Home Energy Consumption Research Articles

Análisis de la Implementación de una Estrategia de Reducción del Consumo Energético en el Sector Residencial del Ecuador: Evaluación del Impacto en la Matriz Energética

En el presente artículo se realiza una evaluación del impacto en la matriz energética de la implementación de una estrategia de Reducción del consumo energético en el sector residencial del Ecuador. La propuesta de Reducción del consumo energético residencial considera la electrificación de los sistemas de cocción y calentamiento de agua en las viviendas, por medio de sistemas de alta eficiencia en sustitución de los cilindros de GLP. Asimismo, se evalúa la reducción del consumo energético en las viviendas debido a la integración de criterios bioclimáticos en la construcción de viviendas y edificios, así como, a la sustitución de equipos eléctricos ineficientes de refrigeración e iluminación con ayuda del software de Simulación y Análisis de la Matriz Energética, SAME. En el escenario de sustitución del GLP revela que implicaría un incremento del consumo eléctrico doméstico lo que exigiría la construcción de nuevas centrales eléctricas para cubrir el incremento de demanda esperada. La implementación de estrategias de eficiencia energética, basada en la integración de equipos eficientes de refrigeración e iluminación, así como la integración de sistemas solares térmicos, permitiría reducir sustancialmente la construcción de nuevas centrales hidroeléctricas, evitando la importación de electricidad de países vecinos y el impacto medioambiental de las represas hidroeléctricas.

Review on Home Energy Management System Considering Demand Responses, Smart Technologies, and Intelligent Controllers

The increasing demand for electricity and the emergence of smart grids have presented new opportunities for a home energy management system (HEMS) that can reduce energy usage. The HEMS incorporates a demand response (DR) tool that shifts and curtails demand to improve home energy consumption. This system commonly creates optimal consumption schedules by considering several factors, such as energy costs, environmental concerns, load profiles, and consumer comfort. With the deployment of smart meters, performing load control using the HEMS with DR-enabled appliances has become possible. This paper provides a comprehensive review on previous and current research related to the HEMS by considering various DR programs, smart technologies, and load scheduling controllers. The application of artificial intelligence for load scheduling controllers, such as artificial neural network, fuzzy logic, and adaptive neural fuzzy inference system, is also reviewed. Heuristic optimization techniques, which are widely used for optimal scheduling of various electrical devices in a smart home, are also discussed.

Influence of Changing Behavior and High Efficient Appliances on Household Energy Consumption in Thailand

Energy consumption in the commercial and residential buildings in Thailand has been increasing at a rate of five per cents a year. Concerning this issue, the Thai government has launched several campaigns to promote energy saving habits and replacement of high energy efficient appliances. However, those campaigns do not demonstrate how large impact of such activities on energy saving in households. This research aims at investigation of the potential of energy savings from changing behavior and high energy efficient appliances in the detached houses in Bangkok neighborhood areas. The latin hypercube method was used to generate two hundred combinations of forty-eight variables covering occupancy schedules and energy efficient home appliances. Such data set was then used to perform total energy consumption by using the eQuest 3.65 model. The regression coefficient, ß, was used to identify the most influent parameters on home energy consumption. It was found that changing behavior on higher-wattage appliance significantly affects the total energy consumption. The most significant activities affecting energy consumption are using electric fans instead of air conditioners when the room temperature is acceptable, upgrading plasma TV to LED TV, and replacing fluorescent light bulbs with LED bulbs.

Understanding Resident’s Perception of Energy Saving Habits in Households in Bangkok

The Thailand twenty-year energy efficiency development plan in 2015 aimed at decreasing energy consumption in the building sector about ten percent by 2030. One of the strategies is to promote residents upgrading high efficient home appliances and changing their behavior. This paper investigates the existing residents’ behavior and their interest in behavior change for reducing home energy consumption. The survey was conducted from four hundred households in Bangkok neighborhood areas. The questionnaires includes four parts covering: i) personal information, ii) an understand of energy saving strategies and way to receive information from the saving campaigns, iii) a number of home appliances and its usage schedule, and iv) energy-saving habits and behavioral changes in the future. Women are more likely energy-saving habits than men. Most residents presently selected efficient air conditioners and home appliances with energy rating labels and a few did change their behavior. Potentially saving more energy, they were interested in changing their behaviors on low-cost activities and minor retrofits of building structure. Understanding such household energy conservation behaviors could significantly contribute to effective energy policy making which suits the resident’s need and encourages home energy savings.

