Integrating Smart Meter Load Profiling for Enhanced Data Insights and Patient Care

Abstract

The utilization of smart meter data has opened new frontiers in the healthcare sector by enabling the detection of health status and daily life activities. A new methodology has been proposed to differentiate between normal and abnormal energy consumption, which can be leveraged to determine the health status of consumers by analyzing load profiling. By visualizing human activities based on electricity usage, there is a growing interest in using smart meter data for detecting health status and Activities of Daily Living (ADLs). The chapter discussed, this innovative approach utilizes the K-Means algorithm to classify the data and identify patterns in the energy usage of individuals. By analyzing this data, potential health issues can be indicated, thereby assisting the healthcare sector in addressing the needs of the elderly population more effectively. This technology has the potential to revolutionize healthcare by providing a non-invasive and cost-effective way to monitor the health of individuals and enable early intervention in case of any health issues.

1. Introduction

The escalating population of elderly individuals and the accompanying surge in healthcare needs necessitate innovative approaches to address the challenges faced by the healthcare sector. Structural health-monitoring systems offer a potential solution by utilizing data from smart meters, which provide granular consumption details. By analyzing electricity usage patterns, these systems can offer insights into the health status of individuals. This chapter presents a methodology to determine abnormal behavior in electricity consumption significantly related to potential health issues. As the global population of elderly individuals rapidly grows, the healthcare sector faces unprecedented challenges in meeting the increasing demand. A structural health-monitoring system that leverages smart meter data to analyze consumers’ electricity usage patterns can provide valuable insights into supporting the healthcare sector. This chapter explores the application of load profiling to differentiate normal and abnormal energy consumption, aiming to determine the health status of consumers.

The smart grid differs from the traditional grid due to its inclusion of peripherals for signal processing, hardware and software blocks of communications standard and management. The concept behind the smart grid involves the use of embedded computers to monitor energy generation and transmission, with the smart meter serving as a crucial peripheral device.

The integration of information technology in the smart grid has revolutionized the communication of electricity from power plants to end users. The smart meter plays a vital role in addressing the challenges associated with the rapid increase in electricity demand, while also supporting additional functionalities.

The application of the smart grid extends beyond the realm of energy management, with potential benefits in areas such as Ambient Assisted Living (AAL), healthcare, agriculture, and education. When used for health monitoring, smart grid technology can offer distinct advantages over traditional wearable devices. The smart meter serves as a key device in this context, collecting valuable information that enables elderly individuals or patients requiring special care to remain comfortably in their own homes, while receiving real-time assistance from caregivers and doctors.

The healthcare industry has been actively exploring the use of smart meter data to detect any changes in an individual’s activities of daily living (ADLs) for healthcare applications. ADLs, which are considered to be fundamental self-care tasks, are necessary for an individual to live independently and maintain their overall healthcare and well-being. These tasks include bathing, dressing, grooming, toileting, eating, and mobility, all of which are crucial to ensuring an individual’s health and independence. By closely monitoring changes in an individual’s ADLs over time, healthcare providers can identify any declines in mobility or independence and take proactive measures to prevent further functional decline. This approach can be particularly useful for older adults, people with disabilities, and individuals recovering from illnesses or injuries that may affect their daily activities. By leveraging advanced technology such as smart meters, healthcare providers can monitor and analyze changes in ADLs and provide personalized care plans to ensure the best possible outcomes for their patients. This way, healthcare providers can improve the quality of life for their patients and help them maintain their independence, even when facing health challenges.

This chapter aims to analyze the application of smart meters within a cost-effective smart grid system that can monitor end user energy management by predicting behavioral patterns based on consumer habits and lifestyle. The overall objective of this approach is to reduce costs through energy savings and load proofing, while also monitoring the health status of consumers within their homes. The contributions of this work include: (1) a discussion of the principles of the smart grid and the new health monitoring options provided by smart grid technology features, including the integration of smart meter load profiling for monitoring patient vital signs at home; (2) an investigation of electricity usage in homes, incorporating information on usage duration and energy consumption to analyze and monitor normal and abnormal behavior; and (3) the development of a data mining model for load profiling energy usage, enabling the characterization of normal and abnormal consumer behavior.

