Room Occupancy Prediction: Exploring the Power of Machine Learning and Temporal Insights

The data are collected based on the experiment from the research [24]. The purpose of the experiment was to estimate occupancy by the signal information from non-intrusive sensors. Seven sensor nodes and one edge node were arranged in the setup, and the sensor nodes used wireless transceivers to send data to the edge every thirty seconds. In this experiment, five distinct kinds of non-intrusive sensors were used: digital passive infrared (PIR), light, sound, temperature, and CO2. Calibration of the PIR, sound, and CO2 sensors required considerable labor. Before the CO2 sensor was used for the first time, zero-point calibration was done by hand. This involved keeping the sensor in a clean environment for more than 20 minutes and then lowering the calibration pin (HD pin) for more than 7 seconds. In essence, the sound sensor is a microphone with an analog amplifier of variable gain attached. Two trimpots are available for the PIR sensor: one for adjusting sensitivity and the other for adjusting how long the output remains high after motion detection. These two were both set to their maximum settings. The temperature, light, and sound sensors were located in sensor nodes S1–S4, a CO2 sensor was located in sensor node S5, and one PIR sensor each was placed on the ceiling ledges of S6 and S7 at an angle that optimized the sensor’s field of view for motion detection. Over the course of four carefully planned days, the number of occupants in each room varied from zero to three. The manual recording of the room’s occupancy count served as the basis for accuracy. The data description is provided in Table 1.

There are all numerical features and totally of 10129 entries. In this study, 30% is selected as the test proportion. The summary statistics of the features are displayed in Table 2 and 3. All features are numerical. In addition, there are no missing values identified in the whole dataset. The features “Date” and “Time” are not considered in the training set. The rationales and discussions are provided in the discussion sector 3. The values of the target variable “Room_Occupancy_Count” are integers and the range is from 0 to 3.

The initial introduction of the prediction framework can be traced back to the study presented in [23], where it was primarily employed for addressing a water quality prediction challenge, constituting a regression problem. In this paper, we extend and adapt the prediction framework to encompass classification problems, and its efficacy is evaluated using room occupancy prediction data.

This section outlines the proposed prediction methodology, detailing its development through hyperparameter optimization, prediction generation, and assessment using specific scoring metrics. The implementation process involves the following steps:

1. 1.

   Selection of Scoring Function: To fine-tune and assess hyperparameters, we employ the Area Under the Receiver Operating Characteristic Curve (AUC-ROC) for all candidate techniques.

2. 2.

   Defining Hyperparameter Space: Each method’s hyperparameter space is defined by specifying the relevant parameters.

3. 3.

   Cross-validation: We utilize cross-validation to perform hyperparameter optimization, aiming to identify the optimal hyperparameters for each method.

4. 4.

   Performance Evaluation: The evaluation metrics are computed on the test dataset for the best-tuned models within each method.

5. 5.

   Results Storage: The outcomes, including the performance of each approach and the significance of its features, are saved for facilitating model comparison.

For single predicted point, The localized contributions to that specific forecast are quantified by the SHAP values [25]. We provide the definition for the SHAP value:

In this equation, $\Phi_{i}(F,x)$ represents the SHAP value corresponding to feature $x_{i}$ within the context of a model $F$ constructed using a set of features $X$. Here, $M$ stands for the total number of input features, $X^{\prime}$ denotes the set of all potential feature combinations that include feature $x_{i}$, and $|Z^{\prime}|$ represents the number of features within a specific feature combination denoted as $Z^{\prime}$. Furthermore, $F(Z^{\prime})$ and $F(Z^{\prime}\backslash x_{i})$ correspond to distinct predictive models trained on $|Z^{\prime}|$ and $Z^{\prime}$ with feature $x_{i}$ removed, respectively. Therefore, the SHAP value is determined by aggregating the marginal contributions $F(Z^{\prime})-F(Z^{\prime}\backslash x_{i})$ from all feasible feature combinations $(Z^{\prime})$ through a weighted average.

SHAP values are calculated using the Python implementation of SHAP, as described by Lundberg and Lee in [25]. In this study, we computed SHAP values for several candidate models.

Figure 2 is the illustration of SHAP values for the random forest model (best model). From the plot, the features “S1_Light” and “S2_Light” have notable impact in forecasting the room occupancy. Their impact are much higher than the other features.

Throughout our study, the dataset has exhibited both temporal and spatial dimensions. Surprisingly, our prediction framework has demonstrated strong performance without explicitly incorporating the temporal dimension.

Our analysis reveals intriguing insights into the temporal aspects of our dataset, shedding light on the potential redundancy and high correlation (illustrated in Figure 3) between certain features. Notably, the autocorrelation results (detailed in Table 6) of our numeric columns provide evidence of the underlying temporal dynamics. For instance, let’s consider the ‘S5_CO2’ column, which pertains to carbon dioxide levels. It exhibits an extraordinarily high autocorrelation coefficient of approximately 0.999. This remarkable finding underscores that the current $CO_{2}$ level is intimately connected with its past values. Essentially, as more individuals enter a room, the ‘S5_CO2’ readings tend to follow a consistent pattern of escalation, while their departure leads to a noticeable decline in the readings. Similarly, the ‘S5_CO2_Slope’ representing the rate of change in $CO_{2}$ levels, also demonstrates a strong positive autocorrelation with a coefficient of about 0.998. This implies that the temporal aspect of our data, encapsulated by the $CO_{2}$ slope, is inherently linked to its past values. As more individuals arrive, the $CO_{2}$ slope tends to exhibit a higher positive value, whereas their departure results in a more pronounced negative slope. Furthermore, it’s intriguing to note that various other sensors, such as temperature and light sensors (‘S1_Temp,’ ‘S2_Temp,’ ‘S3_Light,’ etc.), display substantial autocorrelation coefficients, indicating consistent temporal patterns in their readings over time. This pattern of high autocorrelation coefficients across multiple sensors suggests that our dataset may indeed have inherent redundancy or temporal dependencies.