Analysis of Home Energy Consumption by K-Mean

The smart meter offered exceptional chances to well comprehend energy consumption manners in which quantity of data being generated. One request was the separation of energy load-profiles into clusters of related conduct. The Research measured the resemblance between groups them together and load-profiles into clusters by k-means clustering algorithm. The cluster met, also called “Gender (Male/Female), House (Rented/Owned) and customers status (Satisfied/Unsatisfied)” display methods of consuming energy. It provided value information aimed at utilities to generate specific electricity charges and healthier aim energy efficiency programs. The results show that 43% extremely dissatisfied of energy customer is achieved by using energy consumption.

Research on Smart Home Energy Management Algorithm Based on Cloud Environment

Energy saving algorithm for smart home energy consumption budget optimization under the SVM model using a simple particle swarm optimization algorithm to find the optimal value caused by the slow speed gradient value based on PSO algorithm is proposed to optimize the use of machine learning algorithm GDPSO algorithm (Gradient Descent based on down Particle Swarm Optimization Algorithm). First of all, the establishment of energy structure and energy consumption model of optimal hyperplane selection of penalty factor and Gauss the appropriate parameters; secondly, parameter selection and optimization of energy consumption model for smart home energy-saving emission reduction requirements using GDPSO algorithm, and improves the efficiency of the optimal SVM parameters to improve the accuracy of solution. A simulation example shows the effectiveness of the algorithm.

Negative spillover to policy

Individuals are often asked to reduce their home energy consumption. But new research suggests that reminders of these personal energy savings may undermine public support for national-level policies.

An Electricity Price-Aware Open-Source Smart Socket for the Internet of Energy.

The Internet of Energy (IoE) represents a novel paradigm where electrical power systems work cooperatively with smart devices to increase the visibility of energy consumption and create safer, cleaner and sustainable energy systems. The implementation of IoE services involves the use of multiple components, like embedded systems, power electronics or sensors, which are an essential part of the infrastructure dedicated to the generation and distribution energy and the one required by the final consumer. This article focuses on the latter and presents a smart socket system that collects the information about energy price and makes use of sensors and actuators to optimize home energy consumption according to the user preferences. Specifically, this article provides three main novel contributions. First, what to our knowledge is the first hardware prototype that manages in a practical real-world scenario the price values obtained from a public electricity operator is presented. The second contribution is related to the definition of a novel wireless sensor network communications protocol based on Wi-Fi that allows for creating an easy-to-deploy smart plug system that self-organizes and auto-configures to collect the sensed data, minimizing user intervention. Third, it is provided a thorough description of the design of one of the few open-source smart plug systems, including its communications architecture, the protocols implemented, the main sensing and actuation components and the most relevant pieces of the software. Moreover, with the aim of illustrating the capabilities of the smart plug system, the results of different experiments performed are shown. Such experiments evaluate in real-world scenarios the system’s ease of use, its communications range and its performance when using HTTPS. Finally, the economic savings are estimated for different appliances, concluding that, in the practical situation proposed, the smart plug system allows certain energy-demanding appliances to save almost €70 per year.

An analysis on the energy consumption of circulating pumps of residential swimming pools for peak load management

