IoT-Driven Intelligent Energy Management: Leveraging Smart Monitoring Applications and Artificial Neural Networks (ANN) for Sustainable Practices

Abstract

The growing mismanagement of energy resources is a pressing issue that poses significant risks to both individuals and the environment. As energy consumption continues to rise, the ramifications become increasingly severe, necessitating urgent action. In response, the rapid expansion of Internet of Things (IoT) devices offers a promising and innovative solution due to their adaptability, low power consumption, and transformative potential in energy management. This study describes a novel, integrative strategy that integrates IoT and Artificial Neural Networks (ANNs) in a smart monitoring mobile application intended to optimize energy usage and promote sustainability in residential settings. While both IoT and ANN technologies have been investigated separately in previous research, the uniqueness of this work is the actual integration of both technologies into a real-time, user-adaptive framework. The application allows for continuous energy monitoring via modern IoT devices and wireless sensor networks, while ANN-based prediction models evaluate consumption data to dynamically optimize energy use and reduce environmental effect. The system’s key features include simulated consumption scenarios and adaptive user profiles, which account for differences in household behaviors and occupancy patterns, allowing for tailored recommendations and energy control techniques. The architecture allows for remote device control, real-time feedback, and scenario-based simulations, making the system suitable for a wide range of home contexts. The suggested system’s feasibility and effectiveness are proved through detailed simulations, highlighting its potential to increase energy efficiency and encourage sustainable habits. This study contributes to the rapidly evolving field of intelligent energy management by providing a scalable, integrated, and user-centric solution that bridges the gap between theoretical models and actual implementation.

1. Introduction

The IoT is at the vanguard of technological innovation, with innovative potential in a variety of fields, including energy consumption and management. IoT is defined as a network of interconnected devices equipped with sensors, software, and various technologies that collect and share data via the Internet, allowing for autonomous decision-making and optimal resource utilization [1]. The rapid growth of the Internet of Things is transforming everyday life by connecting devices—from smartphones to smart grids—creating intelligent ecosystems that improve efficiency and sustainability [2,3].

Internet of Things (IoT) systems play an important role in energy conservation since they allow for real-time monitoring, control, and optimization of energy consumption. This capability is especially important given the projected global increase in electricity demand—around 2% in 2023—with further acceleration expected in 2024. The building sector alone consumes about one-third of global energy, with much of it lost owing to inefficiencies. According to studies, improper heating, cooling, lighting, and appliance management can account for 20–30% of total residential energy waste. These inefficiencies not only worsen environmental degradation, but also place a huge financial burden on consumers. As a result, developing sustainable and intelligent energy management strategies has become critical [4,5]. Figure 1 shows the role of IoT in energy conservation.This image shows how IoT technologies provide real-time data collecting and cognitive control to increase building energy efficiency. It highlights possible energy savings, economic reductions, and benefits to environmental sustainability.

This study proposes the creation of a smart monitoring system that uses IoT technology to improve energy efficiency in residential and commercial buildings. The system will use IoT-based sensors and microcontrollers to track the operational status of appliances in real time. A dedicated smartphone application will give consumers in-depth insights into their energy consumption, allowing them to control equipment remotely, follow consumption trends, and generate complete reports. The system integrates Wi-Fi networking to ensure seamless communication between devices, resulting in a more intelligent and responsive energy management framework.

Although the coupling of IoT and Artificial Neural Networks (ANNs) in energy management has previously been investigated, the novel aspect of this study is the practical integration of these technologies into a unified, end-to-end mobile energy management system. Rather than focusing on theoretical models or isolated components, this study presents a fully simulated and adaptable framework for personalized energy monitoring and control based on behavioral profiles and usage situations.

Data fusion techniques contribute significantly to the performance of intelligent EMS by merging sensor data to increase insights. Fusion may be centralized or decentralized. In a centralized approach, all sensor data are combined during the fusion phase, but in a decentralized model, measurements from each sensor are analyzed separately. The fusion process is divided into three stages: data fusion, feature fusion, and decision fusion. Raw sensor data is combined during data fusion to provide a more refined dataset with less data loss. The following phase is to extract characteristics and relationships from the data to ease pattern interpretation, with the goal of strengthening decision-making processes based on evidence.

1.1. Research Objectives

The major goal of this study is to assess the effectiveness of the proposed IoT-based smart monitoring system in optimizing energy usage, decreasing waste, and encouraging sustainable practices among users in a variety of building settings. Furthermore, the study intends to evaluate user acceptance and the long-term behavioral impact of adopting such intelligent energy management systems. Despite substantial advances in IoT-powered energy management systems, important gaps persist. Many existing studies prioritize individual IoT devices over integrated multi-sensor systems capable of optimizing energy consumption across multiple appliances. Furthermore, robust frameworks that use real-time analytics and predictive modeling to increase energy efficiency dynamically are limited. This study fills these gaps by integrating an ANN-driven predictive model directly into a user-facing application that combines technical optimization and real-world usability. Security and privacy problems with IoT energy management systems provide additional hurdles, emphasizing the necessity for research into secure communication protocols and data protection mechanisms. Finally, while IoT has showed the ability to reduce energy consumption, there is a scarcity of empirical research on user acceptance and the long-term behavioral implications of these technologies. By combining secure IoT infrastructure, adaptive user profile, and ANN-based forecasting, the proposed system represents a scalable and realistic leap in the deployment of intelligent energy management solutions. Addressing these limitations is critical for creating more effective, scalable, and secure IoT-based energy management solutions. The rest of the paper is organized as follows: Section 2 presents a literature overview of IoT applications in energy conservation. Section 3 describes the proposed system, including the research hypothesis and objectives. Section 4 gives the experimental results, while Section 5 summarizes the study’s main findings and implications.