This work is an extension of our previous paper [1], and this article is organized into several sections for a clear and organized presentation of information. The second section, labeled as 2, will delve into a discussion of related work, providing context for the research presented in the paper. In the third section, labeled as 3, the background information related to the Smart meter will be described in detail, outlining the underlying principles and technologies used. The fourth section, labeled as 4, will present a proposed load profiling method, which will be discussed in connection with both normal and abnormal behavioral characterization. Additionally, this section will include an in-depth analysis of the results obtained from this method. Finally, the paper will conclude with a discussion of future directions for research in Section 5, providing insights into where the field may be headed and what areas of investigation may be most fruitful in the coming years.

2. Related work

The theoretical background of this text is focused on the use of smart meters for load profiling in an e-health monitoring system. The text discusses how the data collected from smart meters can be used to support the healthcare sector by analyzing energy consumption patterns. The data analysis involves grouping the data, reducing it using matrix-based analysis, and using the K-Means algorithm for data mining clustering. The goal is to determine normal or abnormal behavior in electricity usage and relate it to the consumer’s health status. The text also mentions the use of smart grids and smart meters in the healthcare, agriculture, and education sectors. Our resource, highlights related work on smart grids, load monitoring, and smart meter data analysis.

The study presents several key characteristics of smart grids that enable active consumer participation in marketing and service provision [2]. The researchers have examined and summarized the automation, monitoring, and remote-control capabilities of smart grids and smart meters [3]. The implementation of an Internet of Things (IoT) based smart grid system allows for communication control commands to be sent with scheduling responses, resulting in reduced response times and real-time monitoring [4].

The research emphasizes the use of load monitoring to assess the activity of patients in elderly homes. Smart meter data collection of daily living activities offers a cost-effective solution [5]. Specifically, this paper proposes an Early Intervention Practices (EIP) method that utilizes smart meter data to enable detailed real-time monitoring of energy consumption and user interactions with electrical devices [6]. The approach involves connecting the remote patient monitoring system directly to the patient’s smart meter and analyzing the collected data to identify any behavioral anomalies using machine learning techniques.

Another work by the same author focuses on profiling users in the smart grid. By implementing smart meters, real-time energy usage of customers can be obtained [7]. The proposal involves analyzing smart meter data and using classification techniques to identify irregular energy usage patterns. In another paper, the same author discusses the monitoring of consumer electricity usage with high accuracy. Each device records consumer data at regular intervals, allowing for the identification and profiling of routines and habits [8]. Sudden abnormal changes in behavior can be detected based on the analysis of regular activities performed by consumers throughout the day. Advanced metering infrastructure is utilized to monitor electricity usage and distinguish between normal and abnormal usage [9].

The application of smart meters is also beneficial for identifying emerging abnormal behaviors and trends in individuals living alone with various health conditions. Load profiling and detection methods are employed to identify sudden changes in behavior related to illnesses such as depression and Alzheimer’s [9].

Smart meters have the ability to detect behavioral changes and determine if a house is vacant or not [10]. Authors in this context utilize Non-Intrusive Load Monitoring (NILM) to disaggregate a household’s aggregated energy consumption into individual appliances [11].

The main research question in this study is how to use smart meter data to support the healthcare sector by load profiling normal or abnormal energy consumption. The hypothesis is that clustering the data using the K-Means algorithm can identify normal and abnormal behavior of electricity usage, which can be indicative of the consumer’s health status.

Our proposed solution addresses the challenges faced in previous studies by presenting a suitable method for distinguishing patterns in consumer energy usage. This paper introduces a monitoring technique for individuals with health limitations, allowing them to live independently in their own homes for a longer period based on their individual needs and conditions. The proposal involves analyzing energy usage, smart meter data, and employing data mining, clustering, and classification techniques to identify normal and abnormal energy consumption.

