[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123418-en":3,"doc-seo-123418-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123418,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","Enhancing Energy Consumption Prediction by Integrating Occupant Activity with Machine Learning Models","Accurate building power-consumption forecasting is essential for modern large-scale construction, yet many existing models overlook how people’s in-building activities affect energy use. A synchronized data-collection approach gathers time-aligned sensor data for occupancy activity and power consumption. Multiple machine learning models are trained and evaluated to examine how occupant behavior influences power usage, enabling behavior-sensitive prediction. Results show that incorporating occupant-activity data improves energy-use assessment accuracy and supports more adaptive, efficient building management systems while addressing a human-factor gap in energy-forecasting research.","Enhancing Energy Consumption Prediction by Integrating Occupant Activity with Machine Learning Models  \nFarzana Sharmin Nila1, Wooi-Haw Tan1*, Chee-Pun Ooi1, Muhammad Umair 1, Yi-Fei Tan1, Soon-Nyean Cheong2  \n1 Faculty of Engineering, ultimedia University, Persiaran Multimedia, 63100 Cyberjaya, Selangor, MALAYSI  \n2 Faculty of Creative Multimedia, ultimedia University, Persiaran Multimedia, 63100 Cyberjaya, Selangor, MALAYSI  \n*Corresponding Author: [twhaw@mmu.edu.my](twhaw@mmu.edu.my)  \nDOI: [https://doi.org/10.30880/ijie.2025.17.02.002](https://doi.org/10.30880/ijie.2025.17.02.002)  \n\n| Article Info | Abstract |\n| --- | --- |\n| Received: 10 November 2024 | The precision of the forecast of the power consumption of buildings is |\n| Accepted: 28 June 2025 | essential for big constructions in the present day. However, many of the |\n| Available online: 18 July 2025 | models in use fail to consider the effect of people’s activities within the building on energy consumption. To overcome this limitation, this |\n| Keywords\u003Cbr>Synchronization, data collection, machine learning models, prediction, occupant activity | paper uses a synchronized data collection approach to collect data from different sensors about occupancy activity and power consumption. Several machine learning models are employed with this coordinated data, and the effects of occupant behaviour on power usage are explored. By analyzing the results of the models generated by the two algorithms, the best ways of reaching behaviour-sensitive power consumption prediction are determined. Therefore, the findings establish that the additional data concerning occupant activity provides more accurate assessments of energy usage that can be quite beneficial for enhancing the further development of better adaptive and more efficient building management systems. This work also helps to fill the existing gap in energy prediction literature wherein, unlike other fields, the human factor is considered in machine learning models that can lead to more accurate and less distortion-prone energy forecasting. |\n\n1. Introduction  \nAccording to reports, about 30 to 40 percent of the world's energy consumption is within the building sector, while more than 80 percent of the used energy in a building happens in a building's entire lifespan, that is, the operational phase, which is inclusive of heating, cooling, ventilation, and lighting [1] . It, therefore, becomes very important to have predictions regarding a building's use of energy to make energy-efficient decisions. Use of data analytics: Lately, data analytics has made huge strides in development, such that new and advanced machine learning models are in place to be developed and deployed in the field of energy prediction [2] . As much as data analytics is improved, machine learning models have evolved to handle big data sets that capture several factors that could affect energy use—for example, environmental conditions, weather patterns, and equipment performances [3].  \nAn artificial intelligence system employs machine learning, which is a guideline or procedure that enables the system to locate beneficial patterns and solutions within a predetermined amount of data, or it can be employed to predict output values depending on a certain amount of input values [4] . Machine learning, in turn, needs algorithms to learn [5]. One needs a set of data and then investigate the relationship between them, define patterns  \nand use algorithms, which allow one to take a sum of input data and, according to definite patterns, produce definite outputs [5], [6].  \nSensors are fundamental components of the IoT, as they allow information to be gathered from several physical spaces [6] . Keeping data consistent in time is important for accuracy when building machine learning models. Therefore, if data responses are asynchronous, then the simulations are inaccurate, predictions wrong, and decisions unhelpful to the model, hence making a model ineffective","cbCaijd3cNbcYNvP","https://ap.wps.com/l/cbCaijd3cNbcYNvP","pdf",1384792,1,16,"English","en",105,"# Introduction\n## Building energy forecasting needs\n## Machine learning and sensor data\n## Time synchronization and IoT sensing\n## Occupant behavior as a missing factor","[{\"question\":\"Why do current energy-consumption prediction models often underperform?\",\"answer\":\"Many existing models fail to account for how occupants’ activities inside buildings influence energy consumption, which reduces forecast accuracy for real operational decisions.\"},{\"question\":\"What role does synchronized data collection play in the proposed work?\",\"answer\":\"Synchronized collection aligns data from different sensors in time, enabling accurate correlation between occupancy behavior and energy-consumption variables and improving prediction reliability.\"},{\"question\":\"How are machine learning models used to evaluate occupant behavior effects?\",\"answer\":\"Several machine learning models are trained with the coordinated occupant-activity and power-consumption dataset, and the results are analyzed to determine approaches that best produce behavior-sensitive power predictions.\"}]","Enhancing Energy Consumption Prediction by Integrating Occupant Activity with Machine Learning Models | 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do current energy-consumption prediction models often underperform?","Question",{"text":76,"@type":77},"Many existing models fail to account for how occupants’ activities inside buildings influence energy consumption, which reduces forecast accuracy for real operational decisions.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What role does synchronized data collection play in the proposed work?",{"text":81,"@type":77},"Synchronized collection aligns data from different sensors in time, enabling accurate correlation between occupancy behavior and energy-consumption variables and improving prediction reliability.",{"name":83,"@type":74,"acceptedAnswer":84},"How are machine learning models used to evaluate occupant behavior effects?",{"text":85,"@type":77},"Several machine learning models are trained with the coordinated occupant-activity and power-consumption dataset, and the results are analyzed to determine approaches that best produce behavior-sensitive power 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