Forecasting energy demand and efficiency in a smart home environment through advanced ensemble model: Stacking and voting

A machine-learning ensemble model for predicting energy consumption in smart homes

The Internet of Things (IoT) has emerged as a transformative technology that connects the physical and digital worlds, revolutionizing how devices, objects, and systems interact. As the world becomes increasingly interconnected, IoT paves the way for the development of smart cities, homes, industries, and healthcare systems. This paper presents a comprehensive analysis of the Internet of Things and its role in shaping the smart world.The first section of this paper delves into the foundational concepts of IoT, outlining its history, principles, and key components. The discussion highlights the rapid growth of IoT devices, sensors, and communication protocols, which have enabled seamless data exchange and connectivity on a global scale. The potential benefits and challenges of IoT adoption are addressed, emphasizing the need for robust security and privacy measures to protect against potential threats. The second section explores the pivotal role of IoT in shaping smart cities. With urbanization on the rise, the need for efficient resource management, sustainable infrastructure, and enhanced citizen services becomes more pressing. IoT-driven solutions empower cities to optimize traffic flow, manage energy consumption, improve waste management, and enhance public safety through real-time data analytics and predictive modeling.In the context of smart homes, the third section investigates how IoT applications have revolutionized the way people interact with their living spaces. Smart home devices, such as smart thermostats, lighting systems, and voice-activated assistants, provide users with increased convenience and energy efficiency. However, concerns surrounding data privacy and potential vulnerabilities necessitate a discussion on the best practices for securing smart home ecosystems. The fourth section examines the impact of IoT on industries, from manufacturing to agriculture. IoT-enabled industrial processes streamline production, enhance supply chain management, and facilitate predictive maintenance, leading to increased productivity and reduced operational costs. Additionally, precision agriculture applications of IoT offer farmers real-time data on soil conditions, weather patterns, and crop health, leading to improved crop yield and resource utilization.The fifth section addresses the transformation of healthcare systems through IoT integration. From remote patient monitoring to wearable health devices, IoT has opened new avenues for personalized and proactive healthcare. This section emphasizes the potential of IoT to revolutionize healthcare delivery by facilitating timely interventions, reducing hospital readmissions, and empowering patients to take an active role in their health management.The sixth section discusses the challenges and barriers hindering the widespread adoption of IoT and the smart world. Interoperability issues, standardization, and the complexity of integrating diverse systems pose significant challenges for stakeholders. Moreover, concerns regarding data ownership, privacy, and cyber-security remain key obstacles that need to be addressed to build public trust in IoT technologies. The final section explores future trends and possibilities for IoT and the smart world. With the proliferation of 5G networks, edge computing, and artificial intelligence, the potential for IoT applications is boundless. The paper concludes by emphasizing the importance of collaboration among governments, industries, and researchers to shape a sustainable and inclusive smart world powered by IoT technologies.In summary, this paper provides a comprehensive analysis of the Internet of Things and its role in shaping the smart world. From its foundational principles to its diverse applications across various sectors, IoT has the potential to transform societies and redefine how we interact with technology. However, addressing challenges related to security, privacy, and standardization is crucial to unlock the full potential of IoT and pave the way for a truly interconnected and intelligent future.

