, L. (2020). Load prediction methods using machine learning for home energy management systems based on human behavior patterns recognition. CSEE Journal of Power and Energy Systems, 6(3), 563–571. https://doi.org/10.17775/CSEEJPES.2018.01130
Felicetti, F. (2024). Fish Blood Cell as Biological Dosimeter: In Between Measurements, Radiomics, Preprocessing, and Artificial Intelligence. Lecture Notes in Networks and Systems, 1117(Query date: 2025-03-01 10:04:30), 39–51. https://doi.org/10.1007/978-981-97-6992-6_4
Gan, M. (2021). Review on Application of Truck Trajectory Data in Highway Freight System. Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/Journal of Transportation Systems Engineering and Information Technology, 21(5). https://doi.org/10.16097/j.cnki.1009-6744.2021.05.009
Gholami, R. (2021). Modeling residential energy consumption: An application of IT-based solutions and big data analytics for sustainability. Journal of Global Information Management, 29(2), 1–22. https://doi.org/10.4018/JGIM.2021030109
Gómez-Omella, M. (2021). K-Nearest patterns for electrical demand forecasting in residential and small commercial buildings. Energy and Buildings, 253(Query date: 2025-03-01 09:57:18). https://doi.org/10.1016/j.enbuild.2021.111396
González, I. D. E. (2023). Application of AI algorithms and tools to optimize the electrical energy consumption of a building. 2023 IEEE Workshop on Power Electronics and Power Quality Applications, PEPQA 2023 - Proceedings, Query date: 2025-03-01 10:04:30. https://doi.org/10.1109/PEPQA59611.2023.10325704
Good, C. (2021). Connecting the spots: Leopard print fashion and Panthera pardus conservation. Journal for Nature Conservation, 61(Query date: 2025-03-01 10:04:30). https://doi.org/10.1016/j.jnc.2021.125976
Grande, S. D. (2024). Data Science for the Promotion of Sustainability in Smart Water Distribution Systems. Communications in Computer and Information Science, 2105(Query date: 2025-03-01 10:04:30), 50–72. https://doi.org/10.1007/978-3-031-68919-2_3
Gunduz, M. Z. (2024). Smart Grid Security: An Effective Hybrid CNN-Based Approach for Detecting Energy Theft Using Consumption Patterns. Sensors, 24(4). https://doi.org/10.3390/s24041148
Haidar, N. (2020). Occupant Behavior Prediction and Real-Time Correction-based Smart Building Energy Optimization. Proceedings - IEEE Global Communications Conference, GLOBECOM, 2020(Query date: 2025-03-01 10:04:30). https://doi.org/10.1109/GLOBECOM42002.2020.9348056
He, J. (2021). Internet User Behavior Analysis Based on Big Data. 2021 International Wireless Communications and Mobile Computing, IWCMC 2021, Query date: 2025-03-01 10:04:30, 432–435. https://doi.org/10.1109/IWCMC51323.2021.9498875
Jain, V. (2024). Integrative hybrid information systems for enhanced traffic maintenance and control in Bangalore: A synchronized approach. Hybrid Information Systems: Non-Linear Optimization Strategies with Artificial Intelligence, Query date: 2025-03-01 09:57:18, 223–240. https://doi.org/10.1515/9783111331133-012
Jiang, Y. (2024). Self-Powered Traffic Lights Through Wind Energy Harvesting Based on High-Performance Fur-Brush Dish Triboelectric Nanogenerators. Small, 20(40). https://doi.org/10.1002/smll.202402661
Khalid, R. (2020). Electricity load and price forecasting using jaya-long short term memory (JLSTM) in smart grids. Entropy, 22(1), 10–10. https://doi.org/10.3390/e22010010
Khan, M. N. (2024). Impact of Big Data and Knowledge Management on Customer Interactions and Consumption Patterns: Applied Science Research Perspective. Engineering, Technology and Applied Science Research, 14(3), 14125–14133. https://doi.org/10.48084/etasr.7203