3. Research method

The objective of this study is to explore the potential application of smart meters in load profiling and its impact on consumer behavior. The data obtained from smart meters is analyzed using disaggregation algorithms and machine learning tools to identify individual usage patterns. These patterns are then used to analyze and understand normal and abnormal household energy consumption patterns.

Figure 1 illustrates the overall architecture of our proposed load profiling system for e-health monitoring, which is based on the principles of smart meters and power consumption measurements on the smart grid. The system utilizes cloud infrastructures to provide comprehensive monitoring services and is capable of classifying normal and abnormal behaviors. This figure depicts the proposed architecture for smart meter load profiling.

Figure 1.

The proposed smart meter load profiling architecture.

A smart meter is a sophisticated tool used in smart grids for data collection and measuring electricity usage. It is equipped with features that enable real-time collection of energy consumption data from various households and appliances.

Figure 2 presents the key components involved in the analysis of smart meter data for e-health monitoring.

Figure 2.

Smart meter application for e-health monitoring.

The methodology used in this research involves analyzing the electricity consumption data collected from smart meters in 12 households. The data, collected at one-hour intervals for one month, is grouped and reduced using matrix-based analysis. The K-Means algorithm is then applied to classify the data and determine normal or abnormal energy consumption patterns. The Sum Square Error (SSE) is used to assess the clustering results. The goal of this methodology is to support the healthcare sector by profiling the energy consumption patterns and detecting any abnormal behavior that may indicate changes in the health status of the consumers.

To evaluate the proposed methodology, a measured dataset from 12 households was collected using smart meters. The data spans a month and is sampled every hour. The dataset is processed by grouping it according to characteristic patterns, and then reduced using matrix-based analysis. The K-Means algorithm, a data mining clustering technique, is employed to classify the data.

4. Smart meter data analysis

In order to establish the feasibility of our study, we obtained energy consumption data from smart meters installed in 13 houses that rely on Swedish Energy for their power supply. The power consumption levels were recorded at 60-minute intervals over a one-month period in October 2017. However, one of the houses had to be excluded from the analysis due to insufficient data availability. Consequently, we were left with a total of 8940 smart meter datasets from 12 houses, which were utilized to identify patterns of normal and abnormal electricity usage. Each house featured heating and cooling systems, lighting, and a variety of similar appliances.

Upon examining the weekly and daily load, as depicted in Figures 3 and 4 provide, we observed that, for all 12 houses, the peak energy consumption occurred in the morning and evening on a daily basis throughout the entire month. However, it is worth noting that there were five houses that exhibited distinct trends in either low or high energy consumption levels over the course of the month, suggesting the presence of abnormal usage patterns.

Figure 3.

Weekly electricity load consumption for visualization.

Figure 4.

The 24-hour power consumption of all the houses.

Figure 1 illustrates the correlation between behavioral change and energy consumption. To observe variations in electricity usage among households, the mean and standardization of power consumption measurements were calculated. Additionally, Figures 5 and 3 provide a clear visualization of the peak loads for each house on a daily and weekly basis.

Figure 5.

The corresponding trend in behavioral change due to energy consumption.

4.1 Load profiling in relation to household behavior

Based on the analysis of the collected data, abnormal energy usage patterns have implications for the user’s health. We propose that these patterns can be categorized into various behavioral health conditions, as shown in Table 1.

Category	Energy usage category	Abnormal indication assumption
A	High energy during late hours (night and evening)	Related to (Alzheimer)
B	Low Energy in the morning	Sick or depressed (lone hours in bed)

Table 1.

Energy usage and behavioral category.

The relationship between energy usage patterns and health events demonstrates a trend where changes in health lead to changes in energy consumption. This process of behavioral change is depicted in Figure 5.

4.2 Data analysis

The energy usage during daily activities in the 12 houses was measured over a 24-hour period. The trend of peak loads remains consistent throughout the month. The figure allows for easy identification and visualization of periods when consumers are sleeping, at home, or when no one is home. Most houses exhibit an increase in energy consumption at specific times.

