Occupancy Prediction Model Research Articles

Enabling active visitor management: local, short-term occupancy prediction at a touristic point of interest

After the temporary shock of the Covid-19 pandemic, the rapid recovery and resumed growth of the tourism sectors accelerates unsustainable tourism, resulting in local (over-)crowding, environmental damage, increased emissions, and diminished tourism acceptance. Addressing these challenges requires an active visitor management system at points of interest (POI), which requires local and timely POI-specific occupancy predictions to predict and mitigate crowding. Therefore, we present a new approach to measure visitor movement at an open-spaced, and freely accessible POI and evaluate the prediction performance of multiple occupancy and visitor count machine learning prediction models. We analyze multiple case combinations regarding spatial granularity, time granularity, and prediction time horizons. With an analysis of the SHAP values we determine the influence of the most important features on the prediction and extract transferable knowledge for similar regions lacking visitor movement data. The results underline that POI-specific prediction is achievable with a moderate relation for occupancy prediction and a strong relation for visitor count prediction. Across all cases, XGBoost and Random Forest outperform other models, with prediction accuracy increasing as the prediction time horizon shortens. For effective active visitor management, combining multiple models with different spatial aggregations and prediction time horizons provides the best information basis to identify appropriate steering measures. This innovative application of digital technologies facilitates information exchange between destination management organizations and tourists, promoting sustainable destination development and enhancing tourism experience.

Multimodal Framework for Smart Building Occupancy Detection

Over the years, building appliances have become the major energy consumers to improve indoor air quality and occupants’ lifestyles. The primary energy usage in building sectors, particularly lighting, Heating, Ventilation, and Air conditioning (HVAC) equipment, is expected to double in the upcoming years due to inappropriate control operation activities. Recently, several researchers have provided an automated solution to turn HVAC and lighting on when the space is being occupied and off when the space becomes vacant. Previous studies indicate a lack of publicly accessible datasets for environmental sensing and suggest developing holistic models that detect buildings’ occupancy. Additionally, the reliability of their solutions tends to decrease as the occupancy grows in a building. Therefore, this study proposed a machine learning-based framework for smart building occupancy detection that considered the lighting parameter in addition to the HVAC parameter used in the existing studies. We employed a parametric classifier to ensure a strong correlation between the predicting parameters and the occupancy prediction model. This study uses a machine learning model that combines direct and environmental sensing techniques to obtain high-quality training data. The analysis of the experimental results shows high accuracy, precision, recall, and F1-score of the applied RF model (0.86, 0.99, 1.0, and 0.88 respectively) for occupancy prediction and substantial energy saving.

Transferrable contextual feature clusters for parking occupancy prediction

Recently, real-time parking availability prediction has attracted much attention since the rapid development of sensor technologies and urbanisation. Most existing works have applied various models to predict long and short-term parking occupancy using historical records. However, historical records are not available for many real-world scenarios, such as new urban areas, where parking lots are fast adjusted and extended. In this paper, we aim to predict parking occupancy using historical data in other areas and contextual information within the targeted area that lacks historical data. We propose a two-step framework to first learn the important contextual features from areas where parking records already existed. Then we transfer these features to the other new areas without historical data records. Through conducting a real-world dataset with various clustering methods combined with different regression models, we observe that multiple contextual features are likely to influence parking availability prediction. We find the best combination (i.e., k-shape clustering algorithm and LSTM regression model) to build parking occupancy prediction model based on the subsequent quantitative correlation analysis between contextual features and parking occupancy. The experimental results show that (1) the conventional internal clustering evaluation does not work well for spatio-temporal data clustering for the prediction purpose; (2) our proposed approach achieves approximately 3% error rate in 30 minutes of prediction, which is significantly better than the estimation of the occupancy rate using the rate in the adjacent regions (13.3%).

