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Sustainable EnergySense: a predictive machine learning framework
for optimizing residential electricity consumption
Murad Al‑Rajab1 · Samia Loucif2
Received: 9 January 2024 / Accepted: 2 April 2024
© The Author(s) 2024
OPEN
Abstract
In a world where electricity is often taken for granted, the surge in consumption poses significant challenges, including
elevated CO2 emissions and rising prices. These issues not only impact consumers but also have broader implications
for the global environment. This paper endeavors to propose a smart application dedicated to optimizing the electricity
consumption of household appliances. It employs Augmented Reality (AR) technology along with YOLO to detect electrical appliances and provide detailed electricity consumption insights, such as displaying the appliance consumption rate
and computing the total electricity consumption based on the number of hours the appliance was used. The application
utilizes Linear Regression as a machine learning (ML) algorithm to develop the electricity consumption forecasting model
for the next months, based on past utility bills. Linear regression is often considered one of the most computationally
lightweight ML algorithms, making it suitable for smartphones. The application also offers users practical tips for optimizing their electricity consumption habits.
Keywords Sustainability · Machine learning · Deep learning · Electricity consumption · Object detection · Smart
application
1 Introduction
Electricity plays a pivotal role in modern life, offering the power needed to illuminate our homes and drive industries
and economies. Its importance is emphasized by its contribution to lighting, heating, and providing energy for various
appliances and devices that have become integral to our daily lives.
The increased number of devices dependent on electricity reflects the ever-growing integration of technology into our
lives. Information Technology (IT) has a massive contribution to electricity consumption. From smartphones and laptops
to households and the Internet of Things (IoT), such as refrigerators, air conditioners, and washing machines, as well as
electric vehicles, the electricity demand has increased exponentially. These essential home appliances are crucial to our
daily lives, contributing to the overall electricity consumption in households. Another example is data centers which
exemplify a substantial electricity demand, necessitating the installation of thousands of storage devices to manage
large datasets. Maintaining these devices at optimal functionality requires rigorous cooling at lower temperatures. As we
become more dependent on technology, so does the importance of maintaining a robust, sustainable, and eco-friendly
electricity infrastructure.
* Murad Al-Rajab, murad.al-rajab@adu.ac.ae; Samia Loucif, samia.loucif@zu.ac.ae | 1College of Engineering, Abu Dhabi University,
Abu Dhabi, UAE. 2College of Technological Innovation, Zayed University, Abu Dhabi, UAE.
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According to the latest data from [1], the worldwide net electricity consumption in 2022 reached 25,530 terawatthours. Several factors contribute to the high electricity consumption. One of them is the lack of awareness on how to
reduce utility costs. For instance, leaving devices running for extended periods or neglecting to turn them off completely
such as lights left on for days in offices or homes contribute to the problem. Surprisingly, this behavior persists even after
individuals receive their electricity bills. Research conducted by the Ministry of Energy in the UAE; electricity consumption has shown a significant increase from 2003 to 2019 [2].
Another factor is that individuals frequently find it challenging to assess their daily electricity expenditures until
they receive the monthly bill. Given that electricity costs are dependent on individual usage patterns, many users lack
awareness of their consumption levels. This difficulty is amplified by the consistent usage of electrical appliances that
draw power continuously.
Moreover, despite the presence of applications on platforms like iOS, and Google Play that aim to provide expenditure
insights and reduction tips, a considerable number of users express dissatisfaction. Frequently cited criticisms include
complex interfaces that make it harder for users to find solutions and include unnecessary requirements like inputting
daily usage. Furthermore, certain apps overwhelm users with an excess of information that proves challenging to process.
The overall lack of user-friendliness, coupled with a failure to underline the environmental consequences of excessive
electricity consumption and ways to address it, adds to the overall challenge of effectively utilizing these applications.
This paper proposes a smart practical use for sustainable energy, specifically focusing on electricity. The proposed
application assists users in forecasting their upcoming utility bills by utilizing artificial intelligence (AI) algorithms, taking
into account current and past bills. Additionally, it employs a deep learning (DL) algorithm to identify electrical appliances
and calculates the total electricity consumption based on the operating hours of each appliance. The rest of this paper
is organized as follows: Sect. 2 discusses related work and previous studies, while Sect. 3 details the requirements and
architecture of the suggested application. On the other hand, Sect. 4 covers its design and implementation. Section 5
presents the results and includes illustrative examples using the application. Finally, our conclusions and plans for future
work are outlined in Sect. 6.