1.2. Research Direction

To effectively address the research objectives and gaps identified, this study will focus on the following guiding research questions:

1. How effective is the proposed IoT-based smart monitoring system in optimizing energy consumption in residential and commercial buildings? This question is intended to assess the overall efficiency and efficacy of the smart monitoring system. It will involve determining how well the system minimizes energy use through real-time monitoring, adaptive controls, and the use of renewable energy sources. Measurements will include indicators for energy savings, changes in consumption patterns, and improvements in overall energy efficiency.

2. What are the user acceptance levels of the IoT-driven energy management system, and how do they influence long-term behavioral changes? Understanding user approval is critical for successfully implementing the system. This question tries to investigate the factors influencing users’ propensity to acquire and use technology throughout time. Insights will be obtained on user perceptions, satisfaction, and continued involvement, as well as how these characteristics relate to long-term energy consumption behavior change.

3. What security and privacy measures can be integrated into IoT energy management systems to enhance user trust and data protection? Given the vulnerabilities connected with IoT devices, this inquiry investigates the security protocols and privacy safeguards that can be used to secure user data. It will concentrate on identifying potential dangers, outlining best practices for encryption, secure communication, user consent methods, and evaluating how these measures might boost confidence and encourage widespread adoption among users.

4. How can real-time analytics and predictive modeling be effectively employed to improve dynamic energy efficiency? This question will investigate the use of advanced analytics and predictive modeling techniques to improve energy efficiency in real time. It seeks to assess the use of historical data and machine learning algorithms for forecasting energy demand, optimizing consumption techniques, and identifying potential inefficiencies. The goal is to understand how these technologies can improve energy management by allowing for proactive system adjustments based on projected usage patterns.

2. Related Work

The growing integration of intelligent energy management systems (EMSs) into smart homes has been a major research priority, driven by the pressing need for energy efficiency, cost savings, and sustainable living. Numerous research has investigated different techniques to addressing these difficulties, including machine learning (ML), optimization algorithms, Internet of Things (IoT) technologies, and real-time data analytics.

One important contribution is the use of binary particle swarm optimization (BPSO) techniques in conjunction with fuzzy logic inference systems to schedule energy in smart homes [6,7,8,9]. These devices were tested in 10 residential units, efficiently handling appliances such as washers and dryers during off-peak hours. Despite energy savings, some studies found that tight scheduling algorithms compromised user comfort. Fuzzy regulators were also utilized for passive cooling control, which increased energy efficiency through aeration techniques. Other studies used co-evolutionary PSO approaches to optimize collective family energy use, supporting cooperative strategies among homes [10,11].

IoT and cloud computing have substantially advanced the smart home domain, allowing for scalable, city-wide energy services [9]. However, energy data is still prone to transmission glitches and sensor failures. To address this issue, self-correcting approaches such as the Misclassified Recall methodology and augmented decision-tree learning algorithms were proposed to manage misclassified data and improve prediction accuracy [12,13,14].

Optimization techniques such as mixed-integer linear programming [15], multiple knapsack approaches [16], and dynamic pricing models have been used to reduce peak energy consumption and costs. Machine learning and AI-driven techniques have enabled smart decision-making across home devices, while smart meters, as discussed by [17,18], have emerged as key instruments for long-term energy strategies.

The spike in urban migration, as seen by the UN Habitat Trends Observatory, with forecasts predicting that 70% of the global population will live in cities by 2050, has accelerated the development of smart cities [19,20,21,22]. In this context, IoT-enabled smart houses are critical components, especially in gated communities that value security and shared resources. Spain, for example, shows the merging of bioclimatic architecture and sensor-equipped homes for real-time monitoring and automation.

Recent frameworks, such as “Smart Residents” [23,24,25,26], offer extensive IoT platforms that interface with interactive television (iTV) and wireless sensor networks. These platforms provide user-friendly interfaces for controlling home surroundings and provide real-time monitoring capabilities. Furthermore, green smart home architectures have been created to monitor resource usage and renewable energy production using dynamic gateways and cloud analytics [27,28]. Despite tremendous progress, issues persist, particularly in communication standardization, security, and privacy in IoT-based networks [29,30,31,32,33,34]. To address these issues, researchers have developed scalable architectures that use lightweight communication protocols like MQTT, even though many existing systems still use standard HTTP protocols [35].

To improve energy prediction and optimization, some studies used advanced machine learning frameworks and real-time data transfer protocols. For example, cost-effective IoT-based monitoring systems based on ESP8266 modules and current sensors have showed promise for theft detection and automatic control, albeit latency and scalability are still challenges [36,37]. The combination of IoT and wireless sensors enables dynamic adaption of systems like lighting and HVAC, resulting in a responsive and efficient energy management approach [38,39,40]. Multi-inhabitant energy control and load scheduling difficulties have also been addressed using reinforcement learning algorithms [41] and game theory frameworks [42]. It should be emphasized that, while the ML-Fused Layered Architecture described in [42] serves as a conceptual framework for structured system design, it is not part of the suggested implementation. The system presented in this study was developed separately and is specifically geared to handle the energy optimization difficulties associated with individualized residential energy management.