Examining parking occupancy prediction models: a comparative analysis

Parking is a persistent challenge in urban areas, especially on college campuses, as limited parking spaces and increasing vehicular traffic are at the forefront of campus administrators’ concerns. Lack of parking availability leads to frustration among students, faculty, and staff and results in increased traffic congestion, environmental pollution, and reduced overall productivity. Accurate prediction of parking occupancy plays a vital role in optimizing parking resource allocation, minimizing congestion, and improving the overall campus experience. Thus, this study comprehensively analyzes parking occupancy predictions for a college campus garage, using several different models. The dataset spans from January 2022 to March 2023, providing insights into the models' performance for more than one year. The research aims to utilize random forest, decision tree, linear regression, and support vector regression (SVR) models and compare their effectiveness, using evaluation metrics such as root mean square error (RMSE), mean absolute error (MAE), and R-squared (R2). Random forest outperformed decision tree, SVR and linear regression models in terms of accuracy, as indicated by the lower MAE values. Additionally, random forest achieved a better classification performance, and a higher R2 suggests a stronger correlation between the predicted and actual occupancy values. Conversely, SVR displayed the weakest performance among the four models, with a high MAE and negative R2 value. The findings of this research will aid in improving parking management systems and contribute to the development of efficient and sustainable parking solutions on college campuses and in urban areas.

Towards self-learning control of HVAC systems with the consideration of dynamic occupancy patterns: Application of model-free deep reinforcement learning

This study proposes a self-learning control system that aims to learn occupancy profiles, building energy consumption patterns, and lag-time of the heating, ventilation, and air-conditioning (HVAC) systems. The control system learns by interacting with the environment with no need to develop building models and occupancy prediction models. The controller is developed based on a double deep Q-networks (DDQN) algorithm, as a model-free reinforcement learning method. The system's performance is evaluated and compared with that of a model predictive control (MPC) system under two scenarios of perfect and actual occupancy predictions based on occupancy data collected from 20 residential units. The MPC is assisted by a genetic algorithm and supervised learning models for predicting future occupancy patterns, indoor operative temperature, and building energy consumption. The results show that in the case of using perfect occupancy prediction, the self-learning controller operates almost as well as the MPC while not requiring any models. When occupancy prediction uncertainty is added to the problem, the proposed method outperforms the MPC in terms of thermal comfort by increasing the average temperature deviation and deviation period by 0.24 °C and 7.87%, respectively. However, the DDQN agent causes significant thermal comfort violations during the initial training period. The system causes up to a 2.8% longer deviation period and a 0.32 °C higher average temperature deviation, compared with the performance of the fully-trained system.

Auxiliary-LSTM based floor-level occupancy prediction using Wi-Fi access point logs

Smart city concepts have gained increased traction over the years. The advances in technology such as the Internet of things (IoT) networks and their large-scale implementation has facilitated data collection, which is used to obtain valuable insights towards managing, improving, and planning for services. One key component in this process is the understanding of human mobility behaviour. Traditional data collection methods such as surveys and GPS data have been extensively used to study human mobility. However, a key concern with such data is the protection of user privacy. This study aims to overcome those concerns using Wi-Fi access point logs and demonstrate their utility by creating building occupancy prediction models using advanced machine learning techniques. The floor level occupancy counts and auxiliary variable for a campus building are extracted from the Wi-Fi logs. They are used to develop specifications of Long-Short Term Memory network (LSTM), Auxiliary LSTM (Aux-LSTM), Autoregressive Integrated Moving Average (ARIMA), and Multi-layer Perceptron (MLP) models. The LSTM performed better than the other models and can efficiently capture peak values. Aux-LSTM was shown to increase the reliability in prediction and applicability in the context of facilities management. Results show the effectiveness of the Wi-Fi dataset in capturing trends, providing supplementary information, and highlight the ability of LSTM to adequately model time-series data.

Predicting surgical department occupancy and patient length of stay in a paediatric hospital setting using machine learning: a pilot study

ObjectiveEarly and accurate prediction of hospital surgical-unit occupancy is critical for improving scheduling, staffing and resource planning. Previous studies on occupancy prediction have focused primarily on adult healthcare settings, we...