2 Related work
The global call for sustainable energy practices is driven by climate change and the finite nature of natural resources.
Among these different forms of energy, electricity is included. Moreover, with the sophisticated capabilities of smartphone cameras and processing power, AR has found widespread application in various fields through mobile applications such as industry, healthcare, education, environmental sustainability [3], and many other fields. Likewise, in recent
years, ML and DL have been widely employed and have found extensive application across various domains, including
sustainability [4].
In the context of energy conservation, several studies have been conducted. In [5], an AR-based mobile application
for tracking energy usage has been developed. However, this application is limited to enabling users to scan household
appliances, obtaining details on the energy consumed by each device, and calculating the daily total energy usage. The
application is simple and does not have any integrated AI capabilities. The study in [6] concentrates on the creation of
an IoT-based energy meter, which is designed to oversee energy consumption by incorporating a GSM module. The primary aim is to transmit energy data to mobile phones via text messages. The authors proposed a solution to tackle the
challenge where regular meter inspections are not feasible for accurately assessing energy utilization. The suggested
remedy involved enabling remote monitoring of power consumption, thereby improving the precision of billing processes. Additionally, the system provides the capability to manage the load remotely, allowing for the toggling of power
states (on/off ) through message communication. Moreover, the implementation facilitates the transmission of SMS
notifications to both the end-user and the authorized electricity board, serving as an update mechanism and enabling
online bill payment.
A study done in [7] adopted the back-propagation neural network (BPNN) algorithm to forecast electricity consumption, considering various factors such as weather conditions, weekends, and holidays. The authors found out that integrating these elements, particularly weather and weekend/holiday factors enhances the prediction accuracy and decreases
the mean square error. Another study on electricity consumption forecasting using Holt-Winters’ exponential smoothing
method was introduced in [8]. The primary rationale for adopting the Holt-Winters exponential smoothing method for
forecasting is that it works well with a small sample of data. The authors suggested a hybrid forecasting model named
FOA-MHW, where the fruit fly optimization algorithm selects smoothing parameters for the selected method. In the
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context of the growing significance placed on sustainability, the authors in [9] incorporated gamification and persuasive
design principles to provide a system in five design cycles that monitors employees’ electricity usage on their computerrelated equipment at work place.
The study in [10] addressed the challenge of low prediction accuracy in building energy management systems. The
authors proposed the development of a predictive model using ML algorithms and the study was applied in real world
context in Malaysia. The three ML algorithms considered in that study were Support Vector Machine (SVM), Artificial
Neural Network (ANN), and k-Nearest Neighbor (KNN), and they were evaluated based on the metrics: root mean square
error RMSE, normalized NRMSE, and Mean Absolute Percentage Error (MAPE). The results revealed that SVM consistently
outperforms both ANN and KNN in the majority of cases considered in this analysis, demonstrating low RMSE, normalized NRMSE, and MAPE. The authors in [11] proposed predictive models for energy consumption in manufacturing
systems, focusing on energy efficiency in various sectors such as electrical, metal, plastic, and food manufacturing in
the USA. They employed ML algorithms to develop a predictive model for energy consumption, utilizing a dataset from
the Department of Energy Industrial Assessments Centers (IACs). The study’s outcomes revealed that the Random Forest Regressor and DL produced the most reliable and accurate results. In [12], the authors aimed to identify the optimal
method for predicting electricity demand in specific scenarios. They applied various ML algorithms to forecast electricity consumption, with the KNN model demonstrating superior performance, achieving an accuracy rate of 90.92% in
predicting agricultural production.
