Home Energy Consumption Research Articles

Optimized Smart Home Energy Management System: Reducing Grid Consumption and Costs through Real-Time Pricing and Hybrid Architecture

Utility authorities utilize various methods to promote end-user energy conservation, including higher tariff rates and demand response (DR) strategies. This paper investigates an Optimized Smart Home Energy Management System (OSHEMS) designed to minimize grid dependence and energy bills while ensuring reliable load delivery. A hybrid architecture prototype was implemented, integrating a photovoltaic (PV) array, battery storage, and the electrical grid. The system combines Maximum Power Point Tracking (MPPT) solar chargers and Pure Sine Wave (PSW) inverters for efficient energy management. The Home Energy Management Whale Optimization Algorithm (HEMWOA) was employed to optimize energy usage and achieve cost reduction while enhancing user comfort. Real-time pricing (RTP) tariffs incentivizing flexible energy consumption during peak hours were incorporated. OSHEMS manages, schedules, and monitors energy sources and appliances, determining the optimal consumption mix. Experimental results demonstrate a significant decrease in grid reliance (46.6%) and energy costs (57.7%) compared to non-scheduling scenarios. These findings highlight the potential of OSHEMS in promoting sustainable and cost-effective energy consumption in smart homes.

Adoption of smart energy apps and household energy practices: empirical insights from Norway

ABSTRACT This study examines the adoption and usage patterns of household energy apps among a sample of Norwegian households to explore their potential in visualising and controlling home energy consumption and influencing sustainable energy practices. Through a questionnaire survey of 150 respondents using energy apps, this study gathers empirical insights into adoption motivations, user perceptions and factors associated with self-reported changes in household energy practices. Findings indicate that app-related factors, such as perceived app insights applicability, ease of use, and appealing user interface, are more positively associated with self-reported changes in daily energy habits than demographic factors. This research underscores the importance of prioritising user experience and data security in app development to foster sustainable energy practices. It also offers practical recommendations for targeted engagement strategies and policy interventions to promote the wider adoption of smart energy apps and maximise their potential to facilitate household energy transitions.

Building an IoB ecosystem for influencing energy consumption in smart cities

Introduction: The Internet of Behaviors (IoB) represents a paradigm shift in integrating digital technologies with human behaviors, offering unprecedented insights and opportunities across various domains. This research paper explores the transformative potential of IoB and presents an innovative IoB framework applied to an energy consumption scenario. Objective: We offer an innovative IoB ecosystem aimed at heightening citizens' responsibility and awareness regarding home energy consumption in smart cities.Methodology: We propose a framework that elicits behavioral insights by leveraging smart meter data, clusters citizens based on similar energy consumption patterns using K-Means into groups, applies an LSTM-based prediction model to forecast their future energy consumption, and influences their behavior through a continuous personal reflection loop. Moreover, to foster trust, XAI principles are also integrated into our framework to ensure citizens comprehend and trust the IoB model's results.Results: Our proposed LSTM-based prediction model achieved, on the smart meters’ dataset, high-performance results, an R² value equal to 0.986, a root mean squared error of 0.492 and a mean squared error equal to 0.242. Conclusions: This paper presents how we can leverage the IoB and XAI into the energy sector. However, the IoB's potential is not restricted to a certain domain. It has a revolutionary influence across sectors, with the power sector standing out as one of the domains where the IoB has the potential to alter social practices