Based on an extensive dataset containing aggregated hourly energy consumption readings of residents during March 2011 and October 2012 in South Ontario, Canada, this paper estimates the energy consumption of circulating pumps of residential swimming pools (CPRSP) non-intrusively, and quantifies the impact of CPRSP on the power system. The main challenges are that, first, widely used non-intrusive appliance load monitoring (NIALM) methods are not applicable to this work, due to the low sampling rate and the lack of the energy consumption pattern of CPRSP; second, temperature-based building energy disaggregation methods are not suitable for this work, as they highly depend on the accurate base load estimation and predefined parameters. To overcome these issues, in this paper, first it is found that, during the pool season, for homes with and without swimming pools, the ratio between their base loads is approximately equal to the ratio between their temperature-dependent energy consumptions, then a novel weighted difference change-point (WDCP) model has been proposed. The advantages of the WDCP model are that, on one hand, it doesn’t depend on the base load estimation and predefined parameters; on the other hand, it has no requirement on the data sampling rate and the prior information of energy consumption patterns of CPRSP. Based on the WDCP model it is shown that, the average hourly energy consumption of CPRSP is 0.7425kW, and the minimum and the maximum hourly energy consumptions are 0.5274kW at 9:00 and 0.9612kW at 17:00, respectively. At the peak hour 19:00, July 21, 2011, CPRSP contributes 20.36% energy consumption of homes with swimming pools, as well as 8.48% peak load of all neighborhoods. As a result, the peak load could be reduced by 8.48% if all CPRSP are stopped during the peak hour.

Convenience and energy consumption in the smart home of the future: Industry visions from Australia and beyond

Resonating with the industrial revolution of the home (Schwartz Cowan, 1989), the smart home ‘digital revolution’ reinvigorates promises for convenience. This paper analyses the industry vision for a ‘simple life’, and asks what energy implications it is likely to generate. We draw on an international magazine and online media content analysis of the 21st Century smart home, alongside 10 interviews conducted with Australian smart home industry professionals. The analysis explores the contradiction between complexity and simplicity embedded in smart home industry visions, where an expanded range of devices, services and options are marketed as a way to simplify and enhance everyday practices. A promoted side-benefit is reduced and more efficient energy consumption. We unpack the expectations embodied in this current convenience narrative to show how smart home devices may transform everyday practices in ways which result in increased energy consumption and household labour. The paper concludes by calling for energy research and scholarship which seeks to disrupt the convenience and other smart home narratives.

Distributed Optimization Framework for Energy Management of Multiple Smart Homes With Distributed Energy Resources

This paper proposes a distributed optimization algorithm for scheduling the energy consumption of multiple smart homes with distributed energy resources. In the proposed approach, the centralized optimization problem for home energy management is decomposed into a two-level optimization problem, corresponding to the local home energy management system (LHEMS) at the first level and the global home energy management system (GHEMS) at the second level. The controllable household appliances (e.g., air conditioner and washing machine) are scheduled in the LHEMS within the consumer’s preferred appliance scheduling and comfort level, while the energy storage system and power trading between households are scheduled in the GHEMS. In the simulation study, the proposed distributed algorithm shows almost equivalent performance to the centralized algorithm in terms of the electricity cost and the consumer’s comfort level. The impact of different network topologies on the proposed algorithm is also analyzed, and the result provides insight into the selection of the optimal network configuration in view of the consumer’s electricity cost saving.

Design Home Energy Feedback: Understanding Home Contexts and Filling the Gaps

Efficient home energy consumption is a complex, dynamic activity, and householders need to monitor, plan and act with the support of home energy feedback system. This paper reviews the existing literatures of home energy feedback system design, and summarizes the characteristics of home contexts from the householder’s perspective, including low level of attention, motivation, effort, as well as short of knowledge and skills. We then analyze the gaps of using energy feedback system to support the users’ energy actives from perspective of data, information and user engagement, and we propose that using ambient display and natural interaction could improve the effectiveness of home energy feedback systems.

Interaction effects of building technology and resident behavior on energy consumption in residential buildings

Buildings account for a significant portion of energy consumption and carbon emissions around the world and increasingly scholars and practitioners are re-thinking strategies that mitigate use. This paper reports an empirical study aimed at identifying the relationship between building technology and resident behavior and the joint effects on energy consumption in residential buildings. Unlike previous work that isolated effects of technology or behavior on energy consumption, this study investigates their interactions. The researchers collected technical and behavioral data from more than 300 residential units and performed data analysis using energy simulation and multivariate regression techniques. Results identify the interaction effects between building technology and resident behavior and provide quantifiable evidence supporting the hypothesis that “building technology and resident behaviors interact with each other and ultimately affect home energy consumption.” Findings indicate four important resident behaviors that directly correlate to energy consumption and two that indirectly correlate to energy consumption. The research also indicates that only 42% of technological advances directly contribute to home energy efficiency, suggesting that the achievable impact on energy savings depends on both technical advances and behavioral plasticity.