Other existing applications in this domain include Emporia Energy [13], a smart home energy management system
that displays electricity consumption, provides tips on saving electricity, and facilitates the visualization of corresponding
bills. Energy Cost [2] is an application enabling users to create a registry of electrical appliances and estimate the cost of
their electricity consumption. Energy Conservation [14] is a straightforward application featuring various energy-saving
wiki articles, offering advice on sustainable energy practices. The Electricity Cost Calculator [15] is an online application
that serves as a tool, allowing users to compute electricity bills for various appliances by entering parameters such as
power in watts, quantity, duration, electricity cost per unit, and billing duration. Etihad Water and Electricity [2] is a simple
application that enables users to view and pay their bills, though it lacks features specifically addressing sustainability
concerns. Meters Reading [16] is another application that calculates water, electricity, and gas readings based on images
taken by the user’s mobile device or iPad, utilizing OCR dependencies.
The authors in [17] conducted a comparative study to predict appliance energy consumption in low-energy buildings. They considered Tree Model, Ensemble Model, Support Vector Regression, Linear and Statistical Regression Models,
and Neural Networking (NN) algorithms implemented in Matlab. The author incorporated important parameters such
as temperature, humidity, and wind speed into the analysis, as these factors have an impact on energy consumption.
Accuracy metrics, including Root Mean Square Error, Coefficient of Determination, and Mean Absolute Error, were used to
evaluate the performance of the models. The results of the study revealed that the NN model exhibited higher accuracy
in predicting energy consumption. However, it is noteworthy that the authors did not specify the types of appliances
considered in the study, which is crucial information for drawing conclusive findings. The authors in [18] focus was on
developing a smart energy management system for a microgrid, which is like a small-scale power system. The system
was using deep neural networks (DNN) to efficiently control power and predict energy demand considering factors
such as how many people are around, their habits, income, appliances, and the weather. They evaluated the proposed
model to other models like gradient boost and linear regression using metrics like root mean square error (RMSE) and
coefficient of determination (R2), the results showed that their proposed deep neural network model was more accurate
in predicting energy consumption patterns. The authors in [19] conducted a thorough examination of the current ML
approaches employed to predict energy consumption but the focus was on the manufacturing industry. Similarly, the
authors in [20] conducted research with a Taiwanese semiconductor corporation, utilizing data analytics to create energy
efficiency models. The aim was to enhance manufacturing efficiency, reduce overall energy consumption, and optimize
machine configurations in the semiconductor industry.
Extensive efforts and research have prompted exploration into energy management systems and smart grid technologies, aiming to enhance the efficiency of residential electricity consumption [21]. Although they offer several advantages,
including real-time monitoring, reduced energy utilities, and increased sustainability, they are not affordable solutions,
as not all countries and individuals can afford them. Additionally, other concerns such as privacy and security arise.
In the various studies mentioned earlier, some focus on developing predictive models for energy usage in industrial
and manufacturing settings, while others aim for simplicity by raising awareness. Additionally, some studies utilize proposed predictive models specifically for home appliances. Our proposed solution presents a user-friendly application,
designed for easy use. This application integrates diverse technologies like AR and YOLO object detection, providing
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forecasts for the electricity consumption of home electrical appliances for 3 months ahead. It notifies users when the
electricity consumption has exceeded the regular monthly consumption rate, and offers detailed statistics on the individual contribution of each appliance to the overall consumption.
3 System requirements and architecture
3.1 Requirements gathering
We conducted a quantitative survey using a stratified random sampling method via Google Forms, targeting all families
in the UAE. This population was divided to include three income-based categories: low, middle, and high income. The
sample size was 100 participants. The survey consisted of 14 questions—13 close-ended and one open-ended. Among
the respondents, 24% reported living in small apartments with two or three bedrooms. Additionally, 36% revealed
that their electricity consumption utility fell within the range of US$150 to $300, which is considered somewhat high.
From the survey responses, we identified important features for our sustainability-focused mobile application, including monthly electricity consumption tracking, cost limit notifications, energy-saving tips, appliance-specific electricity
consumption insights, and quick reminders. The application aims to assist users in sustaining and staying within their
budgeted electricity consumption.
3.2 System architecture
The system’s primary goal is to offer solutions for optimizing electricity consumption in household appliances. The
application analyzes monthly electricity usage and suggests sustainable alternatives or assistance based on user input
values. Furthermore, it forecasts future consumption and utility bills using data analytics techniques and ML algorithms.