Furthermore,

Table 1. Comparative analysis of existing smart home energy management approaches.

Ref.	Technique/ Methodology	IoT Integration	Energy Optimization	User Comfort	Scalability/Limitations
[13]	BPSO + Fuzzy Logic for scheduling	Partial	High	Limited	Not easily scalable; rigid schedules
[14]	IoT-cloud integrated services	Full	Medium	Moderate	Susceptible to data loss, latency
[15] [16] [17]	Misclassified Recall, Decision Tree enhancements	High	High	High	Dependent on data quality
[18] [19]	MILP, Multiple Knapsack, Dynamic Pricing	Partial	High	Low	Complexity increases with household size
[20]	Co-evolutionary PSO among households	None	Medium	Limited	Lacks real-time adaptability
[21] [22] [23]	Sensor-driven automation (e.g., HVAC, lighting)	High	High	High	Requires robust infrastructure
[24]	MQTT communication protocol	Lightweight	Medium	Moderate	Transitioning from HTTP poses integration issues
[25] [26]	Reinforcement Learning, Game Theory	Medium	High	Adaptive	Needs extensive training, context-aware models
[27]	Smart meter analytics	High	Medium	Moderate	Dependent on user feedback for full potential
[8] [15] [16]	Smart Resident framework (iTV + WSN)	Full	Medium	High	Focused on usability; moderate energy gains
[17] [18]	Green architecture with dynamic gateway	Full	High	High	High implementation cost
[19] [20]	ESP8266 IoT modules for energy theft and monitoring	Low-Cost	Medium	Basic	Limited range and scalability
[42]	ML-Fused Layered Architecture (Proposed)	Full	Very High	High	Designed for real-time, multi-resident control

compares existing approaches in terms of their integration of preprocessing, decision-making, and fused ML models—an area where our proposed methodology contributes significantly by strengthening all three layers [43].

Our proposed method builds on these works by integrating preprocessing, decision-making, and fused ML techniques into a unified framework. Table 1 below highlights the key gaps in previous work and illustrates how our proposed architecture addresses these challenges more effectively.

This table compares prior research efforts on intelligent energy management systems (EMSs) for smart homes. Key evaluation metrics include IoT integration, energy optimization capabilities, user comfort, and system scalability. The examination focuses on both classic and current methodologies, such as fuzzy logic, machine learning, dynamic pricing, and reinforcement learning. The proposed ML-fused layered architecture outperforms others by combining preprocessing, robust decision-making, and autonomous control methods to improve energy efficiency and user adaptability.

This study’s main contributions are: (1) the development of an optimized ANN-based predictive framework for energy management; (2) the integration of adaptive user profiling for personalized recommendations; (3) the incorporation of GDPR-compliant privacy-preserving mechanisms; and (4) the provision of a scalable foundation for deployment in future IoT-based smart home systems.

3. Method

This paper presents a refined, intelligent energy consumption model for smart homes that emphasizes the usage of Artificial Neural Networks (ANNs) for accurate energy forecast. Figure 2 depicts the model’s four key layers: data acquisition, processing, application, and visualization.

3.1. System Architecture Overview

The proposed system provides a coherent ANN-driven intelligent energy management framework for optimizing household electricity consumption by combining IoT-enabled data collecting, predictive analytics, adaptive user profile, and personalized recommendation generation. The architecture was created expressly to meet the difficulties of real-time monitoring, predictive decision assistance, and user involvement in household energy management applications.

Unlike previous techniques, such as the ML-Fused Layered Architecture presented in [42], which inspired layered system design, the framework provided in this paper is a new, fully integrated model designed specifically for energy management applications. The key innovation of the suggested architecture is the mix of the following:

• Real-time data collecting from environmental and energy sensors using the Internet of Things.
• Predictive modeling with Artificial Neural Networks (ANNs).
• User Profiling Module for context-aware suggestions.
• Secure Communication Layer allows for secured data transport.
• Personalized Recommendation Engine to generate actionable behavioral feedback.

Figure 2 depicts the overall structure of the proposed framework, including a thorough depiction of each system layer and its relationships.

3.1.1. Model Architecture and Hyperparameter Tuning

We ran a series of exploratory experiments to identify the appropriate number of hidden layers and neurons per layer before deciding on an ANN architecture. We tested models with one, two, and three hidden layers, increasing the number of neurons in each layer (from 16 to 128). The Adam optimizer was used to train the models, which were then verified using a 20% hold-out subset of the data.

The three-hidden-layer design with [specify number] neurons in each layer was shown to give the best balance of prediction accuracy and computing efficiency, with an overall validation accuracy of 86.2% and precision of 99.8%. Models with fewer layers had a lower capacity to capture complicated temporal patterns, whereas deeper models with additional layers did not significantly enhance performance and had higher training costs.We also compared the ANN to alternative models, such as Long Short-Term Memory (LSTM) networks and gradient boosting (XGBoost). Despite similar train–validation splits, the ANN outperformed the other models for our dataset size (~3000 samples), making it appropriate for real-time deployment in resource-constrained contexts.

