Home Energy Consumption Research Articles

Thermal comfort – an intercultural comparison

ABSTRACT Growing energy consumption together with the problem of increasing CO2 emissions are widely viewed as critical factors for achieving global sustainability. The Green Building Council has estimated that 39% of CO2 emissions come from buildings and that this percentage is increasing. While much of home energy consumption is related to heating and cooling there is little research on how consumers manage the temperature in their homes. This study examines residential consumer perceptions regarding thermal comfort with the objective of gaining insights on how to reduce energy consumption for heating or cooling homes. Thus, we would gain insights for achieving a more sustainable approach for energy consumption while considering ecological, economic, and social aspects.

Analyzing energy consumption factors during coronavirus (COVID-19) pandemic outbreak: a case study of residential society

ABSTRACT The global pandemic of COVID-19 has affected most countries and has impacted every household’s operations. Mandatory stay-at-home lockdowns have forced building residents to use more energy for their daily routine activities, giving rise to higher energy usage. The increased energy use by building residents results in high energy bills and leads to a scarcity in the energy supply. Identifying factors that can control the high energy consumption in buildings can be used to optimize energy consumption and act as energy conservation factors. This proposed research is conducted to identify and explore the most influential energy consumption factors amid COVID-19 pandemic in residential buildings using a case study in India. To analyze the energy consumption factors, a Multiple Criteria Decision Making (MCDM) methodology based Best Worst Method (BWM) and a Decision-Making Trial and Evaluation Laboratory (DEMATEL) methodology have been integrated. The results show that among 34 comprehensive energy consumption factors, the high priority topmost 50% of the factors were analyzed. The main research outcome indicates that 53% of the factors were in the cause group and the remaining 47% were in the effect group factors. Research results conclude that in response to COVID-19, social distancing, home quarantine, and home transformation were the primary factors for energy consumption in homes. Among the sub-factors, the lockdown of streets, shutdown of public markets, and psychological factors were the top three factors adding to increased energy consumption. Further, Industrial and economic importance of the research is also discussed.

Rational installation choice of heat pump outer duct

Problem statement. The use of renewable energy sources by the world community is considered as one of the most promising ways to solve the growing problems of energy supply. The presence of an inexhaustible resource base and the environmental friendliness of renewable energy sources are their defining advantages. Given the significant potential of renewable energy sources in Ukraine, which is many times higher than the predicted levels of heat energy consumption, the withdrawal of alternative energy sources to a cost-effective level and the expansion of areas of use is an urgent issue of our time. Purpose of the article. is an in-depth study of alternative energy sources and methods for the efficient use of energy resources, followed by the selection of rational technology for constructing a heat supply system for a residential building based on geothermal energy sources. Analysis of recent research. According to the Organization for Economic Cooperation and Development (OECD), Ukraine has the highest level of utility costs in relation to income levels among European countries. The cost of Ukrainians to pay for housing is close to 40%, which is 1.6 times higher than the European average. To ensure a low level of energy consumption in energy efficient homes of the new generation, European countries use technologies that use geothermal energy through ground heat exchangers. However, this technology has not become widespread in Ukraine or enshrined in law. Therefore, the issue of introduction of technologies of ground heat exchangers at the expense of renewable energy sources is actual. Conclusion. The methods for arranging the external contours of the heat pump are considered and showed that the laboriousness of the device of heat exchangers in the horizontal circuit is almost 4 times lower than during drilling operations. The cost is also almost 4 times lower. Despite this, the developer will not always choose a horizontal heat exchanger, since for its construction it is necessary to provide a significant territory that cannot be given away for development or temporary, commercial buildings. In the case of tight construction conditions, an energy-efficient pile with a heat exchanger circuit will be acceptable.

Smart Monitoring System for Home Energy Consumption (SMEC)