The mean and the standardization of measurements of the power consumption is calculated in order to observe the different home usage of electricity.

The weekly and a daily electricity load can clearly also lead to visualize the peaks load of each house for each day separately as shown in Figures 3 and 4.

The process of analyzing smart meter data for power consumption in 12 houses involves several steps. Firstly, the load pattern data of each household is measured under specific loading conditions. Secondly, representative features are selected from the data. Thirdly, the load pattern data is processed to create a matrix input for the specified loading condition. Finally, clusters are formed using a selected algorithm, considering the composition of household classes and time-domain data. Clustering validity indicators are then computed to evaluate the effectiveness of the clustering.

The accuracy of load profiling relies on the data collection module, the description of time stamps, and the measurement values. Signal processing and machine learning algorithms are employed to profile and characterize the measured data.

In terms of features, smart grids have the capacity to generate extensive datasets through smart meter measurements. However, visualization techniques may not always provide a straightforward means of distinguishing between different types of energy usage behavior. Therefore, the first step in dataset analysis is to extract the feature patterns and classify them using selected classification algorithms [12].

4.3 Features

The features extracted from smart meter data can be categorized into four groups based on feature selection theory described in [13]. Firstly, there are features related to maximum or minimum energy consumption, which depend on the energy usage at different times such as day or night, weekdays or weekends, etc. Secondly, there are features that represent the ratios between maximum or minimum energy consumption values. Thirdly, there are features that capture the statistical variability of power consumption, including measures such as standard deviation, sum of absolute differences, variance, auto-correlation, and other statistical values within the range of maximum and minimum. Lastly, there are temporal features of power consumption that resemble patterns, including consumption levels, peaks, and significant moments. These temporal features can indicate household activity or inactivity in terms of electricity usage.

The data in question pertains to the first group, which has seven features per consumer, resulting in a total of 84 features. These 84 features are used to identify the maximum and minimum energy consumption of each household. Additionally, the cross-correlations of the pattern data can be used to detect whether the energy consumption of a household is related to others by comparing the average energy consumption of all the houses.

There is a total of 12 houses, each with 744 instant measurements. Among these, eight have normal measurement readings, while four have abnormal readings. Further, two of the houses have low energy consumption, while two have excessive energy consumption. This excessive consumption can be indicative of a consumer’s health status.

To distinguish between normal and abnormal electricity usage, one of the most effective visualization methods is through representation. In this case, the original Electricity Load can be visualized using a Matrix M, where I = 744 and N = 12. The Matrix M represents the total instant measurements for each house, allowing for a clear and detailed visual representation of electricity usage patterns.

$$M=\left|\begin{array}{ccc}{A}_{11}& A_{12}& \dots \dots \dots \dots \dots \dots \dots .A_{112}\\ :& :& :\\ A_{7441}& A_{7442}& \dots \dots \dots \dots \dots \dots \dots .A_{74412}\end{array}\right|$$E1

The Matrix M744 denotes the power consumption of house number 12 over the course of 744 measurements throughout the month. The matrix consists of 744 rows, representing the attributes, and 12 columns, representing the instances. The data in matrix M is transposed, with rows indicating instances and columns indicating attributes.

$$M^{\prime }=\left|\begin{array}{ccc}{A}_{11}& A_{12}& \dots \dots \dots \dots \dots \dots \dots .A_{1744}\\ :& :& :\\ A_{121}& A_{122}& \dots \dots \dots \dots \dots \dots \dots .A_{12744}\end{array}\right|$$E2

Dimension reduction is performed on the dataset due to the excessive number of attributes. The process involves the following steps:

Firstly, we select the duration of time for one month, specifically October 2017, and consider the electricity consumption of 12 instances (houses).

Secondly, we aggregate the electricity needs for each group time of the day throughout the entire month. This aggregation is carried out by summing the electricity consumption data.

It is important to note that the dataset’s information is recorded in Swedish time, and the electricity consumption of the houses is categorized into five time periods during the day, as indicated in Table 1.