Advances in fuzzy control systems for energy management in smart homes

The drive towards energy efficiency and sustainability has spurred technological innovations for managing energy consumption in residences, with fuzzy control systems standing out. These systems, based on fuzzy logic, offer adaptive and intuitive solutions to optimize energy use in smart homes, dealing with uncertainties and imprecisions in environmental conditions and occupant preferences. Fuzzy logic allows for refined control of devices such as lighting, heating, ventilation, and air conditioning (HVAC), adjusting in real-time to variations in conditions and user behaviors. Recent studies have demonstrated the effectiveness of these systems in various applications. Kontogiannis, Bargiotas, and Daskalopulu (2021) developed a fuzzy control system to recommend optimal energy consumption values based on environmental data, using the Mamdani approach and decision tree linearization. Keshtkar and Arzanpour (2017) introduced an autonomous thermostat combining supervised learning with wireless sensors and dynamic electricity pricing, adjusting temperatures and maintaining user comfort. Ain et al. (2018) proposed a Fuzzy Inference System that incorporates humidity and internal temperature variations to optimize energy consumption without sacrificing comfort. Additionally, Khalid et al. (2019) presented an energy management controller for smart grids using fuzzy logic and heuristic optimization techniques, improving load management and reducing costs and consumption. Collotta and Pau (2015) explored the integration of IoT technology with fuzzy-based systems using Bluetooth Low Energy (BLE) to manage energy consumption in home automation networks. These studies highlight that fuzzy control systems are crucial for optimizing energy consumption in smart homes, balancing efficiency and comfort, with ongoing advancements promising continuous improvements in energy management.

The Impact of Smart Home Technologies on Energy Efficiency, Cost Savings, and Environmental Benefits

The results of this study addressed the effectiveness of smart home technologies in terms of energy consumption, cost reduction, and environmental advantages in Nigerian homes. Given the growing energy requirements and concern for the environment, assessing the efficiency of such technologies is critical for socioeconomic development. We collected data using a combination of surveys and statistical analysis in five major Nigerian cities: Lagos, Abuja, Kano, Port Harcourt, and Enugu. We evaluated smart home technologies such as smart thermostats, lighting systems, appliances, plugs, and energy monitoring systems based on their energy savings (%), monthly cost savings (NGN), kilogram of CO2 emissions cut, and percent adoption. We used descriptive analysis and regression modeling to analyze the data collected from the respondents. The introduction of smart technologies reduced general energy use by fifteen to twenty-five percent, with smart appliances recording the highest reduction of up to 25%. This feature demonstrated how monthly cost savings varied depending on the technology and geographical location, resulting in significant increases of up to 4000 NGN per household, each household saw a reduction in CO2 emissions by 500 kg to 700 kg annually, resulting in significant environmental benefits. According to the studies, smart home technologies can help promote energy conservation and therefore reduce CO2 emissions in Nigerian households. However, there are still widely recognized obstacles, like high initial costs and a low level of awareness. offering financial incentives, improving Information Promotion of existing policies and overhauling the distribution systems to increase the pace of uptake and impact returns.

Evaluation of Constructive Parameters to Minimize the Effects of Solar Radiation in Households with Tropical Climate

In tropical climates, the energy consumption of buildings is strongly influenced by solar radiation. However, more research needs to be done on how specific design parameters can mitigate the effects of solar radiation in tropical homes. This study uses simulations in EnergyPlus to evaluate how orientation, roof solar absorptance, overhang depth, glass type, and wall type affect energy consumption in a typical middle-income home in a tropical climate. The results highlight the benefits of adequate solar absorbance and correct orientation. Incorporating insulation into exterior walls does not offer significant improvements in the energy performance of the building. Key words. Building energy simulation, Tropical climate, parametric analysis.

Optimizing energy consumption in smart homes using GA-LSTM

The need to optimize energy consumption arises from the inadequate energy supply many homes face. However, to optimize energy consumption in a home, one must be equipped with the knowledge of the energy consumption rate and energy supply rate in the home. This paper proposed the use of a Long Short-Term Memory (LSTM) model optimized by Genetic Algorithm (GA) to optimize the energy consumption in a smart home. The model was designed using 8 input variables, which were observed weather information of a given region over a span of 350 days. The data set was split into a training data set and a test data set in the ratio 4:1. Both the baseline LSTM model and our proposed GA-LSTM model was used to train and test the data. The results of the baseline LSTM model showed a Mean Average Percentage Error (MAPE) of 0.15 and a Root Mean Square Error (RMSE) of 0.0007, while our proposed GA-LSTM recorded an MAPE of 0.07 and RMSE of 0.0004. The implication of these results is a better performance of our proposed GA-LSTM model over the baseline LSTM model. This paper demonstrated how the Genetic Algorithm was able to optimize the baseline LSTM model by selecting the best weights during the LSTM training process. The findings of this paper are recommended for smart homes having the capability to monitor energy usage of installed electrical appliances and the ability to predict to weather condition of the region the home is sited.