A review on occupancy prediction through machine learning for enhancing energy efficiency, air quality and thermal comfort in the built environment

The occupants' presence, activities, and behaviour can significantly impact the building's performance and energy efficiency. Currently, heating, ventilation, and air-conditioning (HVAC) systems are often run based on assumed occupancy levels and fixed schedules, or manually set by occupants based on their comfort needs. However, the unpredictability and variability of occupancy patterns can lead to over/under the conditioning of space when using such approaches, affecting indoor air quality and comfort. As a result, machine learning-based models and methodologies are progressively being used to forecast occupancy behaviour and routines in buildings, which may subsequently be used to aid in the design and operation of building systems. The present work reviews recent studies employing machine learning methods to predict occupancy behaviour and patterns, with a special focus on its related applications and benefits to building systems, improving energy efficiency, indoor air quality and thermal comfort. The review provides insight into the workflow of a machine learning-based occupancy prediction model, including data collection, prediction, and validation. An organised evaluation of the applicability or suitability of the different data collection methods, machine learning algorithms, and validation methods was carried out.

Impact of predictor variables on the performance of future occupancy prediction: Feature selection using genetic algorithms and machine learning

This study analyzes the impact of employing different features on the performance of future occupancy prediction models. The aim is to identify the most effective predictor variables for occupancy models in order to enhance the prediction performance. To this end, a multi-objective genetic algorithm (MOGA) is proposed and employed to maximize accuracy while minimizing the number of features utilized in the model. A trade-off between the mentioned objectives is provided by using the technique for order of preference by similarity to ideal solution (TOPSIS). The performance of the MOGA is compared with that of a single-objective genetic algorithm, forward sequential selection, and backward sequential selection methodologies to assess the effectiveness of the proposed method. The results reveal that the MOGA provides superior performance and is able to improve the median accuracy by up to 4.81% from 70.94% for long-term occupancy prediction based on backward sequential feature selection while utilizing fewer features. Based on the TOPSIS method, no more than six features are required for developing the occupancy models with recent occupancy states and day of week selected as the essential features. In some cases, using recent CO2 levels, occupancy duration, lighting states, and previous occupancy states also shows a potential improvement in the prediction performance.

Scalable Room Occupancy Prediction with Deep Transfer Learning Using Indoor Climate Sensor

An important instrument for achieving smart and high-performance buildings is Machine Learning (ML). A lot of research has been done in exploring the ML models for various applications in the built environment such as occupancy prediction. Nevertheless, the research focused mostly on analyzing the feasibility and performance of different supervised ML models but has rarely focused on practical applications and the scalability of those models. In this study, a transfer learning method is proposed as a solution to typical problems in the practical application of ML in buildings. Such problems are scaling a model to a different building, collecting ground truth data necessary for training the supervised model, and assuring the model is robust when conditions change. The practical application examined in this work is a deep learning model used for predicting room occupancy using indoor climate IoT sensors. This work proved that it is possible to significantly reduce the length of ground truth data collection to only two days. The robustness of the transferred model was tested as well, where performance stayed on a similar level if a suitable normalization technique was used. In addition, the proposed methodology was tested with room occupancy level prediction, showing slightly lower performance. Finally, the importance of understanding the performance metrics is crucial for market adoption of ML-based solutions in the built environment. Therefore, in this study, additional analysis was done by presenting the occupancy prediction model performance in understandable ways from the practical perspective.

Impact of occupancy prediction models on building HVAC control system performance: Application of machine learning techniques

This paper analyzes the impact of using different types of occupancy prediction models on the performance of occupancy-based heating, ventilation, and air conditioning (HVAC) control systems in residential buildings. The future occupancy prediction problem is approached from two different viewpoints: 1) as a regression task for arrival-time prediction and 2) as a classification task for occupancy-state prediction. Four machine learning techniques, namely decision trees, k-nearest neighbors (kNN), multi-layer perceptron, and gated recurrent units, are implemented to predict future occupancy from each perspective. The performance of the occupancy models is evaluated in terms of mean absolute error (MAE), accuracy, and the ability to provide occupants’ thermal comfort and to save energy. An overall performance score is proposed by making a trade-off between the energy and thermal comfort objectives using the technique for order of preference by similarity to the ideal solution method. The results demonstrate that selecting the optimal viewpoint has a higher impact on the control performance than selecting the machine learning techniques, with occupancy-state prediction models providing superior performance in most cases. It is also shown that the machine learning evaluation metrics (i.e., MAE and accuracy) provide a weak to moderate correlation with the overall performance score. Consequently, relying solely on the MAE and accuracy might fail to provide a reliable evaluation of the occupancy model performance for use in HVAC control systems.