As a result of the dimension reduction, each house (instance) is represented by five attributes, which are presented in the following matrix representations:

$$M{\mathit{new}}_{\mathit{\text{reduced}}}=\left|\begin{array}{ccc}{x}_{11}& A_{12}& A_{13}{A}_{14}\\ :& :& :\\ A_{121}& A_{122}& A_{123}{A}_{124}\end{array}\right|$$E3

$$M{\mathit{new}}_{\mathit{\text{reduced}}}\mathit{\text{datasetisusedvisualization}}\wedge \mathit{\text{clustering}}$$E4

5. Results and discussions

5.1 Results

The example dataset resulting from the dimension reduction process is shown in Table 2.

House	Night	Morning	Lunch	Late Night	Evening
#1	31	58,22	62,66	71,66	108,81
#2	20,31	59,44	46,18	46,68	87,6
#3	30,04	43,5	45,27	61,63	80,75
:	:	:	:	:	:
:	:	:	:	:	:
#12	38,6	69,21	81,14	83,18	113,03

Table 2.

The reduced dataset of the 12 houses power consumptions according to the time using $\mathrm{M}{\mathrm{new}}_{\text{reduced}}$.

The following information depicts Table 2, which contains an unprocessed dataset of households along with their unique identification numbers and their respective electricity consumption during various time zones. This dataset outlines the quantitative data for households’ electricity usage, which can be analyzed and interpreted for research or analytical purposes.

5.2 Data visualization, figures representation

The graph depicted in Figure 6 illustrates the relationship between household and electricity consumption in kW/h. The x-axis represents the households and the y-axis represents the electricity consumption. Upon analyzing the graph, it can be observed that the majority of households consume less than or equal to 100 kW/h of electricity throughout the day. However, there are a few exceptions to this trend. House #7 and House #8 have significantly low power consumption, whereas House #1 and House #12 have a higher electricity consumption rate. These variations in consumption patterns can be correlated with the abnormal health conditions mentioned in Table 1. Additionally, there seems to be a trend in behavioral change due to energy consumption on the smart grid, which is contributing to the consumption patterns observed in the graph.

Figure 6.

The comparison graph of the total electricity consumption between the five-time periods during the day.

In order to verify the similarity in specific time and power consumption between households, it is recommended to use a data mining classification method. This method is effective in identifying the similarities and differences between different households based on their energy consumption patterns. Specifically, the K-Means algorithm can be employed to cluster the dataset into different classifications, which can help to identify households with similar energy consumption patterns.

By analyzing the consumption patterns, it is possible to identify households that consume either more or less electricity than a normal household would consume. This abnormal usage can be indicative of various issues, such as faulty electrical appliances or inefficient energy usage. Therefore, by identifying these households, it may be possible to take corrective actions to improve energy efficiency and reduce energy consumption.

Overall, the use of data mining classification methods and the K-Means algorithm can be an effective way to identify abnormal energy consumption patterns in households, which can help to improve energy efficiency and reduce energy consumption. To verify the similarity in specific time and power consumption between households, a data mining classification method should be used. The K-Means algorithm can be employed to cluster the dataset into different classifications. The analysis of the consumption pattern suggests that some homes consume more or less electricity than a normal household, which indicates abnormal usage.

5.3 K-means algorithm

The k Nearest Neighbor (KNN) algorithm is a supervised classifier that is widely used for clustering and classification of large datasets. One of the most popular clustering algorithms is the k-Means algorithm, which is preferred due to its simplicity of implementation and frequent application in fields similar to electricity consumption datasets. It has been listed as one of the top 10 data mining algorithms [14] and is used to group data based on the similarity of data characteristics.