The application includes an AR feature, allowing users to target electrical appliances and view real-time electrical consumption indicators. Another important feature is a Chatbot that is integrated to assist users with frequently asked
questions, utilizing a Natural Language Processing (NLP) artificial intelligence algorithm. The context diagram of the
system is illustrated in Fig. 1.
In the user interaction sequence, as shown in Fig. 1, consumers interact with the application by first registering electrical appliances in the system and recording the consumption rate of each of these devices. Subsequently, the consumer
Fig. 1 The proposed application system architecture
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has the option to request information on electricity consumption, including predictions generated through the ML
algorithm and appliance consumption rates determined by object detection. Additionally, consumers have the option
to interact with an intelligent chatbot for assistance. The application facilitates the utilization of AR technology to scan
home appliances, incorporating the state-of-the-art YOLO (You Only Look Once) DL algorithm for object detection. This
integration allows the user to easily scan and identify home appliances providing accurate and real-time measurements
of electricity consumption. By employing Yolo as an efficient algorithm for object detection, the application empowers
the accurate identification of different home appliances. These requirements collectively address the needs of stakeholders and users, aiming to optimize electricity consumption while enhancing user engagement and system functionality.
4 Design and implementation
The following explains the design and the implementation of the proposed application.
4.1 Application design
4.1.1 Electricity consumption prediction design using ML
Since this is a mobile application using a smartphone, we opted for a linear regression algorithm to construct the electricity consumption forecasting model. In terms of CPU computing and power efficiency, linear regression is often considered
one of the most computationally lightweight ML algorithms. Linear regression involves simple mathematical operations,
which are computationally efficient and generally require lower processing power compared to other ML algorithms.
The model was trained using the dataset found in [22]. The dataset captures the electricity consumption of electrical
appliances. It includes features such as the number of hours each electrical appliance like air-conditioners, refrigerators,
televisions, mobile devices, and washing machines operates per month. Finally, using this model, the feature allows the
consumer to scan their bills each month, which help the model to predict future monthly bills.
As shown in Fig. 2, the electricity consumption for each month is predicted based on the previous bills entered by
the consumer.
4.2 Object detection design using AR
To build the model, the YOLO (You Only Look Once) algorithm, which uses a Convolutional Neural Network (CNN) for
real-time object detection along with AR technology displays the electricity consumption rate and based on the number
of hours, it computes the total electrical consumption rate. To reach higher detection accuracy of objects, a large number
Fig. 2 Electricity Consumption Prediction Design Using ML
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of pictures for various appliances to train the model. The electrical appliance detected will be visually represented within
a green or red frame based on the total electrical consumption rate, whether low or high respectively.
4.3 AR and ML implementation
Our proposed application integrates both AR and ML components in order to provide real-time detection of electrical
appliances inside the house and accurate electricity consumption rates. This integration consists of several steps and
interactions as listed below:
1. AR Object Detection This feature requires the use of the mobile camera to scan and detect electrical appliances. Once
the user chooses this option, the mobile camera opens, allowing the user to view the surrounding environment and
target any electrical appliance. The integrated YOLO algorithm activates the live camera and starts detecting objects
(electrical appliances) with high accuracy since it is based on the CNN algorithm. As long as the camera captures the
frame, YOLO will analyze each frame to determine the electrical appliances within the surrounding environment.
Once an object is detected, AR technology adds an overlay visual representation as a boundary box or a frame around
the detected object (electrical appliance) within the camera view, resulting in real-time feedback to the user.
2. ML Electricity Consumption Calculation The ML feature computes the electricity consumption based on the detected
appliances and their usage amounts. Each electrical appliance has a known predetermined power consumption rate
(wattage). Users input the number of hours each appliance was powered on, and then the proposed application
computes the total electricity consumption rate for any detected object. The computation is done by multiplying
the wattage of each electrical appliance by the number of hours it was used. The resulting consumption output is
then reflected and displayed to the user in real-time through different colored frames (green and red) around the
detected object. The green frame indicates the consumption rate is within range (low consumption), while the red
frame indicates that the consumption rate is above range (high consumption) and the user must take proactive
measures to save electricity.
3. Interaction between AR and ML Once the AR detects electrical appliances throughout the camera view, it visually
displays each appliance surrounded by frames or an overlay visual representation like a boundary box of different
colors (green and red) as real-time feedback to the user regarding the consumption rate. The ML model immediately
processes the data associated with these detected objects to calculate the electricity consumption rate.