Assessing the Predictive Value of Traffic Count Data in the Imputation of On-Street Parking Occupancy in Amsterdam

On-street parking policies have a huge impact on the social welfare of citizens. Accurate parking occupancy data across time and space is required to properly set such policies. Different imputation and forecasting models are required to obtain this data in cities that use probe vehicle measurements, such as Amsterdam. In this paper, the usage of traffic data as an explanatory variable is assessed as a potential improvement to existing parking occupancy prediction models. Traffic counts were obtained from 164 traffic cameras throughout the city. Existing models for predicting parking occupancy were reproduced in experiments with and without traffic data, and their performance was compared. Results indicated that (i) traffic data are indeed a useful predictor and improves performance of existing models; (ii) performance does not improve linearly with an increase in the number of counting points; and (iii) placement of the cameras does not have a significant impact on performance.

Value of incorporating geospatial information into the prediction of on-street parking occupancy – A case study

ABSTRACT In light of growing urban traffic, car parking becomes increasingly critical for cities to manage. As a result, the prediction of parking occupancy has sparked significant research interest in recent years. While many external data sources have been considered in the prediction models, the underlying geographic context has mostly been ignored. Thus, in order to study the contribution of geospatial information to parking occupancy prediction models, road network centrality, land use, and Point of Interest (POI) data were incorporated in Random Forest (RF) and Artificial Neural Network (ANN, specifically Feedforward Neural Network FFNN) prediction models in this work. Model performances were compared to a baseline, which only considers historical and temporal input data. Moreover, the influence of the amount of training data, the prediction horizon, and the spatial variation of the prediction were explored. The results show that the inclusion of geospatial information led to a performance improvement of up to 25% compared to the baseline. Besides, as the prediction horizon expanded, predictions became less reliable, while the relevance of geospatial data increased. In general, land use and POI data proved to be more beneficial than road network centrality. The amount of training data did not have a significant influence on the performance of the RF model. The ANN model, conversely, achieved optimal results on a training input of 5 days. Likely attributable to varying occupancy patterns, prediction performance disparities could be identified for different parking districts and street segments. Generally, the RF model outperformed the ANN model on all predictions.

Season-Based Occupancy Prediction in Residential Buildings Using Machine Learning Models

• Data mining-based framework to predict occupancy accounting variation corresponding to different seasons is proposed. • Prediction performances of six machine learning algorithms are studied in terms of accuracy and computational time. • The recursive feature elimination with cross-validation technique is used for selecting optimal inputs. • The developed framework is generic and can be extended to a wide range of residential buildings. A reliable occupancy prediction model plays a critical role in improving the performance of energy simulation and occupant-centric building operations. In general, occupancy and occupant activities differ by season, and it is important to account for the dynamic nature of occupancy in simulations and to propose energy-efficient strategies. The present work aims to develop a data mining-based framework, including feature selection and the establishment of seasonal-customized occupancy prediction (SCOP) models to predict the occupancy in buildings considering different seasons. In the proposed framework, the recursive feature elimination with cross-validation (RFECV) feature selection was first implemented to select the optimal variables concerning the highest prediction accuracy. Later, six machine learning (ML) algorithms were considered to establish four SCOP models to predict occupancy presence, and their prediction performances were compared in terms of prediction accuracy and computational cost. To evaluate the effectiveness of the developed data mining framework, it was applied to an apartment in Lyon, France. The results show that the RFECV process reduced the computational time while improving the ML models’ prediction performances. Additionally, the SCOP models could achieve higher prediction accuracy than the conventional prediction model measured by performance evaluation metrics of F-1 score and area under the curve. Among the considered ML models, the gradient-boosting decision tree, random forest, and artificial neural network showed better performances, achieving more than 85% accuracy in Summer, Fall, and Winter, and over 80% in Spring. The essence of the framework is valuable for developing strategies for building energy consumption estimation and higher-resolution occupancy level prediction, which are easily influenced by seasons.