To obtain the cluster label of the data, the main parameter is to compute the distance of the data from the neighborhood data, compare the distance, assign the clusters and visualize the cluster points. This is done using different distance calculation methods, such as the Euclidean distance calculation method. The user defines the K-Means method to classify the given dataset into K clusters, where K is the number of clusters. The clustering algorithm minimizes the sum of squared distances between all points and the cluster center. The calculation and approach to determine the k function clustering have the following steps as indicated in [15].

$$D\left(x,y\right)=\sum _{i=1}^n\left(\mathit{xi}+\mathit{yi}\right)$$E5

The clustering algorithm uses the input dataset and computes the distance to cluster centers (usually Euclidean distance) between an instance and all instances of the training set in the matrix. By comparing the distance between the data points, the algorithm assigns each data point to its nearest cluster center. This process is repeated until the algorithm converges to a stable solution. The resulting clusters are then visualized to gain insights into the data. Overall, the k-Means algorithm is a powerful tool for clustering and classification of large datasets, and it has been widely used in various fields for its simplicity and effectiveness.

The following text describes the process of clustering data using the agglomerative algorithm. This algorithm starts with n clusters, where each cluster contains a single sample or point. It then merges two clusters that have the closest similarity between them, and this process is continued until the number of clusters becomes 1 or is specified by the user.

To determine the algorithm for n samples, the k-nearest neighbors are distinguished. The datasets are used to obtain clusters, and the two closest clusters are merged. The new cluster center is computed, and the distance is again calculated. This process is repeated until all the cases have been merged into one. The sum square error (SSE), which describes the standard deviation of each cluster to the data center, is calculated using the formula below during the clustering process:

$$\mathrm{SSE}=\sum {\left(\mathrm{xi}–\mathrm{x}̅\right)}^2$$E6

Here, xi represents the data point and x̅ represents the mean of the data points.

The input data for this clustering process includes the measurements of all 12 houses. The process segments the data from one to five clusters to measure the SSE and the number of iterations in each. The largest SSE is obtained when clustering with 1 cluster (all the members of the house are included). However, the SSE has better performance with 5 clusters (the members of the houses are classified according to k-Means with 5 clusters). The SEE result is shown in Table 3.

clusters	1	2	3	4	5
SSE	13.352	10.827	6.801	4.225	2.462

Table 3.

Results from the clustering.

Table 4 shows the results of the 5 clusters. In the normal case, electricity usage is highest during the evening. However, the analysis from the SSE result indicates that, for some members of the cluster, the lowest power consumption is during the night.

Cluster number	Number of cluster members	Average Night (kw/H)	Average morning (kw/H)	Average lunch (kw/H)	Average Late Night (kw/H)	Average Evening (kw/H)
1	18.34	18.34	65.52	38.01	44.35	75.77
2	21.22	21.22	31.80	21.71	30.56	43.41
3	25.04	25.04	68.08	41.78	57.43	69.74
4	22.81	22.81	61.15	39.34	42.19	85.04
5	33.21	33.21	56.98	63.02	72.16	93.58

Table 4.

Clustering result for 5 clusters.

There are also abnormality indications for the member of the cluster that has abnormal power consumption during the 24 hours of the day.

Figure 7 and Table 4 show the clustering results that identify the houses with very high and very low electricity usage, including the corresponding 24-hour power consumption measurements.

Figure 7.

Comparison of SSE for each segment of houses.

Initially, it was expected that the feature set 4 households have abnormalities in power consumption compared with the others. However, the analysis of the k-Means clustering algorithm demonstrates that the prediction on the pattern clusters of the households can detect abnormal energy usage behavior. The result of the status of each house is described as follows:

The houses with high power consumption are household #1 and household #12. These conditions remain true even during the evening and late-night hours.

The houses with low power consumption are household #7 and household #8. These conditions also remain true even during the morning (wakeup time), lunch, and most of the day.

5.4 Discussions

Technology has greatly influenced human civilization throughout history, from the invention of tools to the creation of the internet. It has brought numerous benefits and advancements but also raised concerns and challenges that need to be addressed. The internet and digital communication have transformed the way we connect and interact with one another, revolutionized the workplace, and created new opportunities. However, the rapid pace of technological advancement has raised concerns about the digital divide, privacy, and data security.

