Energy Consumption Of Appliances Research Articles

Smart System for Reducing Standby Energy Consumption in Residential Appliances

Residential consumption represents one of the most important percentages of total electricity consumption. A considerable number of household appliances consume energy even when they are not in operation, i.e., they are in the so-called standby state, thus producing additional costs, which become significant over time. In this context, one method to solve this problem is to develop a smart system capable of severing the power connection to devices in standby mode, thereby conserving energy and reducing the energy costs. The first step in the design of this system consists of the identification and accurate measurement of the standby state, which was carried out for three of the most common household appliances. Then, by using an ESP32 microcontroller, a system was designed to manage the operation of a relay module based on the current consumption of the connected equipment. Control over the system was achieved through a web application that works across all devices equipped with a web browser, offering functionalities to adjust current value time delays and to manually switch the system on or off. Finally, the deployment of this system across the three appliances studied led to a reduction in the energy consumption in standby mode of 26.68 kWh per month.

Enhancing non-intrusive load monitoring with weather and calendar feature integration in DAE

The construction of modern power system is key to achieving dual carbon goals, where non-intrusive load monitoring (NILM) plays a vital role in enhancing energy utilization efficiency and energy management. For example, to enable prosumers to better understand the extent of their flexible loads for demand response and peer-to-peer trading, it is essential to be aware of the types and states of loads using the method of NILM. To improve the predictive accuracy and implementation effectiveness of NILM technology, this paper proposes a novel NILM method integrating meteorological and calendar features. It delves deeply into the close connection between external factors such as temperature, precipitation, wind speed, and holidays, and the energy consumption of electrical appliances, constructing additional associative mappings in the training of the Denoising Autoencoder (DAE) model. Test results on the UK-DALE public dataset show that the NILM method proposed in this paper has significant advantages over traditional NILM methods that consider only single-dimensional electrical data features, in terms of load pattern recognition and accuracy in load energy consumption monitoring. This confirms the potential of multi-dimensional feature fusion technology in the application of NILM.

Comparative Analysis of Machine Learning Techniques for Non-Intrusive Load Monitoring

Non-intrusive load monitoring (NILM) has emerged as a pivotal technology in energy management applications by enabling precise monitoring of individual appliance energy consumption without the requirements of intrusive sensors or smart meters. In this technique, the load disaggregation for the individual device is accrued by the recognition of their current signals by employing machine learning (ML) methods. This research paper conducts a comprehensive comparative analysis of various ML techniques applied to NILM, aiming to identify the most effective methodologies for accurate load disaggregation. The study employs a diverse dataset comprising high-resolution electricity consumption data collected from an Estonian household. The ML algorithms, including deep neural networks based on long short-term memory networks (LSTM), extreme gradient boost (XgBoost), logistic regression (LR), and dynamic time warping with K-nearest neighbor (DTW-KNN) are implemented and evaluated for their performance in load disaggregation. Key evaluation metrics such as accuracy, precision, recall, and F1 score are utilized to assess the effectiveness of each technique in capturing the nuanced energy consumption patterns of diverse appliances. Results indicate that the XgBoost-based model demonstrates superior performance in accurately identifying and disaggregating individual loads from aggregated energy consumption data. Insights derived from this research contribute to the optimization of NILM techniques for real-world applications, facilitating enhanced energy efficiency and informed decision-making in smart grid environments.

The Importance of AI-Enabled Internet of everything Services for Smart Home Management

Abstract Smart home applications are ubiquitous and have become popular because of the overwhelming use of the Internet of Things (IoT) and artificial intelligence (AI). Living smart with automation and integrated AI-IoT has become more affordable as home automation technologies have matured. In addition, the Internet of Everything (IoE), which involves the interconnection of humans, businesses, and intelligent objects, has the potential to reshape various industries. However, the rising energy cost and demand have led numerous organizations to determine smart ways to monitor, control, and save energy. Hence, this study suggests AI-Enabled Internet of Everything Services (AI-IoES) for efficient smart home energy management. The data have been taken from the Open Smart Home IoT//Energy Dataset for analyzing the energy consumption of home appliances. This paper presents an IoT sensor for energy management to track and control specific loads in smart homes. The deep neural network (DNN) is built for secure demand-side management (DSM) in an IoT-assisted smart grid and trained on the extracted feature from electricity consumption information gathered using an IoT sensor. The system is established with real-time monitoring and a user interface for remote control and access. The experimental outcome demonstrates that the suggested AI-IoES system increases the user experience by 98.9%, energy efficiency ratio (EER) by 97.8%, and accuracy ratio by 97.2%, and reduces energy consumption by 19.2% compared with other existing methods.