As the Internet of Things (IoT) revolutionizes intelligent environments, energy efficiency remains a critical challenge. Traditional IoT-based energy management systems rely on predefined rule-based automation, which lacks adaptability to dynamic environmental conditions. This study proposes an Agentic AIoT system that integrates AI Agents, Large Language Models (LLMs), and Digital Twin technology to enhance IoT decision-making and optimize energy consumption in smart homes. Unlike conventional IoT frameworks, our system dynamically analyzes contextual factors—including real-time environmental conditions, user behaviors, and external data sources (e.g., weather forecasts and occupancy schedules)—to generate optimal energy-saving decisions autonomously. The proposed system consists of three key components: (1) Information Modules, which aggregate multi-source data beyond traditional IoT sensor inputs and construct a Digital Twin model for real-time environment simulation; (2) AI Agents & LLMs, which process contextual data and generate adaptive energy-saving strategies through intelligent reasoning; and (3) Automated Decision Execution, which seamlessly integrates optimized actions into IoT operations. By incorporating Digital Twin technology, the system can simulate various energy consumption scenarios, predict future power demands, and refine decision-making through AI-driven iterative learning. Experimental results demonstrate that our AIoT system achieves a 47.77% reduction in energy consumption compared to non-IoT environments while enhancing automation, adaptability, and user-centric control. Unlike conventional rule-based IoT solutions, integrating Digital Twin with LLM-powered AI Agents enables real-time context-aware decision-making and predictive energy optimization. This study contributes to intelligent IoT applications, Digital Twin-driven energy management, and AI-enhanced automation. Future research will focus on refining AI-driven autonomous decision-making, improving Digital Twin’s predictive accuracy, and further quantifying the impact of LLM-powered AIoT systems on energy efficiency. Our findings highlight the trans-formative potential of LLM-integrated AIoT with Digital Twin technology in revolutionizing energy management for intelligent environments while ensuring seamless interoperability with existing IoT infrastructures.

The effective operation of water injection pumps is vital for enhancing oil recovery in the oil and gas industry. To ensure optimal pump performance and prevent unplanned downtime, this study focused on implementing predictive maintenance strategies. We began by identifying five critical operational parameters—Seal Pressure 1, Seal Pressure 2, Vibration Data for the Drive End (VIB DE), Vibration Data for the Non-Drive End (VIB NDE), and Ampere. These parameters were monitored and analyzed to evaluate their impact on pump performance and maintenance needs. To achieve this, we applied three machine learning algorithms: Extreme Gradient Boosting (XGBoost), Light Gradient-Boosting Machine (LGBM), and Random Forest. Each algorithm was independently trained and tested on the dataset corresponding to each operational parameter. We assessed their performance using key accuracy metrics, including R squared, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). Following this, we developed an Ensemble model, combining the predictive outputs of XGBoost, LGBM, and Random Forest. The Ensemble model was then applied to the same parameters to evaluate its ability to address the limitations observed in standalone models. The results demonstrated that the Ensemble model consistently delivered superior performance, achieving lower RMSE and MAE values and higher R squared coefficients across all parameters. This study culminates in the validation of the Ensemble model as a robust and reliable approach for predictive maintenance. By leveraging the strengths of multiple algorithms, the Ensemble model offers significant improvements in accuracy and reliability, contributing to more effective maintenance systems for the oil and gas industry.

The mankind’s home has evolved as humanity itself and through history, humanity has observed the safety and comfort of their homes. The adaptation of homes to the modern times, is now involved in a technological environment and constant innovation, especially in the control of appliances, safety, pleasure, the monitoring of electrical consumption, etc. These factors have allowed the integration of homes with IoT environments in what is known as smart home. In this work, an IoT smart sensor of electrical consumption in smart homes is presented which is capable to analyze the power consumption using mobile devices through a wireless connection. The smart meter was designed using a cyber-physical system based on the ESP32 micro-controller in which an embedded Web application is executed that shows the electrical consumption of electrical devices. The aim of this technological IoT smart device is to help to detect the phantom consumption of electrical energy in a smart home environment in order to promote the energy saving. The results obtained show that this kind of IoT technology contributes to decrease the economic expense for home owners and also allows to observe and analyze the electrical energy consumption of different electrical devices using mobile devices.