3.1.2. Data Acquisition

The dataset used in this study came from Kaggle’s publicly available IoT-enabled smart home energy usage repository [give link if possible]. It contains around 3000 daily energy usage records collected from households in various regions, including timestamps, indoor temperature, humidity, and energy consumption parameters. Prior to modeling, the data was thoroughly cleaned to improve data quality and model robustness. Missing sensor readings were interpolated using moving average techniques to fill gaps, and outliers were discovered using z-score thresholding and then deleted to decrease noise and assure data consistency. The dataset was randomly partitioned into 70% training and 30% testing subsets to facilitate model validation on unseen data, ensuring the generalizability of the predictive model. The features included in the dataset were as follows:

• Day: The sequential day number of data collection.
• Temperature: Daily average indoor temperature.
• Humidity: Daily average indoor humidity.
• Energy Consumption: Total energy used in kilowatt-hours (kWh).

The target variable was Cost, calculated based on energy consumption using Formula (1):

Cost = Electricity Usage (kWh) × 0.16 USD

(1)

This preprocessing pipeline established a standardized dataset suitable for training accurate and reliable predictive models.

3.1.3. Dataset Analysis

Dataset analysis aims to extract useful insights and build predictive capabilities from the data. The steps include the following:

-

Data Preparation: The dataset was read from a CSV file using the pandas library. Relevant features (Day, Temperature, Usage Hours, Electricity Usage) and the target variable (Cost) were extracted. The dataset was cleaned and structured.

-

Data Scaling: Features were scaled using StandardScaler to ensure zero mean and unit variance (Equation (2)).

X_scaled = (X − μ)/σ.

(2)

where μ is the mean and σ is the standard deviation.

-

Model Training: A neural network was trained using the scaled features with data split into training and validation sets.

-

Model Evaluation: Performance was monitored using validation loss (MSE).

-

Predictions and Aggregation: Model outputs were aggregated to weekly, monthly, and yearly predictions.

3.1.4. Data Processing

This layer ensures the integrity and consistency of the acquired data through several preprocessing steps:

• Missing Value Imputation using Moving Average (Equation (3)):

  x̃ₜ = (1/n) ∑_{i = t − n + 1}^{t} xᵢ.

  (3)

  where x̃ₜ is the estimated value at time t, and n is the window size.
• Feature Normalization (Min-Max Scaling) (Equation (4)):

  x’ = (x − x_min)/(x_max − x_min)

  (4)
  This scales features into the range [0, 1], improving training efficiency.
• Train–Test Split: The dataset is divided into 70% training and 30% testing subsets.

3.1.5. Application Layer (ANN-Based)

The ANN model predicts energy usage using a feedforward architecture that includes:

-

Input Layer: Accepts normalized features.

-

Hidden Layer(s): Performs non-linear transformations using the sigmoid function:

f(x) = 1/(1 + e^(−x))

(5)

-

Output Layer: Outputs the predicted energy consumption value.

Each neuron computes a weighted sum and applies the activation function (Equation (6)):

zⱼ = ∑_{i = 1}^{n} wᵢⱼ * xᵢ + bⱼ
aⱼ = f(zⱼ)

(6)

The network is trained using backpropagation with Mean Squared Error (MSE) as the loss function (Equation (7)):

E = (1/N) ∑_{k = 1}^{N} (yₖ − ŷₖ)²

(7)

Weights are updated using gradient descent (Equation (8)):

wᵢⱼ(t + 1) = wᵢⱼ(t) − η * ∂E/∂wᵢⱼ

(8)

where η is the learning rate and ∂E/∂wᵢⱼ is the gradient of the error with respect to the weight.

3.1.6. Prediction and Visualization Layer

The final forecasts are visualized to provide end-users with actionable insights, including real-time and historical energy use trends.

The trained ANN model was used to make predictions on unseen data. The predicted daily values of electricity usage and cost were aggregated to generate weekly, monthly, and yearly insights using (Equation (9)):

$${\mathrm{Total}}_{\mathrm{predicted}}={\sum }_{\mathrm{i}=1}^{\mathrm{n}}·{\mathrm{P}}_{\mathrm{i}}$$

(9)

These predictions were then visualized using matplotlib and seaborn libraries to identify long-term trends and seasonal variations in household energy consumption and cost.

3.1.7. User Profiling and Personalization

To improve the contextual relevance and practical usability of the proposed intelligent energy management system, a User Profiling Module was added into its architectural framework. This module captures and models behavioral variance among user groups, allowing for individualized energy usage advice.

Demographic Segmentation

Users are originally grouped into demographic profiles based on freely submitted registration data or system-derived trends, such as the following:

-

Elderly residents are typically associated with increased occupancy during the day and have special temperature comfort requirements.

-

Working Professionals: Frequently exhibit modest energy use throughout typical working hours, with increases in the early morning and late evening.

-

Multi-Resident Families: Show more complex and overlapping consumption patterns due to several users with different schedules.

-

Seasonal users (e.g., vacation homes) consume energy irregularly and seasonally.

Behavioral Pattern Learning

Using past consumption data, the system employs unsupervised clustering methods (such as k-means clustering) to automatically determine routine usage behaviors for each user profile. This allows for the creation of personalized operating baselines for each user category.

Personalized Recommendation Engine

Recommendations are dynamically adapted based on the active user profile. Examples include:

-

Working Individuals: Suggestions for postponing appliance consumption to off-peak hours during known absences, or automatic temperature adjustments before to arrival.

-

Prioritize comfort and safety for elderly residents by preventing automated shutdown of key appliances during active hours.