Multi-agent-based decentralized residential energy management using Deep Reinforcement Learning

In smart grid, energy consumption has grown exponentially in residential houses, which necessitates the adoption of demand response management. To alleviate and handle the energy management in residential houses, an efficient residential energy management (REM) system can be employed to regulate the energy consumption of appliances for several energy loads such as non-shiftable, shiftable, and controllable loads. Many researchers have focused on the REM using machine learning and deep learning techniques which is not able to provide secure and optimal energy management procedure. Thus, in this paper, a multi-agent-based decentralized REM, i.e., MD-REM approach is proposed using Deep Reinforcement Learning (DRL) with the utilization of blockchain. Furthermore, The combinatorial model DQN, i.e., Q-learning and deep neural network (DNN) is employed, to gain the optimal price based on the reduced energy consumption by appliances associated with different energy loads utilizing the Markov Decision Process (MDP). Here, multiple agents are designed to handle different energy loads and consumption is controlled by the DQN agent, then reduced consumption data is securely shared among all stakeholders using blockchain-based smart contract. The performance evaluation of the proposed MD-REM approach seems to be efficient in terms of reduced energy consumption, optimal energy price, reward, and total profit analysis. Moreover, blockchain-based result is evaluated for the proposed MD-REM approach considering the performance metrics such as transaction efficiency, Interplanetary File System (IPFS) bandwidth utilization, and data storage cost comparison.

A Multi-Stream Deep Neural Network to Predict the Energy Consumption of Smart Home Appliances

A novel ensemble machine learning approach for predicting energy consumption in smart appliances is presented in this paper. The main objective is minimizing the number of necessary sensors and improving the prediction accuracy at the same time. The proposed method combines the Fuzzy C-Means method with a multi-stream deep neural network to achieve this goal. The method focuses on prediction accuracy and aims to extract the minimum number of essential features from the dataset corresponding to the required sensors. These selected features are then scatter-reduced and homogenized into subsets. Each subset is used to train a cluster-specific deep neural network designed exclusively for that subset. The final prediction is obtained by computing the fuzzy-weighted sum of these cluster-specific network outputs. Numerical results show that the proposed prediction method outperforms conventional methods in terms of root mean square error and mean absolute percentage error criteria, despite using fewer sensors. This improvement can be attributed to the reduced dataset scatter, which improves the learning speed and model performance. Furthermore, the fuzzy combination of the outputs improves the final prediction accuracy. Overall, the proposed approach provides a more cost-effective and accurate solution for predicting the energy consumed by smart appliances.

Self-Supervised Feature Learning for Appliance Recognition in Nonintrusive Load Monitoring

Non-intrusive load monitoring (NILM) can monitor the operating state and energy consumption of electric appliances in a non-intrusive manner and provides a promising approach to improving electricity usage efficiency for residential and commercial buildings. Although machine learning (ML) methods are powerful and have significantly advanced the developments of NILM, they request a sizable amount of labelled data for model training. However, getting operational data of each elec- trical appliance in real life is challenging, so the requirements for labelled data limit the NLIM's practicality. To tackle this challenge, a novel multi-layer momentum contrast (MLMoCo) learning mechanism is proposed for self-supervised feature rep- resentation learning. With only unlabelled aggregate load data, the proposed MLMoCo contrasts the augmented versions of the same sample (“positives”) with instances extracted from other samples (“negatives”). To maintain a dictionary with enough negative samples to be compared with the input, a momentum encoder is adopted to momentum update the parameters rather than by backpropagation during training. An event-based data augmentation method is also proposed to obtain the distinct but strongly related positive pairs for self-supervised feature learning. The experimental comparisons, including different state-of-the- art techniques and various downstream tasks with real-world datasets, demonstrate the remarkable performance gains of the proposed approach through learning from the unlabelled data, which could significantly increase the practicality of the NILM.