Enhancing smart grid electricity prediction with the fusion of intelligent modeling and XAI integration

This study examines the vital role of accurate load forecasting in the energy planning of smart cities. It introduces a hybrid approach that uses machine learning (ML) to forecast electricity usage in homes, improving accuracy through the extraction of correlated features. The accuracy of predictions is assessed using loss functions and the root mean square error (RMSE). In response to increasing interest in explainable artificial intelligence (XAI), this paper proposes a framework for predicting energy consumption in smart homes. This user-friendly approach helps users understand their energy consumption patterns by employing shapley additive explanations (SHAP) techniques to provide clear explanations. The research uses gradient boosting and long short-term memory neural networks to forecast energy usage. In the context of sustainable urban development, it emphasizes the importance of conserving energy in homes. The paper explores AI and ML methods for predicting residential energy use, aiming to make socially meaningful impacts. It highlights the need to understand the factors affecting predictions to improve the accountability, reliability, and justification of decisions in energy optimization. Explainable AI techniques are used to gain insights into the prediction models and identify factors influencing household energy consumption. This research aids in decision-making processes related to electricity forecasting, advancing discussions on intelligent decision-making in power management, especially in smart grids and sustainable urban development.

House building techniques for achieving sustainable development goals

AbstractThis study aims to design and build a pilot home according to the United Nations sustainable development goals (UN SDGs). The research methodology is a nature‐oriented housing policy to promote renewable energy consumption in homes for cleaner environments. This study analyzes the structural elements of traditional homes in the eastern south of Iran. This study examines the UN SDGs in planning and designing the model to find the advantages of nature‐oriented constructions compared to the current harmful development projects damaging nature, environments, ecosystems, and the earth. The building model presented in this paper is in harmony with the UN SDGs. The model provides an architectural and planning procedure to prevent the earth from damaging fossil consumption‐built environments in the world. Calculations show that the housing pilot model saves USD 3600 in energy consumption per household on average. The outcomes of this study prove the hypothesis that the use of renewable energy in homes is beneficial economically and environmentally. This study implicates housing policies in shifting planning and design methods toward UN SDGs. Policies are applicable globally in the housing programs.

Smart Homes, Smart Choices: Using Big Data to Boost Energy Efficiency and Environmental Sustainability

The major contributor to home energy consumption is home appliances, comprising about two-thirds of the total energy used. Analyzing electricity usage at the appliance level is crucial due to varying energy consumption levels among different appliances. This analysis allows individuals to either upgrade or minimize the use of inefficient appliances. To prioritize energy conservation, reducing the usage of energy-inefficient household appliances is imperative. Smart meters in a smart home scenario record electricity consumption data. The collected and monitored data from smart meters integrate with Big Data, playing a substantial role in this domain. Our proposed model optimizes electricity usage by identifying and categorizing household energy-inefficient appliances with a novel transition detection algorithm and appliance detection model. Furthermore, Utilizing Appliance-Level power consumption data from the UK-DALE dataset and One vs Rest clustering techniques, the proposed model effectively identifies devices with higher energy consumption. Additionally, the result analysis of the proposed model highlights the advantages of transitioning to energy-efficient devices, reducing both power consumption and financial wastage.