-

Multi-Resident Families: Notifications regarding coordinated use of high-power equipment to avoid demand spikes.

Adaptive Profile Refinement

User profiles are dynamically updated by continuously monitoring user engagement patterns with the system, ensuring that recommendations are responsive to changing user behaviors, lifestyle changes, or seasonal trends.

This personalized method not only improves energy management, but it also increases user happiness and long-term engagement by addressing the diversity of household energy behaviors in modern smart home environments.

3.1.8. Data Security and Privacy Mechanisms

The suggested intelligent energy management framework includes features that ensure secure communication and protect user privacy. Given the sensitivity of behavioral and consumption data in smart home contexts, the system employs multi-layered security mechanisms and user-centric privacy controls to ensure data integrity, confidentiality, and user autonomy.

Encryption of Data in Transit and at Rest

All communication between IoT devices, the ANN-based prediction server, and the cloud storage infrastructure is encrypted with Transport Layer Security (TLS 1.3). This ensures that transmitted data is encrypted from beginning to end, reducing the danger of interception or alteration when being transmitted across potentially unsecured wireless networks. For data kept on local or cloud-based servers, Advanced Encryption Standard (AES) with 256-bit keys (AES-256) is used. This symmetric encryption standard offers a strong barrier against illegal access to stored data, such as previous consumption records and model training data.

Data Anonymization and Minimization

To preserve individual privacy, the system employs pseudonymization techniques. Before being analyzed, personally identifying information (PII) such as names and addresses is deleted or replaced with pseudonymous identifiers. Predictive modeling uses only aggregated or anonymised data, which reduces the possibility of re-identification.

Furthermore, the system follows data minimization principles, gathering just the information required to deliver key features, decreasing the potential exposure of sensitive data.

User Consent and Transparency

A consent management interface has been built into the User Interface layer to improve transparency and user control. Users are offered with clear, affirmative consent prompts prior to data collection, in accordance with General Data Protection Regulation (GDPR) guidelines. This includes the following:

-

Before any data transmission begins, the user must explicitly opt in.

-

Granular consent controls allow users to define which sorts of data they are willing to release.

-

Revocation mechanisms allow users to withdraw their consent at any moment, causing the system to erase or anonymize the related personal data.

Compliance with Industry Standards

The proposed system’s security and privacy architecture is based on industry best practices and regulatory frameworks such as the General Data Protection Regulation (GDPR) and the ISO/IEC 27001 standards [44] for information security management. This not only ensures legal compliance, but also builds user trust and long-term involvement.

By incorporating encryption protocols, data minimization techniques, and explicit consent frameworks into the system’s architecture, the proposed framework provides a secure, privacy-preserving, and reliable energy management solution suitable for real-world deployment in residential and commercial settings.

3.1.9. Evaluation Metrics

-

Mean Absolute Error (MAE) (Equation (10)):

MAE = (1/n) ∑|yi − ŷi|

(10)

-

Root Mean Squared Error (RMSE) (Equation (11)):

RMSE = sqrt((1/n) ∑(yi − ŷi)^2)

(11)

3.2. ANN Model Training and Validation

The ANN is trained using supervised learning methods. The dataset is split across 70% training and 30% testing. During training, the network learns from past consumption patterns, with hyperparameter adjustment and optimization performed via 5-fold cross-validation to avoid overfitting and improve model robustness. Following training, the model’s generalizability is assessed on an unknown test dataset using a variety of performance metrics, including accuracy, sensitivity, specificity, and precision.

3.3. Intelligent Decision Making

Once energy consumption projections are generated, the system determines if the expected usage is within acceptable limits. If a substantial deviation is detected—such as odd spikes or inefficiencies—the system takes intelligent decision-making steps. These steps may involve informing the user or automatically initiating optimization methods such as changing use schedules or offering alternative energy-saving choices.

Figure 3 depicts the intelligent workflow of the ANN-based energy consumption prediction system, which is structured into four basic layers:

-

Data Acquisition: Sensors or IoT-enabled devices capture real-time or historical data, including temperature, usage hours, and electricity consumption.

-

Dataset Analysis and Preprocessing: This stage assures data quality by managing missing values, feature scaling (e.g., with StandardScaler), and optional feature selection to maximize model performance.

-

Model Training and Prediction: The ANN design is defined, including the number of layers, activation functions (such ReLU), and neurons per layer. The model is trained with Adam optimizers and assessed with metrics like Mean Squared Error (MSE). The system is then used to make real-time or scheduled forecasts.

-

Visualization and interpretation: Predictions are expressed as daily, weekly, monthly, or yearly consumption patterns, with cost estimates. When threshold breaches are recognized, the system triggers a recommendation engine to give targeted suggestions or alerts.

This integrated architecture integrates workflow with ANN model structure, allowing autonomous, intelligent decision-making that promotes sustainability and energy efficiency in smart environments.

4. Results and Findings

This section displays the experimental findings from a trained Artificial Neural Network (ANN) model designed to forecast electricity usage and cost in smart homes.

4.1. Model Architecture and Training Summary

The suggested Artificial Neural Network (ANN) model is built around an input layer that receives crucial predictive information, such as the day of the week, time of day, indoor temperature, and previous electricity consumption. Prior to model training, raw data was preprocessed using sklearn’s StandardScaler, which standardized all input characteristics to zero mean and unit variance. This normalization increased training efficiency by assuring equal contribution from all features, allowing for consistent and effective convergence.