The issue of environmental protection and related issues has become a major concern for many people. Despite this concern, electricity consumption continues to pose several environmental pollutions. Electricity consumption leads to the need for natural gas, which in turn leads to many devastating consequences for the environment. Electricity, in itself, does not necessarily pose environmental damage, but on the contrary, it offers many benefits, such as lighting and the operation of many devices; but the problem with power consumption is the gas needed to produce it, which can be damaging to the environment. Indeed, natural gas is currently the most important sources of energy used to produce electricity, which is, create an enormous amount of carbon dioxide, which the environment cannot tolerate. Regardless of the method used to generate electricity, environmental issues related to the use of electricity will help to ensure that the method used is best suited to the environment as a whole. The best way to reduce environmental problems from electricity consumption is to minimize this use. In this research, we will try to solve this problem by developing a new application titled Smart Monitoring System for Energy Consumption (SMEC). SMEC will provide a management of power‘s consumption within the building anywhere and anytime in which users can see how much of electricity they use while power’s consumption report would be provided automatically. The main advantages of SMEC is to avoid some problems like decrease consumption of electricity, help to detect abnormal use and controlling unneeded devices, save effort and time for monitoring and reading the amount of uses and finally such a new application will be more reliable and good for employee to check meter’s reading online. All this will be displayed in a friendly interface, which will have graphs and text to make the use of such application more efficient and simple to use. SMEC application will be developed using different languages such as C++, Java script, PHP, html and MySQL. Moreover, the proposed energy detection device uses Arduino UNO with some sensors such as ACS712, electric meter, GPS, LED and Wi-Fi adapter. The information is received by reading the power meter consumption and then handled through the Arduino device to perform the objectives of the research. By reading electricity’s consumption online, SMEC can analyze collected data that will help to make better decisions using Internet of Things.

A Multi-Objective Approach for Optimal Energy Management in Smart Home Using the Reinforcement Learning.

Maintaining a fair use of energy consumption in smart homes with many household appliances requires sophisticated algorithms working together in real time. Similarly, choosing a proper schedule for appliances operation can be used to reduce inappropriate energy consumption. However, scheduling appliances always depend on the behavior of a smart home user. Thus, modeling human interaction with appliances is needed to design an efficient scheduling algorithm with real-time support. In this regard, we propose a scheduling algorithm based on human appliances interaction in smart homes using reinforcement learning (RL). The proposed scheduling algorithm divides the entire day into various states. In each state, the agents attached to household appliances perform various actions to obtain the highest reward. To adjust the discomfort which arises due to performing inappropriate action, the household appliances are categorized into three groups i.e., (1) adoptable, (2) un-adoptable, (3) manageable. Finally, the proposed system is tested for the energy consumption and discomfort level of the home user against our previous scheduling algorithm based on least slack time phenomenon. The proposed scheme outperforms the Least Slack Time (LST) based scheduling in context of energy consumption and discomfort level of the home user.

Analysis of the Impact of Residential Property and Equipment on Building Energy Efficiency and Consumption—A Data Mining Approach

Human living could become very difficult due to a lack of energy. The household sector plays a significant role in energy consumption. Trying to optimize and achieve efficient energy consumption can lead to large-scale energy savings. The aim of this paper is to identify the equipment and property affecting energy efficiency and consumption in residential homes. For this purpose, a hybrid data-mining approach based on K-means algorithms and decision trees is presented. To analyze the approach, data is modeled once using the approach and then without it. A data set of residential homes of England and Wales is arranged in low, medium and high consumption clusters. The C5.0 algorithm is run on each cluster to extract factors affecting energy efficiency. The comparison of the modeling results, and also their accuracy, prove that the approach employed has the ability to extract the findings with greater accuracy and detail than in other cases. The installation of boilers, using cavity walls, and installing insulation could improve energy efficiency. Old homes and the usage of economy 7 electricity have an unfavorable effect on energy efficiency, but the approach shows that each cluster behaved differently in these factors related to energy efficiency and has unique results.

Gendered homes in theories of practice: A framework for research in residential energy consumption

A reduction of residential energy consumption is urgently needed, and gender is central in the performance of residential practices for this energy consumption. Thus, including gender is necessary for energy research. Theories of practice have proven to be a useful approach to the study of residential energy consumption. However, these theoretical approaches have only included gender to a limited extent. Building on contributions from gender studies, this paper presents an analytical framework for working with gender in theories of practice. The focus is on residential energy consumption in north-western middle-class homes, as most energy is consumed here. The paper presents the argument that gender can be conceptualised as `general understandings’ in theories of practice because `general understandings´ combines discursive and tacit elements of practices. Further, the paper builds on the practice theoretical understanding that the roles of materials, such as technology and houses, change according to specific associated practices. This also means that the practice as much as the materials are gendered. The paper concludes by indicating four ways to include gender in residential energy studies: 1) in all parts of the research design, 2) in the analysis of the surrounding institutions, 3) in the study of the performance of energy-related practices, and 4) in how gender is affected by and affects sustainable transitions.