Consumer energy efficiency gap and the rebound effect across households income groups

The paper sets up a model which reconciles the energy efficiency gap and the decline of the rebound effect with households’ income. It is shown that the two phenomena can be explained in a framework with a utility-maximizing household that enjoys an income-independent endowment of energy services. Energy service endowment is a barrier against the adoption of the most energy-efficient appliances and leads to the so-called energy efficiency gap. Low-income households only use endowments and do not use energy-consuming appliances (cars, wall insulation, etc.). Higr-income households buy an appliance whose energy efficiency depends on the household’s income. Only households with income above a threshold buy the most efficient appliance. For households that replace their appliance with a more efficient one, there will be a rebound effect (the realized energy saving is less than the presumed one). It is shown that the rebound effect is higher at lower income levels because income and substitution effects from a decline in energy service prices are stronger. These stronger effects come from the endowment of energy services. The numerical example shows that the model can reproduce the patterns for the rebound effects of household income groups and their expenditure shares. It is also shown that other causes of the energy efficiency gap, such as credit constraints, do not lead to higher rebound effects at lower income levels.

Nonintrusive Load Monitoring (NILM) Using a Deep Learning Model with a Transformer-Based Attention Mechanism and Temporal Pooling

Nonintrusive load monitoring (NILM) is an important technique for energy management and conservation. In this paper, a deep learning model based on an attention mechanism, temporal pooling, residual connections, and transformers is proposed. This article presents a novel approach for NILM to accurately discern energy consumption patterns of individual household appliances. The proposed method entails a sequence of layers, including encoders, transformers, attention, temporal pooling, and residual connections, offering a comprehensive solution for NILM while effectively capturing appliance-specific energy usage in a household. The proposed model was evaluated using UK-DALE, REDD, and REFIT datasets in both seen and unseen cases. It shows that the proposed model in this paper performs better than other methods stated in other papers in terms of F1-score and total error of the results (in terms of SAE). This model achieved an F1-score equal to 92.96 as well as a total SAE equal to −0.036, which shows its effectiveness in accurately diagnosing and estimating the energy consumption of individual home appliances. The findings of this research show that the proposed model can be a tool for energy management in residential and commercial buildings.

EN Improving the energy efficiency of refrigeration equipment with a direct sales function

The increased use of refrigerants with low global warming potential GWP over the past decade indicates that manufacturers are already making gradual replacements with refrigerants with reduced environmental impact. Thus, in addition to HFC gas phase-out requirements, most EU countries are pursuing a trend to reduce the energy consumption of refrigeration appliances. Such countries restrict the import of equipment that does not meet the energy efficiency requirements. This paper presents the ecodesign of refrigeration equipment in relation to the requirements of the sales market using the Regulation on Conformity Assessment Procedure. HVAC&R equipment contributes to GHG emissions in two ways: directly through refrigerant leakage or venting, and indirectly through electricity use, the latter accounting for up to two-thirds of GHG emissions. The paper compares the energy and environmental characteristics of refrigerants R404a and R290 in freezer M200V. The experimental results show that the highest COP va­lues in the system for R404a and R290 are 3.02 and 3.19, respectively. Possibilities of complete transition from F-gases to natural analogues with GWP ≈ 0 are shown. At application of R290 its advantages on energy efficiency are obvious - reduction of electric power consumption of freezing chests on 8,9 %, influence on environment R404A – GWP=3260, R290 – GWP=3. The calculation of energy efficiency class for freezing chest was made. To improve energy efficiency, a number of design modifications were made: the density of the thermal insulation material and the compressor were changed. The energy consumption of the modified version at the second climatic class was measured at 1.23 kW/24h, and the energy efficiency coefficient according to the calculation EEI =34.904, which corresponds to the energy efficiency class «C».