The network design is made up of three hidden layers, each with the Rectified Linear Unit (ReLU) activation function to incorporate nonlinearity and increase learning capacity. The output layer consists of two neurons that are programmed to anticipate both electricity consumption and the accompanying expense estimate.

The Adam optimizer was used to compile the model, and the loss function was set to mean squared error (MSE). To enable reliable performance evaluation and reduce overfitting concerns, the dataset was randomly divided into training (80%) and validation (20%) subsets. The model was trained over 50 epochs with a batch size of 16, achieving a balance between computational efficiency and convergence dependability. Validation data was examined in real time to guide hyperparameter adjustments throughout testing.

It is vital to highlight that the experimental evaluation was carried out on a virtualized IoT simulation environment rather than on actual hardware deployments.

The simulation setup replicated common IoT household environments, including variable sensor sampling rates, simulated network latency, environmental variability (e.g., temperature and humidity fluctuations), and multiple behavioral profiles representing different user types (e.g., elderly, working individuals). These simulated settings were created to mimic the operational challenges of real-world IoT deployments and aid in the generalization of the model’s predictive performance.

As part of our future research path, the system will be implemented and evaluated on physical IoT hardware platforms to validate these findings in real-world settings, taking into account hardware-level restrictions and heterogeneous IoT device environments.

4.2. Data Processing and Prediction

After training, the model was used to make predictions on previously unseen data. The input data, which comprised a timestamp and electricity usage, was normalized with the previously fitted StandardScaler. Validation was carried out on a reserved subset of data that was not used during training, ensuring an unbiased evaluation of the model’s generalization capability.

Table 2. Model performance metrics.

Metric	Training Phase	Validation Phase
Accuracy	0.926	0.912
Sensitivity	0.915	0.895
Specificity	0.912	0.972
Miss Rate	0.085	0.105
Precision	0.996	0.999

shows the validation findings, which include measures such as accuracy, sensitivity, and specificity, indicating that the model learns meaningful patterns rather than noise and can consistently estimate energy use in new, previously encountered circumstances.

The ANN model predicted monthly electricity usage and cost, and the outputs were inverse transformed back to their original units for easier interpretation.

The ANN produced accurate estimates for weekly, monthly, and yearly electricity use and associated costs (see Figure 4).

4.3. ANN-Driven Electricity Cost Prediction and Threshold Alert System

Setting a cost threshold is an important element for enabling proactive energy management and maintaining sustainable consumption patterns. The proposed system uses an Artificial Neural Network (ANN) trained on historical power usage and cost datasets to dynamically anticipate future costs based on real-time inputs from smart appliances, accounting for temporal fluctuations in consumption behavior.

To empirically characterize cost control mechanisms, a weekly threshold was determined using the following relationship (Equation (12)):

Electricity Usage × Rate ≤ 37.5

(12)

where the prevailing electricity tariff is USD 0.16 per kilowatt-hour (kWh). This translates to a monthly expenditure ceiling of USD 150, as depicted in Figure 5 Such thresholds serve as actionable triggers, prompting user alerts when consumption approaches critical financial limits. Furthermore, multi-tiered thresholds were established to enable comprehensive monitoring across different time horizons (illustrated in Figure 5):

Monthly threshold (Equation (13)):

Electricity Usage × Rate ≤ 150

(13)

The ANN continually monitors user energy patterns and identifies abnormalities when anticipated expenses surpass predefined criteria. When such a detection occurs, the EcoZone system proposes personalized actions such as load shifting to off-peak periods, strategic appliance usage, or the implementation of alternative energy solutions.The adoption of dynamic cost thresholds not only helps to keep household budgets on track, but it also improves overall energy efficiency. By combining predictive analytics and real-time decision assistance, the system represents a significant step toward practical, data-driven sustainability in household energy management.

4.4. Model Optimization and Ensemble Framework

To address the highlighted misclassification difficulties and improve the predictive capability of the ANN-based model, a comprehensive optimization framework was created, which included loss function enhancement, advanced hyperparameter optimization, and ensemble learning methodologies. These changes were carefully integrated to improve model generality, especially for the minority group linked with high energy use trends.

4.4.1. Loss Function Optimization

In the case of class imbalance, standard mean squared error (MSE) and unweighted binary cross-entropy loss functions tend to perform poorly. As a result, we created a weighted binary cross-entropy loss function in which class weights were dynamically assigned based on class frequencies using the following formula:

$${\mathrm{w}}_{\mathrm{c}}={\displaystyle \frac{\mathrm{N}}{2.\mathrm{N}\mathrm{c}}}$$

(14)

where

• w_c = class weight;
• N = total number of samples;
• N_c = number of samples in class c.

4.4.2. Hyperparameter Tuning

A grid search algorithm was employed to systematically explore hyperparameter spaces, focusing on the following:

• Learning rate (η ∈ [0.0001; 0.01));
• Batch size (16, 32, 64);
• Number of hidden layers (2–4 layers);
• Number of neurons per layer (32, 64, 128).

Model performance was evaluated using 5-fold cross-validation, optimizing for validation loss and F1-score to balance precision and recall across classes.

4.4.3. Ensemble Learning

We further reduced model variance by using a heterogeneous ensemble of five independently trained neural networks, each with different random seeds and slightly varying hyperparameter settings. Soft voting was used to aggregate ensemble predictions, resulting in probabilistic outputs that reduced overfitting and stabilized classification decisions.