4. ML Prediction Model The linear regression model is utilized to predict future bills of electricity consumption. As
discussed earlier, this model was chosen for its computational efficiency, making it suitable for implementation on
mobile devices. We trained the model using a dataset that captures different features related to electricity consumption, such as the name of the electrical appliance and the number of hours each appliance operates per month.
Features such as air conditioners, refrigerators, televisions, mobile devices, and washing machines are included in the
dataset to effectively train the model. Once trained, the model can predict future monthly electricity consumption
bills. Our current model is designed to predict bills for 3 months ahead. Users can view these predictions alongside
other relevant information, such as current electricity usage and tips for optimizing consumption.
5. User Interaction Users interact with our proposed application through various options and functions from the main
menu, such as initiating object detection or predicting electricity consumption. Both AR and ML models work simultaneously in response to user interaction to provide real-time feedback to the user regarding their electricity usage
and consumption rate in a user-friendly and easy-to-use manner.
4.4 Application implementation
4.4.1 Development environment
To train our object detection model for the features, we used Darknet, an open-source neural network framework written in C and CUDA. The YOLO object detection algorithm will be employed in conjunction with Darknet, and using the
OpenCV API. Furthermore, in developing our application, we employed two development environments. Android Studio was utilized to craft the application interfaces, ensuring a user-friendly and visually appealing design. On the other
hand, PyCharm was employed to deploy the ML algorithms to integrate the YOLO object detection algorithm and the
Darknet framework.
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4.5 Illustrative example
Once the user launches the application, the user will be directed to the registration screen as shown in Fig. 4a. If the
user already registered with the application, he will just sign in. Otherwise, the user creates an account. Once signed
in, the user will get another screen to interact with the application to be able to either (1) start detecting objects
(appliances), (2) predict the electricity consumption for the upcoming 3 months, (3) get tips on conserving electricity,
and (4) chat with the chatbot. All these options are shown in Figs. 3, 4 and 5.
Once the consumer selects the option “Start detecting objects”, it allows the consumer to scan any home appliances using a smartphone camera. The latter detects those appliances then, the consumer will be prompted to input
the number of hours during which the appliances were powered on. Then, the total electricity consumption rate for
these appliances will be displayed, Fig. 4b, c.
The total electricity consumption is computed as the product of the wattage of the appliance and the number
of hours out by the consumer. In the current implementation of the proposed application, seven appliances can be
detected, which are: TVs, laptops, smartphones, microwaves, ovens, toasters, and hair dryers. Moving to the camera
activity screen we can see that the application immediately starts detecting every electrical appliance in our detection list. When an appliance is detected, here the AR is applied and shows a frame around the appliance along with
the type of this appliance, and the electricity consumption of that appliance.
There are two possible colors for the frame displayed around the appliance, green and red. The green frame indicates that the consumer did not exceed the regular monthly consumption rate. Each appliance recommends a regular
rate of consumption per month [23]. Similar appliances might have different wattage values, but as mentioned earlier,
this feature is based on estimation; hence, the application will store an average wattage for each appliance to allow
the feature to calculate the daily electricity consumption. To give the consumer more customization, which would
help get more accurate estimations, an extra option will be added to allow the consumer to enter the exact wattage
value that is registered on the back of the appliance. After that, the electricity consumption of that appliance will
show up on the screen [24].
At the end of the month, and after the consumer has received the monthly bill, they will be able to see how much
of that bill their appliances consumed. A bar chart option, as shown in Fig. 4d, can be chosen to show the statistics
of each appliance. The feature will continue to send alerts to the consumer for appliances that have been heavily
Fig. 3 EnergySense application menu options
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Fig. 4 Registration screen: a
registration screen, b object
detection, c computation of
total electricity consumption
rate, d statistics of electricity
consumption per appliance
Fig. 5 Prediction of electricity consumption
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used and have exceeded the normal consumption. The alerts will remind the consumer to follow the tips that help
optimize the use of those appliances [9].