Due to the rapid adoption of renewable energy sources and the increasing complexity of residential electricity consumption, there is a growing need for intelligent, scalable, and context-aware energy management systems in smart homes. This paper presents an artificial intelligence (AI)-based model that integrates high-resolution household power forecasting with dynamic demand response (DR) simulations to optimize energy consumption, enhance grid stability, and support sustainable energy transitions. Using the publicly available UCI Smart Home Energy dataset, a seven-step framework was implemented, including data preprocessing, exploratory data analysis (EDA), feature engineering, and the development of various predictive models—linear regression (LR), random forest regressor (RFR), support vector regression (SVR), k-nearest neighbors (k-NN), and long short-term memory (LSTM) neural networks. Each model was evaluated under seven DR strategies to capture temporal dependencies and nonlinear load behaviors: Peak Clipping, Valley Filling, Load Shifting, Load Leveling, Time-of-Use (ToU) Optimization, Price-Based Control, and Behavioral DR. Model performance was assessed using the mean absolute error (MAE), root mean squared error (RMSE), and the coefficient of determination ( R ²). Results revealed that the LSTM model achieved the highest accuracy, with the lowest MAE (18.95 Wh), lowest RMSE (24.83 Wh), and highest R ² (0.94) under the Price-Based DR strategy. Random forest emerged as the most effective traditional model, yielding an MAE of 25.13 Wh and R ² of 0.89, particularly for Behavioral and ToU-based DR strategies. The SVR and k-NN models provided moderate accuracy, while LR performed poorly, underscoring its limitation in modeling nonlinear and dynamic energy patterns. DR simulations indicated that Price-Based, Behavioral, and ToU Optimization strategies achieved the best alignment between household loads and grid requirements while maintaining user flexibility. Visualization tools such as heatmaps, grouped bar plots, and R ² comparisons further confirmed the superior temporal modeling capability of deep learning (DL) methods. Overall, the proposed framework offers a scalable and interpretable AI-driven platform that integrates load forecasting with DR evaluation, demonstrating that DL—particularly LSTM—can substantially enhance forecasting accuracy, enable smarter DR programs, and promote sustainable energy management in smart homes.

Accurate prediction of energy consumption in smart homes is essential for optimizing energy usage, reducing operational costs, and enhancing occupant comfort. Traditional statistical and machine learning models often struggle to capture the complex spatiotemporal patterns inherent in energy usage data. To address this limitation, this study proposes a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model for forecasting energy consumption in smart homes, combining the spatial feature extraction strengths of Convolutional Neural Networks (CNNs) with the temporal learning capabilities of Long Short-Term Memory (LSTM) networks. The model demonstrated high predictive accuracy and generalization, as reflected by the evaluation metrics: Mean Absolute Error (MAE) = 0.0325, Mean Squared Error (MSE) = 0.0031, Root Mean Squared Error (RMSE) = 0.0557, and Mean Absolute Percentage Error (MAPE) = 0.2054. However, as the model was trained on synthetic data, further validation using real-world energy consumption datasets is necessary to assess its practical applicability and robustness.

The Internet of things is a new technological revolution following the computer and Internet. It aims to connect all physical objects existing in the world and forms a network with everything. In recent years, smart home gradually enters into our life. Smart home uses the Internet of things technology to connect all kinds of devices in the home, to achieve a smart home environment. Although the development of smart home has brought a qualitative leap to people’s life, there are many problems in security. Privacy security is one of the challenges to the smart home environment. Attackers can intrude various smart devices in the smart home environment, to achieve the purpose of stealing users’ personal information and privacy. Among these devices, smart cameras are the most intruded frequently. Since many cameras are installed in users’ homes to achieve real-time monitoring of the environment, but the existence of these cameras provides a channel to get information for attackers. In recent years, the leak of video privacy is emerging in an endless stream. According to the researches about privacy protection, this paper proposes a new scheme to selectively encrypt the video captured by the cameras through machine learning technology, so as to protect the personal privacy of users and improve the security of the smart home environment.

Internet of Thing (IoT) is going to make such a world where physical things (smart home appliances, and smart watches etc.) revolutionized the information networks and services providing systems which provide innovative and smart services to human. With smart home technology, our living area is becoming more comfortable and convenient. Smart home technology provides automated, intelligent, smart, innovative and ubiquitous services to residential users through Information Communication Technology (ICT). Due to internet-connected, dynamic and heterogeneous nature of smart home environment creates new security, authentication and privacy challenges. In this paper, we investigate security attacks in smart home and evaluate their impact on the overall system security. We identified security requirements and solutions in the smart home environment. Based on several scenarios, we suggest to set security goals for the smart home environment. Based on historical data, we forecast security attacks (like malware, virus etc.) and estimated that how many attacks are expected to be launched in coming years.