Urban Data in the primary classroom: bringing data literacy to the UK curriculum

As data becomes established as part of everyday life, the ability for the average citizen to have some level of data literacy is increasingly important. This paper describes an approach to teaching data skills in schools using real life, complex, urban data sets collected as part of a smart city project. The approach is founded on the premise that young learners have the ability to work with complex data sets if they are supported in the right way and if the tasks are grounded in a real life context. Narrative principles are used to frame the task, to assist interpretation and tell stories from data and to structure queries of datasets. An inquiry-based methodology organises the activities. This paper describes the initial trial in a UK primary school in which twelve students aged 9-10 years learnt about home energy consumption and the generation of solar energy from home solar PV, by interpreting existing visualisations of smart meter data and data obtained from aerial survey. Additional trials are scheduled with older learners which will evaluate learners on more challenging data handling tasks. The trials are informing the development of the Urban Data School, a web-based platform designed to support teaching data skills in schools in order to improve data literacy among school leavers.

Novel home energy management system using wireless communication technologies for carbon emission reduction within a smart grid

Home energy consumption is increasing, and renewable energy sources are being deployed. The use of renewable energy sources as an alternative to fossil fuels and nuclear energy is a significant opportunity for reducing CO2 emissions. However, carbon emissions from electricity generation are dependent on the type of renewable energy source and the quantity of the electricity that is produced. With the emergence of the smart grid, residents are able to reduce their electricity consumption by shedding the power usage in their homes. The objective of this study is to smooth power peak demand and diminish CO2 emissions. This paper describes the power shedding benefits of reducing energy consumption and CO2 emissions. This reduction of CO2 emissions eliminates the need for auxiliary power plants in the grid in the case of peak demand points and the need to switch to renewable energy sources. In this study, we prove that emerging information and communication technology can reduce energy use and CO2 emissions. Wireless communication architecture is introduced in this study to increase grid flexibility and rapidity in the data flow among all of the components in the proposed system. Our proposed system with local renewable generation can reduce CO2 emissions nearly 72% of all CO2 emissions when all appliances are supplied with the grid. With our load shedding algorithm, the CO2 emissions decrease nearly 91%. The yearly estimation shows that our system can reduce 62% of CO2 emissions and 37% of consumed power. In this paper, we introduce an efficient architecture of a smart home energy management system and propose a shedding algorithm for home energy usage. This system is based on wireless communication among home appliances, a home management and control system, a grid management and control system and domestic renewable energy sources. Our proposed system, which employs wireless communication, domestic renewable generation and a power shedding algorithm, can operate in conjunction with future smart grids by applying real-time data, such as peak demand points. Numerous real data sets permitted the development of some of the statistics in the system results.

Sensitivity Analysis of Common Input Parameters in Tools for Modeling Energy in Homes

Energy models of buildings can be developed and used for analysis of energy consumption. A model offers the opportunity to simulate a building under specific conditions for analysis of energy efficiency measures or optimum design. Due to the great amount of information needed to develop an energy model of a building, the number of inputs can be reduced by making variable the most relevant input parameters and making the others to take common or standard values. In this study, an analysis of input parameters required by computational tools to estimate energy consumption in homes was done in two stages. In the first stage, common input parameters were identified for three software and three webtools based on the criteria that the input parameter should be common for at least two software and at least one webtool. In the second stage, a sensitivity analysis was performed on the inputs identified in the first stage. The software BEopt, developed by the National Renewable Energy Laboratory, was used as the source of typical input parameters to be compared, and to perform the simulations for the sensitivity analysis. The base or reference model to perform simulations for the sensitivity analysis corresponds to a model developed with information from a research house located on the campus of the University of Texas at Tyler and default inputs for the BEopt B-10 reference benchmark. Results show that besides the location, and consequently the weather, common parameters are building orientation, air leakage, space conditioning settings, space conditioning schedule, water heating equipment, and terrain. Among these parameters, the sensitivity analysis identified the largest variations in energy consumption for variations on space conditioning schedule (heating and cooling setpoints), followed by the type of water heating equipment. KEYWORDS: Residential Buildings; Energy Consumption; Energy Analysis; Input Parameters; Building Simulation; Source Energy