The ensemble architecture dramatically increased tolerance to data noise and variability in household energy usage patterns.

4.4.4. Results of Optimization

Following the introduction of these optimization strategies, we observed marked improvements in classification performance on the validation set:

• False positives (F.P.) reduced to 70;
• False negatives (F.N.) reduced to 20;
• Sensitivity increased to 89.5%;
• Specificity increased to 97.2%.

Detailed performance metrics before and after optimization are presented in

Table 3. Proposed model validation results (post-optimization).

Metric	Value
Predicted Positive (T.P.)	980
False Negative (F.N.)	20
Predicted Negative (F.P.)	70
True Negative (T.N.)	130

(new table summarizing pre- and post-optimization results).

These improvements substantially enhanced the model’s reliability, particularly for proactive detection of excessive energy consumption events, aligning with the system’s intended role in sustainable energy management.

4.5. Model Training and Validation Results

To evaluate the efficacy of the proposed ANN in predicting energy consumption, a curated dataset of 3000 samples was used for model training and validation.During the training phase, the dataset was divided into 2700 positive samples (showing normal energy use) and 300 negative samples (representing excessive energy consumption), as shown in

Table 4. Proposed model training data summary.

Total Samples	Expected Positive Output	Expected Negative Output
3000	2700	300

.

For validation, a subset of 1200 samples was used, with 1100 positive and 100 negative cases. Throughout the training phase, hyperparameter adjustment and 5-fold cross-validation were used to improve model performance and avoid overfitting. This strategy improved the model’s capacity to generalize to previously encountered data. Table 3 shows the detailed prediction outcomes obtained throughout validation. These stages, which included hyperparameter adjustment and 5-fold cross-validation, were necessary to ensure the model’s robustness and generalizability.

Following optimization, the ANN correctly predicted 980 true positives and 130 true negatives, demonstrating significant gains in both normal usage detection and anomaly identification. The reductions in false negatives to 20 and false positives to 70 reveal increased sensitivity to modest changes in household consumption patterns.

Additional diagnostic measures included:

• Fallout: 0.088 (training), 0.028 (validation);
• Positive Likelihood Ratio (PLR): 10.398 (training), 31.964 (validation);
• Negative Likelihood Ratio (NLR): 0.093 (training), 0.108 (validation);
• Negative Predictive Value (NPV): 0.356 (training), 0.317 (validation).

These findings highlight the enhanced generalization capabilities of the optimized ANN-based prediction model, which achieves higher precision, increased sensitivity and specificity, and improved predictive stability across energy consumption behaviors in residential situations.

5. Discussion

This study presents a practical, data-driven strategy to optimizing domestic energy consumption by combining IoT-enabled smart monitoring systems with ANNs. The EcoZone program allows users to better manage their energy consumption by using real-time sensor data and predictive modeling, which contributes to both financial savings and sustainable living habits. The predictive modeling findings showed remarkable performance, even when trained on a small dataset of 3000 samples. With an overall validation accuracy of 86.2%, a precision of 99.8%, and a low miss rate of 13.8%, the ANN demonstrated a strong capacity to anticipate energy consumption trends and detect instances of excessive usage.

However, it is important to note that the dataset used for training and validation, obtained from Kaggle, is limited in scope, comprising only a few geographic areas, building kinds, and usage patterns. These restrictions increase the danger of data bias and may limit the model’s ability to effectively forecast energy behavior across a variety of real-world situations. For example, patterns learned from a particular home or region may not apply to other settings with differing consumption practices, seasonal fluctuations, or socioeconomic profiles. As a result, while the findings are encouraging, they should not be overly generalized without further validation.

To address this, future study will use larger, more diverse datasets that include different building layouts, temperature zones, and user behaviors from multiple regions. These initiatives will improve the models’ robustness and generalizability, allowing for effective implementation in broader situations. In addition, to avoid overfitting—when a model performs well on training data but badly on unknown data—we used stringent validation approaches such as data standardization, strategic data splitting into training and validation subsets, and hyperparameter optimization. These procedures ensure that the model learns true patterns rather than noise. Future research will look into transfer learning approaches and multi-source data fusion to improve model adaptability to multiple environments and datasets, decreasing the need for substantial retraining.

The implementation of multi-tiered cost criteria (USD 37.5 weekly, USD 150 monthly, and USD 1800 annually) enabled the system to proactively inform users when their consumption approached or exceeded these levels, resulting in timely behavioral modifications.

Furthermore, the system’s capacity to distinguish between normal and excessive consumption (as indicated by true positive and true negative rates) demonstrates the ANN’s generalization ability. Notably, despite minimal instances of false positives and false negatives, the model’s high sensitivity (85.9%) and specificity (96.4%) following optimization underscore its strength in reducing misclassification errors. That said, further testing in more diverse environments is essential to confirm the reliability of these metrics.

This study’s incorporation of a specialized User Profiling Module, which captures behavioral variability among distinct user groups, represents a significant improvement. This personalization framework improves the relevance of energy recommendations by customizing them to the lifestyles of specific demographic groups such as working people, the elderly, and multi-resident families. Adaptive profiling rapidly adjusts suggestions based on changing user behaviors, encouraging long-term engagement, building trust, and boosting acceptance. Future work will focus on refining these profiles using unsupervised learning approaches and incorporating explicit user feedback to ensure that recommendations are contextually suitable and user-centric.