As the population of elderly individuals continues to grow, the healthcare sector is facing unprecedented challenges in meeting the increasing demand for healthcare services. To address these challenges, a structural health monitoring system that uses smart meter data to analyze consumers’ electricity usage patterns has been proposed. This system can provide valuable insights into supporting the healthcare sector by analyzing the electricity usage patterns of elderly individuals.

This study explores the use of load profiling to identify abnormal energy consumption and potential health issues. Data was collected from 12 households using smart meters and analyzed using the K-Means clustering algorithm. The results showed a clear connection between abnormal energy usage and potential health issues, which can assist the healthcare sector in addressing the needs of the elderly population more efficiently. Overall, this article presents a novel approach for utilizing structural health monitoring systems to support the healthcare sector for ADL in meeting the needs of the rapidly growing elderly population.

1. Research the global population of elderly individuals and the challenges faced by the healthcare sector in meeting their increasing demand.

2. Learn about structural health monitoring systems and their potential to leverage smart meter data for analyzing electricity usage patterns.

3. Understand the concept of load profiling and how it can differentiate normal and abnormal energy consumption.

4. Study the methodology presented in the paper for evaluating abnormal behavior in electricity consumption significantly related to potential health issues.

5. Analyze the measured dataset from 12 households collected using smart meters and understand how it was processed and reduced using matrix-based analysis.

6. Learn about the K-Means algorithm, a data mining clustering technique, and how it was employed to classify the data.

7. Study the clustering results and their connection with the normal or abnormal behavior of electricity usage, which enables the determination of potential health issues for the consumer.

8. Understand how the clustering results can be compared to known health conditions to assume the consumer’s health status and AAL in the category of ADL

9. Evaluate the novel approach presented in the paper for utilizing structural health monitoring systems to support the healthcare sector for ADL.

10. Identify future work that should focus on validating the methodology on a larger dataset and incorporating additional parameters to enhance the accuracy of health condition predictions.

6. Conclusion

The Article has shown that smart meters can play a crucial role in providing valuable insights into the daily routine changes or ADL of individuals, particularly for healthcare applications. This is a significant development in the healthcare industry as it can help in early detection and timely intervention, which can significantly improve the quality of life of patients while reducing healthcare costs. Smart meters can collect data on various aspects of an individual’s life, including sleep patterns, energy consumption, and mobility. This data can be analyzed to identify changes in daily routines, which can indicate early signs of illness or injury. By studying this data, healthcare professionals can provide timely support and care to individuals, potentially preventing the need for expensive hospitalizations and other medical interventions. The use of smart meters for health care and ADL detection is an exciting area of research with promising implications for the future of healthcare.

The data collected from smart meters can help healthcare providers to identify patterns and trends in the daily routine changes of individuals, which can be used to prevent or manage chronic diseases such as diabetes, hypertension, and cardiovascular diseases. In conclusion, the use of smart meters for healthcare applications is a significant advancement in the healthcare industry. It has the potential to improve the quality of life of patients while reducing healthcare costs. By providing early detection and timely intervention through the analysis of smart meter data, healthcare professionals can help individuals live healthier, happier lives.

Our upcoming research will focus on ensuring efficient and secure data management in smart meters suitable for healthcare applications. We aim to address security concerns by exploring encryption technologies, including advanced encryption algorithms that provide end-to-end encryption, to ensure secure data transmission between the smart meter and the utility provider. Our next phase of this research will focus on developing algorithms that are tailored to specific healthcare applications to determine the most appropriate anonymization method. This way, sensitive data can be stored securely and only authorized individuals can access it.

In conclusion, smart meters are promising technologies that can improve AAL and support healthcare applications. However, we must address data security and privacy concerns before their implementation. Our approach involves developing more advanced encryption algorithms and anonymization methods that prioritize data security and user privacy without compromising system efficiency and cost-effectiveness.

Acknowledgments

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Acronyms and abbreviations

AAL	Ambient Assisted Living
ADLs	Activities of Daily Living
IoT	Internet of Things
NILM	Non-Intrusive Load Monitoring
SSE	Sum Square Error