An Enhanced System Architecture for Optimized Demand Side Management in Smart Grid

Demand Side Management (DSM) through optimization of home energy consumption in the smart grid environment is now one of the well-known research areas. Appliance scheduling has been done through many different algorithms to reduce peak load and, consequently, the Peak to Average Ratio (PAR). This paper presents a Comprehensive Home Energy Management Architecture (CHEMA) with integration of multiple appliance scheduling options and enhanced load categorization in a smart grid environment. The CHEMA model consists of six layers and has been modeled in Simulink with an embedded MATLAB code. A single Knapsack optimization technique is used for scheduling and four different cases of cost reduction are modeled at the second layer of CHEMA. Fault identification and electricity theft control have also been added in CHEMA. Furthermore, carbon footprint calculations have been incorporated in order to make the users aware of environmental concerns. Simulation results prove the effectiveness of the proposed model.

A nonintrusive load identification method for residential applications based on quadratic programming

The decomposition of household power consumption is highly desired for scheduling household appliances and reducing home energy consumption. This paper presents a novel nonintrusive load identification method based on quadratic programming. Extensive simulation and laboratory tests have demonstrated that the proposed technique can provide adequate load identification accuracy for residential energy monitoring such that it is suitable for nonintrusive load monitoring (NILM) systems in residential and commercial buildings.

Trust based reliable transmissions strategies for smart home energy consumption management in cognitive radio based smart grid

Reliability of smart home energy management (SHEM) is enhanced due to the use of opportunistic cognitive radio based communications for reliable transmission of peak period energy utility data. Cognitive radio networks are apt communication solutions capable of offering reliable opportunistic data transmissions through spectrum reuse. Reliable opportunistic data transmissions ensure strategic decision making and to execute critical control operations for sustainable energy utilizations. Accurate spectrum sensing and efficient spectrum sharing are vital to aspects of reliable peak energy consumption data transmission. Cooperative spectrum sensing has been proposed as a more reliable method for gaining accurate spectrum availability detection. Trustworthiness of the local decisions of cooperating users is vital for the accuracy of the final spectrum availability decision. However, measures which can assess the trustworthiness of cooperating users are given less attention. In this paper, we describe a novel multi-attribute trust based framework to facilitate reliable spectrum sensing and priority based spectrum access allocation to enhance delay sensitive data transmissions. We have evaluated our solutions using extensive simulation experiments. Furthermore, we comparatively analyzed the reliability of the proposed user selection method for known spectrum sensing data falsification (SSDF) attack behaviors to accurately identify the non-malicious users. As evidenced by the results proposed novel multi-attribute trust based transmission strategies offer greater reliability in ensuring timely availability of peak period energy utility data for SHEM.

Quantification of (p)rebound effects in retrofit policies – Why does it matter?

The ‘prebound effect’ characterises how average heating energy consumption in older homes is consistently lower than these buildings' calculated energy ratings, and helps explain why energy savings from thermal upgrades are often lower than anticipated. This paper explores the conceptual links between prebound and rebound effects and aims to quantify these behavioural effects. It applies the resulting mathematical model to empirical examples of actual and calculated energy consumption at scales of individual dwelling and national housing stock. These show that the rebound effect, as defined in econometrics literature, can only indicate proportionate reductions in energy consumption and can mask high levels of absolute consumption. The prebound effect, however, can identify under- and over-consumption regardless of rebound effects. A combination of high prebound effect and low income suggests fuel poverty, and the rebound effect here is less relevant regarding total energy consumption. Policymakers should identify housing with high prebound effects in order to eliminate fuel poverty, and be aware of inaccuracies in calculating payback time where economic viability of retrofits is mandated. Further research is needed to understand motivations and practices in households that have high prebound effects and to identify specific priority groups for thermal retrofit policy.