Future-oriented trends in intelligent building materials and applications

Abstract Nowadays, smart materials appear more and more frequently in people’s daily lives, and this paper investigates the development and current status of the application of smart materials in the construction industry in view of the high energy consumption status quo of the construction industry. The goal of the energy conversion mode of piezoelectric material is to be based on the basic principle of piezoelectric sensors. Study the monitoring function of buried piezoelectric sensors on the damage to the concrete main frame. The passive energy storage method of phase change material and its integration with the building are being investigated at the same time. The performance of these two materials is then tested by applying different forces to the piezoelectric sensors with the help of linear motors, and the output voltage of the piezoelectric materials is tested, which proves its good cyclic stability. The phase change material is split into multiple layers based on heat flow direction, and each layer’s temperature is calculated separately to analyze the thermal behavior inside the material in depth. Through experimental analysis, it has been found that the output voltage of the triangular piezoelectric transducer can reach 550 V, which can be applied to optimize pavement structure and pavement energy harvesting. The phase change material has excellent energy saving and thermal insulation effect in the summer, and the average heat flux decrease rate of the wall can reach 45.0%, which greatly reduces the energy consumption of air conditioners and other temperature-controlled appliances. The application of intelligent building materials is in line with the requirements of sustainable development planning, and there is ample room for growth.

Modeling techno-economic multi-objectives of smart homes considering energy optimization and demand management

The research suggests an approach that prioritizes customer needs and aims to reduce energy expenses while safeguarding customer privacy. Furthermore, it is recommended that smart homes incorporate a home energy management system to optimize appliance energy consumption. Conversely, the introduction of demand-side management addresses the energy management challenges faced by smart households. The main goal of this approach is to decrease energy usage and electricity costs for customers. Moreover, it enhances user satisfaction while waiting at common intervals. The primary emphasis of this study is on a smart residence furnished with energy management technology and smart home gadgets capable of supplying electricity to the grid. These objectives are considered distinct aspects in the multi-objective optimization issue stemming from this approach. The study utilizes the grasshopper optimization algorithm (GOA) to optimize battery and home appliance scheduling in smart homes with flexible devices. The goal is to reduce the overall cost of microgrid systems through demand-side management implementation. This comparison highlights the superiority of the proposed method in optimizing energy consumption and reducing carbon emissions in a variety of scenarios. By achieving lower energy costs and carbon emissions while maintaining a comfortable indoor environment, the proposed method proves to be a highly effective and sustainable solution for energy management in buildings. These simulation results provide strong evidence of the method’s potential to significantly impact energy efficiency and environmental sustainability in real-world applications. Furthermore, the consistent minimization of the discomfort index showcases the method’s ability to prioritize occupant comfort while still achieving significant energy savings and emissions reductions. Overall, the comparison with other algorithms solidifies the effectiveness and practicality of the proposed method in addressing the complex challenges of energy management and sustainability in smart homes.

Introducing a novel method for simulating stochastic movement and occupancy in residential spaces using time-use survey data

In the context of growing concerns over energy consumption and sustainability, accurate modelling of occupancy patterns within residential buildings is critical. In this study, a novel stochastic occupancy model is introduced for simulating human behaviour within residential buildings by employing Time Use Survey (TUS) data and utilising Markov chains and probabilistic sampling algorithms. The novelty of this research lies in its approach to represent the dynamic nature of occupancy across different functional spaces and age groups, a gap not yet adequately addressed in existing studies. The model's accuracy is ascertained through ten-fold cross-validation, achieving an average R2 value of 0.91 across key functional rooms (bedroom, bathroom, kitchen, living room), indicating a high degree of precision. Applied to a case study of a two-story detached house in the UK, the model effectively reflects varied behaviour patterns and room occupancy among different age groups. For instance, the average daily appliance energy consumption for occupants aged 8–14 ranged from 0 to 3.77 kWh (median 1.71 kWh), for ages 15–64 from 0 to 4.93 kWh (median 2.61 kWh), and for over 65 from 0.87 to 5.65 kWh (median 3.60 kWh). This model, with its scalability and accuracy in capturing the inherent randomness of human behaviour, is a valuable tool for improving energy consumption simulations and contributing to sustainable residential building design and management.