Development and evaluation of occupancy-aware model predictive control for residential building energy efficiency and occupant comfort

The residential sector accounts for 25% of global primary energy consumption. Two methods have previously been proposed to reduce residential energy use associated with the provision of occupant thermal comfort: 1. Occupancy-based HVAC control, operating systems only during confirmed occupancy, and 2. model predictive control (MPC), harnessing a mathematical model and forecasts to find optimal operating strategies. Previous studies estimate the average energy savings of the two methods individually in the range of 21% and 16%, respectively. The research presented herein was carried out to evaluate the energy savings potential in residential buildings by combining both approaches across different climates, house vintages, and occupancy patterns. Occupancy and eight different physical modalities (e.g. CO2 and VOC) data were collected from five homes for time periods of 4–9 weeks. Collected data sets were used to train occupancy prediction models suggested by an extensive literature survey of occupancy model types. The trained prediction models were combined with MPC and detailed EnergyPlus building simulation models to evaluate residential building performance in terms of annual energy savings and thermal comfort, along with discomfort exceedance metrics. Multiple home types and regions were analyzed to understand regional and climate-dependent potential. Based on actual field data, the occupancy models had a prediction inaccuracy between 8% and 35% across the investigated homes. Average occupancy for the collected data ranged from 56% to 86%, a typical range reported in the literature. Building simulations were conducted for three control scenarios: conventional thermostatic control, occupancy-based, and occupancy-based MPC. The results indicate that all advanced strategies improve upon the conventional control, with some scenarios cutting energy use in half with only occasional incurrence of discomfort. The findings indicate that occupancy-aware model predictive residential building control has the potential to drastically reduce energy use and associated emissions while maintaining occupant comfort for both new and existing buildings.

Development and Evaluation of Occupancy-Aware HVAC Control for Residential Building Energy Efficiency and Occupant Comfort

Occupancy-aware heating, ventilation, and air conditioning (HVAC) control offers the opportunity to reduce energy use without sacrificing thermal comfort. Residential HVAC systems often use manually-adjusted or constant setpoint temperatures, which heat and cool the house regardless of whether it is needed. By incorporating occupancy-awareness into HVAC control, heating and cooling can be used for only those time periods it is needed. Yet, bringing this technology to fruition is dependent on accurately predicting occupancy. Non-probabilistic prediction models offer an opportunity to use collected occupancy data to predict future occupancy profiles. Smart devices, such as a connected thermostat, which already include occupancy sensors, can be used to provide a continually growing collection of data that can then be harnessed for short-term occupancy prediction by compiling and creating a binary occupancy prediction. Real occupancy data from six homes located in Colorado is analyzed and investigated using this occupancy prediction model. Results show that non-probabilistic occupancy models in combination with occupancy sensors can be combined to provide a hybrid HVAC control with savings on average of 5.0% and without degradation of thermal comfort. Model predictive control provides further opportunities, with the ability to adjust the relative importance between thermal comfort and energy savings to achieve savings between 1% and 13.3% depending on the relative weighting between thermal comfort and energy savings. In all cases, occupancy prediction allows the opportunity for a more intelligent and optimized strategy to residential HVAC control.