Compared to standard static monitoring systems, the dynamic, ANN-driven EcoZone design provides considerable benefits by including temporal variables like as utilization hours, ambient temperature, and day-specific patterns. This contextual awareness enables the delivery of personalized, practical advice, such as changing energy use to off-peak hours or utilizing alternative energy sources, thereby improving both user experience and total energy efficiency. To improve these benefits, future research will look into merging real-time meteorological data, demand-response signals, and maybe IoT-based energy storage technologies to enable more granular and effective demand control.

Finally, the findings show that significant prediction performance can be achieved even with moderately sized datasets, making the system suitable for early-stage deployments in environments without extensive historical data, such as emerging smart home projects, gated communities, and smart city initiatives. To enable a successful real-world implementation, future efforts will include installing and verifying the system on actual IoT hardware platforms, addressing hardware restrictions and heterogeneous device ecosystems, and developing scalable frameworks for general adoption. By doing so, we hope to close the gap between research prototypes and viable, large-scale energy management systems.

6. Conclusions

This study has explored the development and evaluation of an IoT-based intelligent energy management system focusing on real-time monitoring, predictive analytics, and secure user-centric design.

Key findings of the study include the following:

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With a tiny training dataset of only 3000 samples, the model achieved a high predicted accuracy (86.2%) and precision (99.8%).

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Effective early warning signals based on changing cost thresholds to encourage proactive user response.

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The ANN model performs well in discriminating between regular and high energy usage events.

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Practical and actionable user recommendations for energy conservation and cost savings.

Data security and privacy have been prioritized to ensure that the system is ready for real-world deployment. A multi-layered security system was created, which included encrypted communication (TLS 1.3), AES-256 data encryption, and a GDPR-compliant consent management interface. These enhancements ensure that users have complete control over their data while receiving intelligent, targeted recommendations.In response to RQ1, the suggested system demonstrated its ability to optimize energy use through real-time monitoring and predictive modeling. Using Artificial Neural Networks (ANN) and IoT sensor data, the system effectively predicted energy usage trends, allowing for proactive modifications and increasing energy efficiency. These effects include measurable reductions in wasteful consumption and more informed usage patterns, particularly in residential areas.

Regarding RQ2, the system’s design prioritized user interaction and interpretability, hence promoting user engagement and adoption. Although long-term behavioral change was not quantitatively tested in this study, the system’s interface and recommendation engine were designed to encourage continued user involvement and satisfaction, which are critical components for persistent behavioral shifts. Future work will incorporate longer user trials to assess these effects over time. As previously indicated, when addressing RQ3, data security and privacy were addressed as essential components. In line with RQ4, the merging of predictive modeling (by ANN) with real-time analytics resulted in dynamic energy efficiency increases. Historical usage data aided demand forecasts, while adaptive model outputs enabled intelligent scheduling and optimization. This displays the system’s ability to manage usage proactively in response to expected patterns.

In conclusion, our work contributes to the four important research directions presented in the literature review:

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Accurate data collection and preprocessing;

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Real-time, predictive decision-making;

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User-centered design and behavioral influence;

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Security and privacy are integrated.

The EcoZone framework highlights the need for intelligent energy monitoring systems in promoting sustainability, reducing energy waste, and providing customers with actionable information. By combining IoT technologies, predictive analytics, and user-centered design concepts, the solution promotes smart energy habits in modern households.

Despite these encouraging early results, real-world deployment will necessitate thorough cross-validation with a variety of datasets that represent distinct geographic, climatic, and behavioral trends. This broader testing will help to establish the model’s resilience while reducing the risk of overfitting to specific household features. Thus, while the ANN-based system has tremendous potential, we advise against overgeneralizing results until more empirical validation is undertaken in varied operational scenarios.

7. Future Work

Future research will concentrate on enhancing the system’s capabilities by incorporating larger and more diversified datasets, hence boosting the model’s robustness and application to a wide range of building types, geographic locations, and consumption behaviors. These datasets will allow for improved generalization across different meteorological conditions, architectural layouts, and user demographics.

Subsequent investigations will include complete data preprocessing procedures, including advanced feature scaling algorithms like sklearn’s StandardScaler, as well as rigorous train–validation–test splits. This organized method will help to ensure that ANNs and other predictive models learn relevant patterns.

Validation techniques will include splitting datasets into discrete subsets to evaluate model performance on previously unknown data, reducing overfitting risks and facilitating trustworthy generalization. Furthermore, cross-validation or k-fold validation will be used to provide a more consistent and impartial estimate of model performance across numerous partitions. Beyond validation, further research will incorporate hyperparameter optimization and regularization strategies to improve forecast accuracy and model stability. To improve system resilience and protect user data privacy, the integration of renewable energy forecasting modules as well as the adoption of upgraded cybersecurity measures will be prioritized.

Importantly, future work will focus on installing the EcoZone framework on physical IoT hardware platforms, enabling for system performance evaluation in real-world network situations and heterogeneous device settings. This hardware-level testing will supplement the simulation-based results, providing a more complete assessment of system applicability in operational smart home situations.

Finally, longitudinal studies will be carried out to analyze the long-term behavioral consequences on user consumption habits, as well as the environmental benefits of widespread adoption of intelligent energy management systems.