A Scheduling Algorithm for Appliance Energy Consumption Optimization in a Dynamic Pricing Environment

This research delves into the intricate landscape of energy scheduling and optimization within microgrid and residential contexts, addressing pivotal aspects such as real-time scheduling systems, challenges in dynamic pricing, and an array of optimization strategies. This paper introduces a cutting-edge scheduling algorithm, harnessing the power of artificial neural networks driven by Long Short-Term Memory Networks, and highlights its exceptional performance, boasting a significantly lower Mean Absolute Error of 5.32 compared to conventional models. This heightened predictive accuracy translates into tangible improvements in both energy efficiency and cost savings. This study underscores the delicate balance between user satisfaction, cost reduction, and efficient scheduling for sustainable energy consumption, showcasing a remarkable 38% enhancement in optimized schedules. Further granularity revealed substantial gains in energy efficiency and cost reduction across different scheduling intensities: 11.11% in light schedules, 20.09% in medium schedules, and an impressive 38.85% in heavy schedules. However, this research does not shy away from highlighting challenges related to data quality, computational demands, and generalizability. Future research trajectories encompass the development of adaptive models tailored to diverse data qualities, enhancements in scalability for and adaptability to various microgrid configurations, the integration of real-time data, the accommodation of user preferences, the exploration of energy storage and renewables, and an imperative focus on enhancing algorithm transparency.

Now or later: The long tail effect of household income on energy consumption

In residential buildings, energy-using appliances support nearly all domestic needs, such as heating, cooling, cooking, lighting, and entertainment, and constitute the bulk of household energy consumption. However, decoupling between purchasing energy-using appliances and consuming energy may run counter to the basic expectation of the income consumption curve based on the most established theory of utility. Instead, we expect a long-tailed curve with a left deviation, that is, the long tail effect of income on household energy consumption. We thus rigorously examined the long tail effect by describing the changes in energy consumption against increasing household income. Three empirical studies demonstrate a possible intrinsic mechanism, and four heterogenous tests investigate the extrinsic incentives. Evidently, when considering the priority in household bundles, the decoupling of purchasing energy-consuming appliances and energy consumption leads to a long tail effect. Without intervention, households with higher income are less persuaded to purchase energy-saving products than households with lower income. Moreover, extrinsic incentives can influence intrinsic appeal and the long tail effect. We advance the conceptual and practical understanding of the exceptional consumption pattern of household energy, which is an indispensable and low-priority consumption in household bundles. The findings can provide researchers and policymakers with more detailed theoretical and practical instructions on how to encourage different households to form low-energy consumption patterns and apply energy-efficient appliances.

Data-Driven Modeling of Appliance Energy Usage

Due to the transition toward the Internet of Everything (IOE), the prediction of energy consumed by household appliances has become a progressively more difficult topic to model. Even with advancements in data analytics and machine learning, several challenges remain to be addressed. Therefore, providing highly accurate and optimized models has become the primary research goal of many studies. This paper analyzes appliance energy consumption through a variety of machine learning-based strategies. Utilizing data recorded from a single-family home, input variables comprised internal temperatures and humidities, lighting consumption, and outdoor conditions including wind speed, visibility, and pressure. Various models were trained and evaluated: (a) multiple linear regression, (b) support vector regression, (c) random forest, (d) gradient boosting, (e) xgboost, and (f) the extra trees regressor. Both feature engineering and hyperparameter tuning methodologies were applied to not only extend existing features but also create new ones that provided improved model performance across all metrics: root mean square error (RMSE), coefficient of determination (R2), mean absolute error (MAE), and mean absolute percentage error (MAPE). The best model (extra trees) was able to explain 99% of the variance in the training set and 66% in the testing set when using all the predictors. The results were compared with those obtained using a similar methodology. The objective of performing these actions was to show a unique perspective in simulating building performance through data-driven models, identifying how to maximize predictive performance through the use of machine learning-based strategies, as well as understanding the potential benefits of utilizing different models.