HVAC energy cost minimization in smart grids: A cloud-based demand side management approach with game theory optimization and deep learning

In this paper, we present a novel cloud-based demand side management (DSM) optimization approach for the cost reduction of energy usage in heating, ventilation and air conditioning (HVAC) systems in residential homes at the district level. The proposed approach achieves optimization through scheduling of HVAC energy usage within permissible bounds set by house users. House smart home energy management (SHEM) devices are connected to the utility/aggregator via a dedicated communication network that is used to enable DSM. Each house SHEM can predict its own HVAC energy usage for the next 24 h using minimalistic deep learning (DL) prediction models. These predictions are communicated to the aggregator, which will then do day ahead optimizations using the proposed game theory (GT) algorithm. The GT model captures the interaction between aggregator and customers and identifies a solution to the GT problem that translates into HVAC energy peak shifting and peak reduction achieved by rescheduling HVAC energy usage. The found solution is communicated by the aggregator to houses SHEM devices in the form of offers via DSM signals. If customers’ SHEM devices accept the offer, then energy cost reduction will be achieved. To validate the proposed algorithm, we conduct extensive simulations with a custom simulation tool based on GridLab-D tool, which is integrated with DL prediction models and optimization libraries. Results show that HVAC energy cost can be reduced by up to 36% while indirectly also reducing the peak-to-average (PAR) and the aggregated net load by up to 9.97%.

Model for Identification of Electrical Appliance and Determination of Patterns Using High-Resolution Wireless Sensor NETWORK for the Efficient Home Energy Consumption Based on Deep Learning

The growing demand for electricity and the constant increase in electricity rates have intensified the interest of residential and non-residential energy consumers to reduce their energy consumption. The introduction of non-conventional renewable energies (photovoltaic and wind, in the residential case) demands new proposals to obtain a home energy management system (HEMS), which allows reducing the use of electrical energy. This article incorporates artificial intelligence techniques to demand response, allowing control, switching, turning on and off of appliances, modifying and reducing consumption, and achieving improvements in the quality of life in the home. In addition, an architecture based on a smart socket and an artificial intelligence model that recognizes the consumption of electrical appliances in high resolution (sampling every 10 s) is proposed. The system uses the Wi-Fi communication protocol, ensuring that the smart sockets wirelessly provide the data obtained to the public cloud. The use of Deep Learning allows us to obtain a central control model of the home, which, when interconnected to the smart electrical distribution networks of companies, could generate a positive impact on the environmental effects and CO2 reduction.

An Intelligent Hybrid Machine Learning Model for Sustainable Forecasting of Home Energy Demand and Electricity Price

Home energy systems (HESs) face challenges, including high energy costs, peak load impact, and reliability issues associated with grid connections. To address these challenges, homeowners can implement solutions such as energy management, renewable resources, and energy storage technologies. Understanding consumption patterns and optimizing HES operations are crucial for effective energy management. As a primary step, addressing these concerns requires an efficient forecasting tool to predict home energy demand and electricity prices. Due to the complexity of big data, and uncertainties involved in forecasting, machine learning (ML) methods are necessary. In this study, we develop a hybrid machine learning approach, utilizing one year of data on home energy demand and prices to address the challenge of forecasting home energy consumption. A comprehensive comparison of different deep and non-deep ML models highlights the superiority of the proposed hybrid approach. The performance of these models, measured using metrics such as RMSE, MAE, R2, and RT (running time), are compared. Finally, an optimized hybrid XGBoost (XGB) ML model that combines price and energy demand forecasting is introduced. The proposed ML method’s parameters are optimally determined using Particle Swarm Optimization. The hybrid ML model’s performance is evaluated in predicting both energy demand and consumption prices using historical data from diverse households with various features and consumption patterns. The results indicate that the hybrid ML model achieves accurate predictions for energy consumption and prices, with improvements in RMSE (up to 36.6%), MAE (up to 36.8%), and R2 (up to 3.9), as compared to conventional ML methods. This research contributes to sustainable energy practices by providing an effective tool for forecasting energy consumption and associated costs in the dynamic landscape of home energy systems.