The option “Predict the electricity consumption”, Fig. 5, enables the consumer to input their past monthly electricity
bills and the application runs the ML algorithm to predict the electricity bill for the upcoming 3 months as depicted by
the figure below. The figure to the left shows the inputs of the past monthly electricity bills. The application shows an
interactive screen, shown by the figure in the middle, displaying the amount of the bills as a graph. When the consumer
clicks the graph, it shows the interactive information with the month and the bill value. The predicted bills are shown as
an interactive graph in the Figure to the right.
5 Discussion
Our proposed application is designed to be prominent among other existing ones. What makes it unlike others is the
features that it encompasses AR and ML algorithms to empower real-time electrical appliance detection and electricity
consumption rate. In addition, it highlights the prediction capability of the electricity bills for the upcoming months. The
proposed application with its features compared to other existing ones is summarized in Table 1.
The proposed application has a great impact on the economy, society, and environment. It assists consumers in optimizing electricity usage and saving costs. Also, it aims to address the environmental challenges caused by ever-increasing
electricity consumption. Moreover, by providing users with practical tips for optimizing electricity consumption habits,
the application encourages a more sustainable approach to electricity use, aligning with global environmental conservation efforts.
In testing our proposed application, we conducted thorough evaluations on various appliances, utilizing past utility
bills as a benchmark. The results were encouraging, demonstrating the application’s ability to provide accurate insights
into energy consumption patterns. Moving forward, we plan to expand our testing scope to encompass a wider range
of appliances and scenarios, ensuring the reliability and the enhancement of our proposed solution.
Looking forward, our future plans for the proposed solution include establishing a collaborative relationship with utility
companies, seeking to promote the widespread adoption of our framework and encourage sustainable energy practices.
6 Conclusion and future work
As the years progress, technological advances continue to make our lives more convenient. Throughout this research,
we have provided a detailed implementation of an application focusing on the electricity consumption of household
appliances. The primary objectives were to design and develop a mobile application empowering user with information
on the cost of each electrical appliance and ways to reduce it. By optimizing the usage of household appliances through
machine and DL algorithms, particularly utilizing object detection for detailed insights, the application seeks to save
costs and contribute to environmental sustainability. The prediction functionality based on user-provided data not only
enhances efficiency but also aids in long-term electricity planning.
Table 1 Feature comparison: sustainable EnergySense vs. others
Application
Provides consumption or bills Electricity consumption
for the household
rate calculation
Eco-friendly electricity tips
Application
Emporia energy
Yes
No
Yes
Energy cost
Energy consumption
Electricity cost calculator
Etihad water and electricity
Meters reading
Sustainable EnergySense
No
No
Yes
Yes
Yes
Yes
Yes
No
Yes
No
Yes
Yes
Yes
No
No
No
No
Yes
Yes, gives control of the
house using sensors
No
No
No
No
No
Detection and prediction
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In future work, we plan to include more household appliances in the application and to give more flexibility to the
user to get a prediction of the electricity utility cost for any number of months instead of only 3 months. Additionally,
we aim to consider other ML algorithms to test the prediction feature.
Acknowledgements We would like to express our gratitude to our students, Omar Basahel, Areesha Ahmed, Mohanad Ameen, and Sami
Loumachi, who played an integral role in the success of this project.
Author contributions Conceptualization: Murad Al-Rajab and Samia Loucif jointly conceived the research idea. Both authors formulated the
objectives of the study. Application Development: Murad Al-Rajab, led the development team of the Sustainable EnergySense application. He
supervised the implementation of machine learning (ML) algorithms for detailed electricity consumption insights through object detection.
Machine Learning Expertise: Murad Al-Rajab, and Samia Loucif, in addition to the development team contributed expertise in machine learning
algorithms and predictive analytics. All played a key role in designing and implementing the ML algorithms responsible for forecasting future
electricity usage based on user-provided data. Collaborative Data Analysis: All authors collaborated closely on data analysis and interpretation
of results. Manuscript Preparation: Murad Al-Rajab and Samia Loucif collaborated on the writing of the manuscript.
Data availability The dataset used in the experiments for this paper is publicly available in a repository: https://www.kaggle.com/datasets/
suraj520/indian-household-electricity-bill.
Declarations
Competing interests The authors declare no competing interests.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article
are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in
the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will
need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
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