Liu, Y. (2024). Intelligent Analysis and Prediction of User Electricity Consumption Status Based on Big Data. Proceedings - 2024 International Conference on Electrical Drives, Power Electronics and Engineering, EDPEE 2024, Query date: 2025-03-01 10:04:30, 671–676. https://doi.org/10.1109/EDPEE61724.2024.00130
Marquez-Saldaña, F. J. (2022). Enabling Knowledge Extraction on Bike Sharing Systems Throughout Open Data. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13335(Query date: 2025-03-01 09:57:18), 570–585. https://doi.org/10.1007/978-3-031-04987-3_39
Masoudi, S., & Safi-Esfahani, F. (2022). SM@RMFFOG: Sensor mining at resource management framework of fog computing. The Journal of Supercomputing, 78(17), 19188–19227. https://doi.org/10.1007/s11227-022-04592-3
Mehta, V. G. P. (2024). A Study on the Implementation of Dynamic Pricing Mechanisms for Sustainable Energy Management Using AI-Driven Demand Prediction. Environmental Science and Engineering, Query date: 2025-03-01 10:04:30, 935–950. https://doi.org/10.1007/978-3-031-63901-2_61
Miraftabzadeh, S. M. (2021). Estimation model of total energy consumptions of electrical vehicles under different driving conditions. Energies, 14(4). https://doi.org/10.3390/en14040854
Mourtzios, C. (2021). Work-in-Progress: SMART-WATER, a ?ovel ?elemetry and Remote Control System Infrastructure for the Management of Water Consumption in Thessaloniki. Advances in Intelligent Systems and Computing, 1192(Query date: 2025-03-01 09:57:18), 962–970. https://doi.org/10.1007/978-3-030-49932-7_89
Quintanilla, J. I. G. (2024). Design of monitoring tool for decision making in buildings with solar energy generation. Procedia Computer Science, 246(Query date: 2025-03-01 09:57:18), 1090–1099. https://doi.org/10.1016/j.procs.2024.09.528
Rawindaran, N. (2023). Legal Considerations and Ethical Challenges of Artificial Intelligence on Internet of Things and Smart Cities. Data Protection in a Post-Pandemic Society: Laws, Regulations, Best Practices and Recent Solutions, Query date: 2025-03-01 09:57:18, 217–239. https://doi.org/10.1007/9783031340062_8
Souissi, A. (2022). Determinants of Food Consumption Water Footprint in the MENA Region: The Case of Tunisia. Sustainability (Switzerland), 14(3). https://doi.org/10.3390/su14031539
Teres, A. D. (2019). Histogram Visualization of Smart Grid data using Mapreduce algorithm. Proceedings of the 2019 2nd International Conference on Power and Embedded Drive Control, ICPEDC 2019, Query date: 2025-03-01 10:04:30, 307–312. https://doi.org/10.1109/ICPEDC47771.2019.9036693
Wang, X. (2022). Analysis of changes in population’s cross-city travel patterns in the pre- and post-pandemic era: A case study of China. Cities, 122(Query date: 2025-03-01 09:57:18). https://doi.org/10.1016/j.cities.2021.103472
Wu, C. C. (2022). Evaluation of “Four Good” Rural Roads Based on Text Mining Technology. Gonglu Jiaotong Keji/Journal of Highway and Transportation Research and Development, 39(1), 175–182. https://doi.org/10.3969/j.issn.1002-0268.2022.01.023
Zhang, J. (2022). Multidimensional Evaluation of the Quality of Rural Life Using Big Data from the Perspective of Common Prosperity. International Journal of Environmental Research and Public Health, 19(21). https://doi.org/10.3390/ijerph192114166
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Copyright (c) 2025 Anto Susilo, Rachmat Prasetiyo, Bilal Aslam, Rina Farah
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