The Clean Air Priority Programme – Evaluation and Perspectives

The article aims to evaluate the effectiveness of the Clean Air Priority Programme and to establish possible threats to its success. The National Fund for Environmental Protection and Water Management’s programme constitutes an important element of the government programme that goes by the same name. This study, with the aid of an institutional-legal, decision-making and quantitative analysis will undertake to answer the following research questions: (1) can the realisation of the aim formulated in this programme result in an improvement of air quality in Poland?; (2) what, if any, barriers stand in the way of achieving the programme’s aims?; (3) does the Clean Air Programme require further changes? On account of the high energy consumption of Polish homes, which typically results in their owners heating them with the cheapest fuels, emitting significant amounts of pollution, should the programme aims be assessed positively? In the process of discussions however, three types of barriers have come to light: (1) those related to the effective distribution of funds, (2) difficulties arising from programme frameworks that potential beneficiaries might encounter, and (3) potential limitations in the access to EU funding. On account of threats to the success of the programme that have been identified, it is expected that further changes to its functioning will be introduced as well as the ring-fencing of funds for thermo-modernisation and change of heating systems.

Empowering householders: Identifying predictors of intentions to use a home energy management system in the United Kingdom

Trials of technologies designed to promote residential demand-side energy management (DSM) have found aggregate levels of load-shifting behaviour and curtailment in energy use. These aggregate data, however, mask considerable differences in people's engagement in DSM at an individual household level. We present the findings of a quantitative exploration of people's intentions to use a home energy management system (HEMS) for residential DSM in the United Kingdom. The technology acceptance model (TAM) was used in conjunction with constructs measuring psychological empowerment and environmental attitudes to explore participants' acceptance of a HEMS to facilitate load-shifting. Findings from a mediation analysis showed perceptions of the usefulness of the HEMS and its ease of use were important predictors of people's intentions to use one. They also highlight a potential conflict between an individual's home energy consumption goals and national DSM goals. The implications of these findings for understanding end-user acceptance of HEMS are discussed. We conclude that seeking opportunities to promote shared, internalised goals for residential DSM may be an avenue for increasing the uptake and use of technologies designed to enable load-shifting (and other energy conservation behaviours) among end-users.

Demand side management of heat in smart homes: Living-lab experiments

In smart energy systems the role of the building is transformed from being a passive consumer into an energy flexibility provider. Buildings are expected to have flexible energy demand to provide services to energy grids. In this paper we present an experimental study conducted in a multi-storey residential apartment building. In the experiments supervisory control of individual room temperatures was applied to provide direct demand response for district heating grids. Control signals were applied to the individual floor heating systems in about 90 rooms in order to reduce heating demand in peak load hours. The results show that there is a significant potential for flexible energy consumption in homes based on smart home systems. It was found that when using a simple time based penalty signal, on average, the peak-hour energy consumption was reduced by 85% with little impact on overall energy consumption and indoor temperature.

Visualization and prediction of energy consumption in smart homes

Visualization and prediction of electrical energy can play an important role in managing the energy consumption at building level. Precise modeling of energy consumption is necessary in order to reduce consumption and thus reduce carbon emission. Thi

MULTI-CRITERIAL OPTIMIZATION OF ENERGY CONSUMPTION IN A SINGLE-FAMILY HOUSE WITH MODIFICATION OF ENTROPIA PREFERENCE

The article discusses examples of solutions that reduce energy consumption in a single-family home. 7 variants were selected to optimize energy consumption and 6 criteria, which describe the designated solutions. The validity value of the adopted criteria was chosen and four preference variants were subjectively determined for them. The data was then collected in a table and normalized to be able to use the weighted sum method to determine the best solution. In the next step, the entropy method was used and the subjective preferences adopted earlier, were used to determine the way to optimize electricity in a single-family home.

Implementation of non-intrusive appliances load monitoring (NIALM) on k-nearest neighbors (k-NN) classifier

Nonintrusive Appliance Load Monitoring (NIALM) is used to analyze individual’s house energy consumption by distinguishing variations in voltage and current of appliances in a household. The method identifies load consumption of each appliance from the aggregated home energy consumption. NIALM will also provide information of load consumptions of each appliance by indirectly detecting the abnormal changes of appliance usage. The proposed NIALM approach is based on features extraction from load consumptions measurements of electrical power signals in order to classify appliance’s state of operation. In this work, we have improved the identification accuracy and the detection of appliances based on their operational state by employing Machine Learning (ML) technique; namely k-nearest neighbor (k-NN) classification algorithm. The dataset used to perform this process is from the publicly available (PLAID) of power, voltage and current signals of appliances from several houses. This is used as benchmark data set. The PLAID dataset is collected and processed for each appliance and our classification results based on k-NN algorithm achieved high accuracy and is able to gain cost-effective solution. In addition, the result shows that k-NN classifier is a proven as an efficient method for NIALM techniques when compared with other proposed different ML options. Based on the used dataset, the average F-score measure obtained using the k-NN classifier is 90%. Possible reasons behind these findings are discussed and areas for further exploration are proposed.

