Machine learning approach in the development of building occupant personas

• URL: http://arxiv.org/abs/2207.11239v1
• Date: Tue, 19 Jul 2022 20:27:22 GMT
• Title: Machine learning approach in the development of building occupant personas
• Authors: Sheik Murad Hassan Anik, Xinghua Gao, Na Meng
• Abstract summary: Building occupant personas are a communication tool for designers to generate a mental model that describes the archetype of users. In this study, we propose and evaluate a machine learning-based semi-automated approach to generate building occupant personas. The models achieve an average accuracy of 61% and accuracy over 90% for attributes including the number of occupants in the household, their age group, and preferred usage of heating or cooling equipment.
• Score: 4.932806255841464
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The user persona is a communication tool for designers to generate a mental model that describes the archetype of users. Developing building occupant personas is proven to be an effective method for human-centered smart building design, which considers occupant comfort, behavior, and energy consumption. Optimization of building energy consumption also requires a deep understanding of occupants' preferences and behaviors. The current approaches to developing building occupant personas face a major obstruction of manual data processing and analysis. In this study, we propose and evaluate a machine learning-based semi-automated approach to generate building occupant personas. We investigate the 2015 Residential Energy Consumption Dataset with five machine learning techniques - Linear Discriminant Analysis, K Nearest Neighbors, Decision Tree (Random Forest), Support Vector Machine, and AdaBoost classifier - for the prediction of 16 occupant characteristics, such as age, education, and, thermal comfort. The models achieve an average accuracy of 61% and accuracy over 90% for attributes including the number of occupants in the household, their age group, and preferred usage of heating or cooling equipment. The results of the study show the feasibility of using machine learning techniques for the development of building occupant persona to minimize human effort.

Related papers

• Data Augmentation in Human-Centric Vision [54.97327269866757]
  This survey presents a comprehensive analysis of data augmentation techniques in human-centric vision tasks. It delves into a wide range of research areas including person ReID, human parsing, human pose estimation, and pedestrian detection. Our work categorizes data augmentation methods into two main types: data generation and data perturbation.
  arXiv  Detail & Related papers  (2024-03-13T16:05:18Z)
• Evaluating the Utility of Model Explanations for Model Development [54.23538543168767]
  We evaluate whether explanations can improve human decision-making in practical scenarios of machine learning model development. To our surprise, we did not find evidence of significant improvement on tasks when users were provided with any of the saliency maps. These findings suggest caution regarding the usefulness and potential for misunderstanding in saliency-based explanations.
  arXiv  Detail & Related papers  (2023-12-10T23:13:23Z)
• Real-time Addressee Estimation: Deployment of a Deep-Learning Model on the iCub Robot [52.277579221741746]
  Addressee Estimation is a skill essential for social robots to interact smoothly with humans. Inspired by human perceptual skills, a deep-learning model for Addressee Estimation is designed, trained, and deployed on an iCub robot. The study presents the procedure of such implementation and the performance of the model deployed in real-time human-robot interaction.
  arXiv  Detail & Related papers  (2023-11-09T13:01:21Z)
• RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation [68.70755196744533]
  RoboGen is a generative robotic agent that automatically learns diverse robotic skills at scale via generative simulation. Our work attempts to extract the extensive and versatile knowledge embedded in large-scale models and transfer them to the field of robotics.
  arXiv  Detail & Related papers  (2023-11-02T17:59:21Z)
• Planning for Learning Object Properties [117.27898922118946]
  We formalize the problem of automatically training a neural network to recognize object properties as a symbolic planning problem. We use planning techniques to produce a strategy for automating the training dataset creation and the learning process. We provide an experimental evaluation in both a simulated and a real environment.
  arXiv  Detail & Related papers  (2023-01-15T09:37:55Z)
• Proactive Robot Assistance via Spatio-Temporal Object Modeling [15.785125079811902]
  Proactive robot assistance enables a robot to anticipate and provide for a user's needs without being explicitly asked. We introduce a generative graph neural network to learn object dynamics from temporal sequences of object arrangements. Our model outperforms the leading baseline in predicting object movement, correctly predicting locations for 11.1% more objects and wrongly predicting locations for 11.5% fewer objects used by the human user.
  arXiv  Detail & Related papers  (2022-11-28T16:20:50Z)
• Machine Learning for Smart and Energy-Efficient Buildings [5.472392992130677]
  Energy consumption in buildings accounts for approximately 40% of all energy usage in the U.S. We review the ways in which machine learning has been leveraged to make buildings smart and energy-efficient.
  arXiv  Detail & Related papers  (2022-11-27T17:04:31Z)
• Power Management in Smart Residential Building with Deep Learning Model for Occupancy Detection by Usage Pattern of Electric Appliances [0.4987480084635553]
  The proposed algorithm achieves occupancy detection using technical information of electric appliances by 95.798.4%. Power consumption with renewable energy system is reduced to 11.113.1% in smart buildings by using occupancy detection.
  arXiv  Detail & Related papers  (2022-09-23T11:02:45Z)
• Zero Shot Learning for Predicting Energy Usage of Buildings in Sustainable Design [2.929237637363991]
  The 2030 Challenge is aimed at making all new buildings and major renovations carbon neutral by 2030. It is important to understand how the various building factors contribute to energy usage of a building, right at design time. Rich training datasets are needed for AI-based solutions to achieve good prediction accuracy.
  arXiv  Detail & Related papers  (2022-02-10T18:08:58Z)
• Skill Preferences: Learning to Extract and Execute Robotic Skills from Human Feedback [82.96694147237113]
  We present Skill Preferences, an algorithm that learns a model over human preferences and uses it to extract human-aligned skills from offline data. We show that SkiP enables a simulated kitchen robot to solve complex multi-step manipulation tasks.
  arXiv  Detail & Related papers  (2021-08-11T18:04:08Z)
• Comfort-as-a-Service: Designing a User-Oriented Thermal Comfort Artifact for Office Buildings [0.0]
  This work aims to optimize individual environmental comfort in open office environments. Based on a Design Science Research approach, we first perform a user experience testing in an exemplary corporate office building. We build a machine learning model including different IoT data sources with an average coefficient of determination of 41.5%.
  arXiv  Detail & Related papers  (2020-03-27T19:06:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.