Design of an Economical Intelligent Frequency Meter

Abstract: Vigilantly monitoring the energy consumption of electrical appliances is a crucial aspect of effectively controlling energy usage and expenditures, hence optimizing financial and temporal resources. The utilization of electrical devices can result in various modifications to the characteristics of an electrical power source. Various aspects of an electrical power source undergo modifications when employed for the purpose of powering devices. There have been alterations in the items that necessitate documentation beside the cumulative measurement of meters. This paper discusses the design and implementation of an Arduino-based frequency meter specifically tailored for the measurement of frequency in a three-phase synchronous generator. Additionally, this method was employed to identify instances of malfunction in a three-phase alternating current generator, encompassing both low and high frequencies. The frequency meter that was suggested displayed the measured parameters and the protection condition of each data point directly on the liquid crystal display (LCD) screen. Additionally, a USB cable was employed to facilitate the printing of the Arduino software program in serial mode on a personal computer. A smart monitoring system was developed for various working circumstances using a Bluetooth module. The ZMPT101b AC voltage sensor has been utilized in conjunction with the frequency meter. The frequency was determined by analyzing the signal obtained from the AC voltage sensor, without the need for additional equipment such as a zero crossing detector.

General Overview and Proof of Concept of a Smart Home Energy Management System Architecture

This paper proposes and implements a smart architecture for Home Energy Management Systems (HEMS) that enables interoperability among devices from different manufacturers. This is achieved through the use of standardized elements and the design of an innovative middleware. The system comprises a control unit that communicates with smart outlets using the Wireless Smart Ubiquitous Network (WI-SUN) Home Area Network (HAN) specification, while smart metering is achieved using the WI-SUN Field Area Network (FAN) specification. To manage important data, a web platform and mobile app were created. Additionally, machine learning techniques are utilized to identify energy consumption of individual appliances when only the aggregate energy consumption of the house is available. The architecture presented here supports real-time control of energy use and generation through HEMS, and new devices can be added transparently. Finally, a comparison of the proposed system with similar systems in literature highlights its many advantages in terms of functionality.

Standby energy consumption and saving potentials in the residential sector in tropical areas: the Caribbean island Curaçao as a case study

Rising levels of carbon dioxide (CO2) are of significant concern in modern society, as they lead to global warming and consequential environmental and societal changes. The standby energy consumption of appliances in households is considerable and can be up to 15% of the appliance energy consumption in the residential sector. Overall, standby energy consumption is accountable for roughly 1% of total global CO2 emissions. When we contrast this impact on global CO2 emissions with the impact of the transportation sector, standby energy’s contribution is minimal. The transportation sector is responsible for about 24% of the global CO2 emissions arising from the combustion of fuel. Nevertheless, a significant reduction in standby energy consumption in the residential sector is crucial to reduce the CO2 footprint accordingly. This paper is among the first to assess the magnitude of standby energy consumption and to explore options for reducing standby energy consumption in the Caribbean. The Caribbean island Curaçao was taken as a first case study. Based on a field study of 20 households, the standby energy consumption of about 300 appliances were measured. It was estimated that about 8% of residential electricity consumption was linked to standby energy consumption. The average standby power of the 20 households in Curaçao is 50.3 W. Past research shows that it may be possible to reduce the estimated standby energy consumption by approximately 43%, which is about 6.4 MWh/year in Curaçao. Besides adopting technical solutions, the intended reduction of standby energy consumption can be realized through the introduction of import regulations to favor the selling of appliances with lower standby energy consumption and the creation of public awareness through TV and other media campaigns.

Implementation of a Real-Time IoT Based Energy Management System

Energy Management System has received a lot of attention from both academia and industries due to the rate of energy consumed by electrical appliances. These industries are faced with inflation in the cost of electrical bills. Therefore, this study presents the design and implementation of a real-time IoT based Energy Management System (EMS) to reduce the inflation in the cost of these electrical bills. The system comprises four (4) basic units namely; the sensing unit, control unit, display unit and switching unit. The sensing unit comprises of two sensors namely; Current Transformer (CT) sensor and voltage sensor which senses and measures the parameters (voltage and current) and sends the parameters to the control unit. This control unit comprises two (2) microcontrollers namely; Arduino Nano and ESP32. The Arduino Nano processes the received data from the sensing unit and sends the processed data to the display unit and ESP32 microcontroller for real-time monitoring. In addition, the ESP32 microcontroller sends the processed data to the cloud. Furthermore, the web-based application receives the measured parameters from the cloud and thus, allowing the user to monitor as well as control the rate of energy consumption of electrical appliances. The experimental results obtained showed that the developed system efficiently monitors and control the rate of energy consumption of the three applied loads (Edison 2.7 bulb, standing fan and phone charger). Hence, reduces the cost of electrical bills consumed by these appliances.