Artificial neural network-based data imputation for handling anomalous energy consumption readings in smart homes

Smart homes are at the forefront of sustainable living, utilizing advanced monitoring systems to optimize energy consumption. However, these systems frequently encounter issues with anomalous data such as missing data, redundant data, and outliers data which can undermine their effectiveness. In this paper, an artificial neural network (ANN)-based approach for data imputation is specifically designed to deal with the anomalies in smart home energy consumption datasets. Our research harnesses the power of ANNs to model intricate patterns within energy consumption data, enabling the accurate imputation of missing values while detecting and rectifying anomalous data. This approach not only enhances the completeness of the data but also augments its overall quality, ensuring more reliable results. To evaluate the effectiveness of our ANN-based imputation method, comprehensive experiments were conducted using real-world smart home energy consumption datasets. Our findings demonstrate that this approach outperforms traditional imputation techniques like mean imputation and median imputation in terms of accuracy. Furthermore, it showcases adaptability to diverse smart home scenarios and datasets, making it a versatile solution for improving data quality. In conclusion, this study introduces an advanced data imputation technique based on ANNs, tailor-made for addressing anomalies in smart home energy consumption data. Beyond merely filling data gaps, this approach elevates the dataset's reliability and completeness, thereby facilitating a more precise analysis of energy consumption and supporting informed decision-making in the context of smart homes and sustainable energy management. Ultimately, the proposed method has the potential to contribute considerably to the ongoing evolution of smart home technologies and energy conservation efforts.

Optimizing energy consumption in smart homes: Load scheduling approaches

AbstractRising fuel prices, global warming, and environmental damage are leading to increased demand for rooftop solar energy systems connected to the power grid. The development of smart grids, modern metering systems, and energy management could promote energy conservation in households. In this study, two different meta‐heuristic optimization techniques were employed to schedule the shiftable load in suitable hours for decreasing electricity costs and minimizing peak to average ratio in a smart home while maintaining optimum user comfort. For power generation and storing energy, a grid‐connected residential load with rooftop solar panels, a battery, and an inverter is considered. First, the problem is theoretically described using a load model and an objective function, with the primary goal of reducing power costs by moving the time of usage for certain home appliances. Simulations validate the proposed strategies, which effectively reduce power costs by 4.5% by shifting the time of use, with both optimization algorithms showing similar output. The residential electricity cost before optimization was 507.12 BDT/day, which decreased to 484.33 BDT/day after optimization without compromising load turn‐off.

Analysis of the impact of energy consumption data visualization using augmented reality on energy consumption and indoor environment quality

This study proposes augmented reality (AR) as one of the methods to effectively reduce energy consumption in smart homes while maintaining an acceptable level of indoor environmental quality (IEQ). The experiment was conducted for a total of 90 min in an environment chamber where IEQ could be controlled, and participants were able to adjust the IEQ as desired throughout the duration of the experiment. Targeting a total of 21 participants, data on energy consumption, IEQ, and psychological response were collected under two conditions: (i) without providing energy consumption data through AR software, and (ii) with providing energy consumption data. The results show that AR software can positively impact energy consumption reduction. Also, reducing energy consumption through AR software did not significantly affect the psychological response to the subjects’ IEQ, suggesting that AR software can be used as an effective tool for reducing energy consumption without compromising occupant comfort concerning IEQ. In other words, when occupants are satisfied with indoor environments and are aware of their energy consumption, it means that energy consumption can be reduced for economic benefits. The results of this study suggest potential economic and social benefits from reducing building energy consumption.

A Smart, Cloud-Enabled Internet of Things System that Optimizes Home Energy Distribution, Safety, and Consumption

This paper focused ona smart, cloud-enabled internet of things system that optimizes home energy distribution, safety, and consumption. The materials used in this paper include Servo motor, lamp holder, energy bulbs, 13A socket, rectifier, jumper wires, electric cables, Blynk interface, motion sensor, temperature/humidity sensor, Esp32, and smoker sensor. The development of the system's code, which was essential for optimizing the system's operations, was carried out using the Arduino Integrated Development Environment. A testbed comprising of the designed work was done in house located in Anambra State, Nigeria over a six-month period, where eight appliances were used to gather used energy by deploying smart plugs, and a smart meter that measures the major energy load of the house. Results show that both implementations collected, stored, and controlled the energy according to the smart, cloud-enabled internet of things system. This work showed that energy providers, and technology developers implement a cloud-enabled Internet of Things system for the best possible household energy usage, safety, and distribution. This technology can significantly increase energy efficiency, save costs, and improve home safety to 100%.