An occupancy prediction model for campus buildings based on the diversity of occupancy patterns

The complexity and randomness of occupancy often lead to the deviation between simulated energy and measured energy. It is of great significance to select appropriate approaches for occupancy prediction. This study applied Gaussian distribution model to fit occupancy of three functional buildings within a campus. Abandoned the average occupancy level widely adopted in exiting researches, a novel classified approach considering the diversity of occupancy patterns was proposed. The resulting Gaussian curves presented a better fitting performance for stable changes of occupancy. Although the sudden increase and decrease of occupancy greatly affects the prediction accuracy, the occupancy prediction error based on Gaussian distribution can still be controlled within ±15 %. The energy of the case building was obtained by superposing simulated energy of each occupancy pattern. Rather than traditional method of averaging, the classified methodology eliminated the simulation errors produced from the inhomogeneous and stochastic occupancy. The exploration of changing regularity of energy affected by occupancy and time periods with energy saving potential were achieved by integrating energy data with occupancy data. And the detection of the degree and occurrence times of peak occupancy of various occupancy patterns of rooms provided a support for rational load distribution.

Occupancy Prediction Using Differential Evolution Online Sequential Extreme Learning Machine Model

Despite increasing energy efficiency requirements, the full potential of energy efficiency is still unlocked; many buildings in the EU tend to consume more energy than predicted. Gathering data and developing models to predict occupants’ behaviour is seen as the next frontier in sustainable design. Measurements in the analysed open-space office showed accordingly 3.5 and 2.7 times lower occupancy compared to the ones given by DesignBuilder’s and EN 16798-1. This proves that proposed occupancy patterns are only suitable for typical open-space offices. The results of the previous studies and proposed occupancy prediction models have limited applications and limited accuracies. In this paper, the hybrid differential evolution online sequential extreme learning machine (DE-OSELM) model was applied for building occupants’ presence prediction in open-space office. The model was not previously applied in this area of research. It was found that prediction using experimentally gained indoor and outdoor parameters for the whole analysed period resulted in a correlation coefficient R2 = 0.72. The best correlation was found with indoor CO2 concentration—R2 = 0.71 for the analysed period. It was concluded that a 4 week measurement period was sufficient for the prediction of the building’s occupancy and that DE-OSELM is a fast and reliable model suitable for this purpose.

Sensitivity analysis of probabilistic occupancy prediction model using big data

The sustainable development of the building sector relies on having more accurate data pertinent to the different energy aspects of building design and operation phases. One of the accepted methods for estimating building energy consumption is simulation, which requires building parameters and occupancy information as inputs to model the energy performance of buildings. Building energy simulation tools are mature in terms of incorporating proper building parameters in the energy analysis. Some shortcomings are, however, observed regarding occupancy data, which cause large discrepancies in the energy usage even between similar buildings with the same characteristics. In order to improve the performance of energy simulation models, the sources of errors regarding the occupancy input data should be investigated. To this aim, the sensitivity of the occupancy prediction models, which are widely used to represent the occupancy information in energy models, to their input occupancy data needs to be evaluated. Occupancy prediction models exploit real data pertinent to the occupants’ locations and behavior to predict the probability of an event and generate the occupancy probabilistic profiles. The data collection period and the resolution level used to analyze the collected data are two crucial factors for developing accurate occupancy prediction models. This study aims to perform a comprehensive sensitivity analysis on these parameters affecting the performance of probabilistic occupancy prediction models. The outcomes of this research are the optimum settings of occupancy prediction models, which result in the generation of the most reliable occupancy information.

Real-time occupancy prediction in a large exhibition hall using deep learning approach

Intelligent control systems for optimizing the energy management of ordinary buildings and houses have been commonly studied for decades, but the development of such management systems has not been studied much in large exhibition halls. While occupancy prediction is considered as a key element of such intelligent control systems, it is not easy in a large exhibition hall due to its spatial volume and irregular movements of visitors. In this paper, we propose spatial partitioning of the hall and an occupancy prediction model based on recurrent neural network (RNN) with long short-term memory units (LSTM) to solve the mentioned problems. We test the feasibility of our RNN approaches to predict short-term and long-term occupancy using the sequence patterns for hall occupancy changes in separated multiple zones until a current time point. We demonstrate that the proposed RNN model achieves superior performance by comparing with other prediction models. Then we apply our software toolset for predicting real-time occupancy in actual exhibition events in a large exhibition hall. Our prediction software pipeline is integrated into energy management systems in the exhibition hall.