In essence, our findings support the notion that the temporal and spatial cues within the dataset might be subtly embedded within other features. The machine learning model can adeptly learn and leverage these temporal dynamics to make accurate predictions, emphasizing the richness of information present in our data.

Furthermore, our dataset exhibits a notably high sampling frequency in the temporal dimension, with data points recorded at 30-second intervals. This high-frequency data collection could potentially empower traditional models to implicitly incorporate temporal and spatial information, capitalizing on the fine granularity of the data points.

The time series plots in Figure 4 illustrate the temporal patterns of various sensor data columns. These patterns are crucial in understanding the concept of redundant time information, where certain features exhibit high autocorrelation, indicating a strong linear relationship with their past values.

For instance, consider Figure 4(a), which shows the time series plot for ‘S1_Temp,’ representing the temperature at Sensor 1. The consistently high autocorrelation observed in this plot suggests a significant temporal dependency, implying that the current temperature is closely related to its past values. Similar patterns can be observed in other sensors, such as ‘S2_Temp,’ ‘S3_Temp,’ and ‘S4_Temp.’

These findings emphasize that the dataset contains valuable temporal information that traditional machine learning models can leverage, even when the specific significance of each feature is not explicitly defined. This temporal redundancy plays a vital role in understanding and predicting the behavior of our data.

Another noteworthy observation is that the spatial and temporal patterns within the dataset may follow relatively straightforward and linear relationships. In such cases, traditional machine learning models like logistic regression or decision trees might suffice in capturing these patterns without necessitating specialized spatial or temporal modeling. However, without comprehensive knowledge of the data collection, recording, and sensor aggregation procedures, we may not directly discern these patterns.

In this figure 5, we present a scatter plot depicting the relationship between the ‘S5_CO2_Slope’ and the ‘Room_Occupancy_Count’. Each data point on the plot represents a specific measurement, where the x-axis corresponds to the ‘S5_CO2_Slope’ values, and the y-axis represents the ‘Room_Occupancy_Count’.

Upon visual inspection, it becomes evident that there is a discernible linear increasing trend in the data points. This trend indicates that as the ‘S5_CO2_Slope’ value increases, there is a corresponding rise in the ‘Room_Occupancy_Count’. The linear regression line fitted to the data further reinforces this observation.

The linear regression analysis provides numerical support for the observed trend. The intercept value of approximately 0.4008 indicates the expected ‘Room_Occupancy_Count’ when the ‘S5_CO2_Slope’ is zero. The positive slope of approximately 0.4611 quantifies the rate at which the ‘Room_Occupancy_Count’ increases concerning changes in the ‘S5_CO2_Slope’ This suggests that, on average, for each unit increase in the ‘S5_CO2_Slope’ we can anticipate an increase of approximately 0.4611 in the ‘Room_Occupancy_Count’.

This observation of a linear association between the ‘S5_CO2_Slope’ and the ‘Room_Occupancy_Count’ aligns with the idea that the dataset may exhibit linear spatial and temporal patterns. These linear patterns may be indicative of straightforward cause-and-effect relationships within the data. In cases like these, traditional machine learning models, such as linear regression, can effectively capture these patterns without the need for specialized spatial or temporal modeling techniques.

It’s important to note that while we can observe these linear patterns, a comprehensive understanding of the data collection, recording, and sensor aggregation procedures is essential for precise interpretation. Without such knowledge, we may not be able to fully elucidate the underlying mechanisms driving these patterns.

In summary, the robust performance of traditional machine learning models, even in the absence of explicit consideration for the temporal dimension, can be attributed to the presence of feature redundancy, the simplicity of linear spatial and temporal patterns, and the advantages offered by a high-frequency data sampling approach. While these findings are intriguing, it is essential to remain open to the possibility that explicitly modeling the temporal dimension could unveil deeper insights or enhance predictive capabilities in certain scenarios.

In our investigation of room occupancy prediction, we generalized the framework presented in [23], utilizing various machine learning approaches. Differing from its application in [23], our focus was on room occupancy prediction as a classification problem. Our study demonstrates that this framework is adaptable to classification tasks and yields excellent predictive performance. Notably, among all the methodologies we examined, the random forest model stood out with the best performance.

An interesting observation is that, when compared to the original paper of the data source [24], our predictive performance surpassed the original paper’s results, even without accounting for time dependencies. Our analysis unveiled valuable insights into the temporal aspects of our dataset, highlighting the potential redundancies and significant correlations among certain features. Particularly, the autocorrelation results from our numeric columns provided evidence of underlying temporal dynamics. Furthermore, our dataset boasted a notably high temporal sampling frequency, with data points recorded at 30-second intervals. This high-frequency data collection potentially empowers traditional models to implicitly integrate temporal and spatial information, making the most of the fine granularity of the data points.

Our findings challenge the assumption made in the original paper that incorporating time-dependent and spatial features would invariably enhance predictive model accuracy.

Conversely, we observed limitations in the data collection process outlined in the original paper [24]. In that paper, sensor nodes were deployed at corners, the middle, and the ceiling of the room to predict the number of people in the room based on sensor readings. However, the position of individuals within the room was not considered. A straightforward implication is that by taking into account the positions of people, more accurate sensor data could be obtained, potentially leading to improved prediction performance. For future research, we plan to redesign the data collection process, considering the positions of people. In this context, we may be able to predict not only the presence but also the specific number of individuals in the room, transforming the task from a simple classification problem to a more detailed occupancy estimation.