Optimization of energy consumption in smart homes using firefly algorithm and deep neural networks

Electronic gadget advancements have increased the demand for IoT-based smart homes as the number of connected devices grows rapidly. The most prevalent connected electronic devices are smart environments in houses, grids, structures, and metropolises. Smart grid technology advancements have enabled smart structures to cover every nanosecond of energy use. The problem with smart, intelligent operations is that they use a lot more energy than traditional ones. Because of the growing growth of smart cities and houses, there is an increasing demand for efficient resource management. Energy is a valuable resource with a high unit cost. Consequently, authors are endeavoring to decrease energy usage, specifically in smart urban areas, while simultaneously ensuring a consistent terrain. The objective of this study is to enhance energy efficiency in intelligent buildings for both homes and businesses. For the comfort indicator ("thermal, visual, and air quality"), three parameters are used: temperature, illumination, and CO2. A hybrid rule-based Deep Neural Network (DNN) and Fire Fly (FF) algorithm are used to read the sensor parameters and to operate the comfort indication, as well as optimize energy consumption, respectively. The anticipated user attributes contributed to the system's enhanced performance in terms of the ease of use of the smart system and its energy usage. When compared to traditional approaches in expressions of Multi View with 98.23%, convolutional neural network (CNN) with 99.17%, and traffic automatic vehicle (AV) with 98.14%, the activities of the contributed approach are negligibly commanding.

Nature-inspired solutions for energy sustainability using novel optimization methods.

This research centres on developing a Home Electricity Management (HEM) system, a pivotal component within the modern supply chain for home electrical power. The system optimizes the scheduling of intelligent home gadgets through advanced meta-heuristics, specifically the Social Spider Algorithm (SSA) and Strawberry Algorithm (SWA), to efficiently manage home energy consumption. Within the supply chain context, HEM acts as a crucial link in the distribution and utilization of electricity within households, akin to optimizing resource allocation and demand balancing within a supply chain for efficient operation and cost-effectiveness. Simulations and comparisons demonstrate that SWA excels in cost savings, while SSA is more effective in reducing peak-to-average power ratios. The proposed solution reduces costs for residences by up to 3.5 percent, highlighting the potential for significant cost savings and efficiency improvements within the home electricity supply chain. It also surpasses existing cost and Peak Average (PAR) ratio meta-heuristics, indicating superior performance within the overall energy supply and consumption framework. Moreover, implementing the HEM system contributes to reducing carbon emissions, aligning with sustainability goals in the energy supply chain. It promotes energy efficiency, integrates renewable sources, and facilitates demand response, mirroring the emphasis on sustainability in supply chain practices. Overall, this research offers a practical and sustainable approach to home energy management, bringing substantial cost savings and environmental benefits to the modern supply chain for residential electricity.

Enhancing energy efficiency in a smart home through window-based support vector regression for energy consumption prediction

Efficient energy management is greatly facilitated by accurately predicting energy consumption in a smart home, benefiting both consumers and utilities alike. The conventional forecasting techniques rely on pre-trained statistical models built upon extensive historical data, which may experience performance degradation due to the dynamic nature of power load demands. To address this limitation, this study proposes a novel approach employing Window-based Support Vector Regression (WSVR) to accurately estimate energy requirements from a smart grid within a smart home. The dataset utilized for this research is sourced from Pecan Street in Texas, USA. To assess the efficacy of the proposed model, it is compared to several other time series data prediction models, including ARIMA, Holt Winter's, Linear Regression, Support Vector Machine, and Support Vector Regression. The performance of each model is evaluated, and the results are thoroughly examined and discussed.