Intelligent energy management in off-grid smart buildings with energy interaction

Smart homes are an emerging paradigm of Internet of Things (IoT) in which users can remotely control various home devices via the internet anytime and anywhere. However, smart home environments are vulnerable to security attacks because an attacker can inject, insert, intercept, delete, and modify transmitted messages over an insecure channel. Thus, secure and lightweight authentication protocols are essential to ensure useful services in smart home environments. In 2021, Kaur and Kumar presented a two-factor based user authentication protocol for smart homes using elliptic curve cryptosystems (ECC). Unfortunately, we demonstrate that their scheme cannot resist security attacks such as impersonation and session key disclosure attacks, and also ensure secure user authentication. Moreover, their scheme is not suitable in smart home environments because it utilizes public-key cryptosystems such as ECC. Hence, we design a secure and lightweight three-factor based privacy-preserving authentication scheme for IoT-enabled smart home environments to overcome the security problems of Kaur and Kumar’s protocol. We prove the security of the proposed scheme by using informal and formal security analyses such as the ROR model and AVISPA simulation. In addition, we compare the performance and security features between the proposed scheme and related schemes. The proposed scheme better provides security and efficiency compared with the previous schemes and is more suitable than previous schemes for IoT-enabled smart home environments.

Ensemble multiclassification model for aquatic toxicity of organic compounds

SummarySepsis-associated acute kidney injury (S-AKI) is very common and early prediction is beneficial. This study aiming to develop an accurate ensemble model to predict the risk of S-AKI based on easily available clinical information. Patients with sepsis from the United States (US) database Medical Information Mart for Intensive Care-IV were used as a modeling cohort to predict the occurrence of AKI by combining Support Vector Machine, Random Forest, Neural Network, and Extreme Gradient Boost as four first-level learners via stacking algorithm. The external validation databases were the eICU Collaborative Research Database from US and Critical Care Database comprising infection patients at Zigong Fourth People’s Hospital from China, whose AUROC values for the ensemble model 48–12 h before the onset of AKI were 0.774–0.788 and 0.756–0.813, respectively. In this study, an ensemble model for early prediction of S-AKI onset was developed and it demonstrated good performance in multicenter external datasets.

With the emergence of the massive educational data, education data mining techniques have extensively drawn considerable interest from scholars to explore the relationship between students’ achievements and other factors. In this study, the data set about the students’ achievements of Portuguese in two secondary education schools in Portugal is selected for education data mining, which involves the personal information, social and school related factors. To analyze the relationship between the students' achievements and other factors, this study proposed an ensemble model based on weighted voting for predicting the students’ achievements of Portuguese in the final period. First, the raw data is preprocessed using some basic methods, including dummy coding, correlation analysis, standardization, and normalization. Second, the isolation forest algorithm-based outlier adaption is applied to deal with the data set to enhance the robustness of the ensemble model. Finally, two base classifiers, i.e. gradient boosting decision tree and extreme gradient boosting, are integrated to form the ensemble model. The experiments are presented for verifying the superiority of the proposed model by comparing with five base classifiers, including gradient boosting decision tree, adaptive boosting, extreme gradient boosting, random forest, and decision tree. The experimental results demonstrate that the ensemble model performs better than other base classifiers in classification, and prove the validity of the outlier adaption based on isolation forest algorithm.

Artificial Intelligence (AI), Data Science (DS) and the Internet of Things (IoT) are vast networks of many intelligent devices connected to each other and/or to other internet-connected objects via sensors, software and other embedded technologies. They make use of large data sources and offer easy identification of trends and patterns, improved automation, continuous development, easy processing of multidimensional data and low computing costs. The development of artificial intelligence and data science in renewable energy management has the potential to manage energy production sources and ensure the energy efficiency of installations. This chapter explores different AI application techniques based on deep learning in the areas of solar and wind power generation, smart grid management, and energy consumption in smart residential buildings. It shows that using AI to estimate prediction performance and uncertainty is an optimal model for solar and wind power generation, smart grid management and energy consumption in smart homes.