An ontology-based framework for automated code generation of Web AR applications

In this paper, we propose an ontology-based framework for automated Web AR application code generation leveraging semantic descriptions of application structure and behavior in synergy with domain knowledge and annotations of building information model (BIM). Three case studies are presented: smart home energy consumption monitoring application, robotics testbed companion and virtual music mixing interface. The presented approach significantly reduces the time needed for Web AR application development.

Vehicle-to-Everything (V2X) energy services, value streams, and regulatory policy implications

Vehicle-to-Anything (V2X) is an umbrella term to explain the use of electric vehicle batteries to derive additional value during times of non-use. V2X services generate revenue from the battery asset through dynamic (V1X) or bi-directional (V2X) charging to provide benefits to the electric grid, to reduce energy consumption of buildings and homes, or to provide back-up power to loads. While relatively unknown and still regarded as a nascent technology, V2X exhibits low capital costs and enabling costs have decreased by 90% since 2014.We present the V2X Value Stream Framework as a means to better communicate and categorize its full economic potential. A meta-analysis of Value Stream potential gives results contradictory to the literature and indicates that Bill Management, Resource Adequacy, and Network Deferral are more valuable than Energy Arbitrage and Spinning Reserves. We distinguish between Energy and Power Value Streams and show how the latter cause less battery degradation and allow for greater stacking of services. Finally, energy policy recommendations are given to better integrate V2X. While we concur that development is of and by the market, we emphasize that V2X will develop within the constraints of the regulatory environment; therefore regulators have an enabling role to play.

Optimal Scheduling of Residential Home Appliances by Considering Energy Storage and Stochastically Modelled Photovoltaics in a Grid Exchange Environment Using Hybrid Grey Wolf Genetic Algorithm Optimizer

The transformation of a conventional power system to a smart grid has been underway over the last few decades. A smart grid provides opportunities to integrate smart homes with renewable energy resources (RERs). Moreover, it encourages the residential consumers to regulate their home energy consumption in an effective way that suits their lifestyle and it also helps to preserve the environment. Keeping in mind the techno-economic reasons for household energy management, active participation of consumers in grid operations is necessary for peak reduction, valley filling, strategic load conservation, and growth. In this context, this paper presents an efficient home energy management system (HEMS) for consumer appliance scheduling in the presence of an energy storage system and photovoltaic generation with the intention to reduce the energy consumption cost determined by the service provider. To study the benefits of a home-to-grid (H2G) energy exchange in HEMS, photovoltaic generation is stochastically modelled by considering an energy storage system. The prime consideration of this paper is to propose a hybrid optimization approach based on heuristic techniques, grey wolf optimization, and a genetic algorithm termed a hybrid grey wolf genetic algorithm to model HEMS for residential consumers with the objectives to reduce energy consumption cost and the peak-to-average ratio. The effectiveness of the proposed scheme is validated through simulations performed for a residential consumer with several domestic appliances and their scheduling preferences by considering real-time pricing and critical peak-pricing tariff signals. Results related to the reduction in the peak-to-average ratio and energy cost demonstrate that the proposed hybrid optimization technique performs well in comparison with different meta-heuristic techniques available in the literature. The findings of the proposed methodology can further be used to calculate the impact of different demand response signals on the operation and reliability of a power system.

Integrating qualitative and quantitative research to develop the final archetypes: Developing home occupant archetypes: First results of mixed-methods study to understand occupant comfort behaviours and energy use in homes

To better understand home energy consumption, it is important to study the behaviours of occupants in their homes, especially in relation to their comfort needs. A mixed methods study comprising of a questionnaire, interviews, indoor environmental parameters monitoring, and energy consumption readings was performed to group home occupants based on their behavioural patterns. The TwoStep cluster analysis produced five clusters of home occupant with the data from 761 questionnaire respondents. The clustering model comprised of 28 variables including constructs of emotions, comfort affordances, and locus of control. Then, in-depth semi-structured interviews were conducted and IEQ monitoring and energy readings were taken with 15 of the questionnaire respondents. The results of the field study were used to substantiate the findings of the questionnaire. The combination of the statistical clusters with the data from the field study resulted in five archetypes: five distinct types of home occupants, differing in their behavioural motivations towards achieving comfort, and their use of energy when doing so. This study shows that a mixed methods approach is valuable for better understanding energy consumption and implementing archetype-customized lines of action to reduce energy use and maintain comfort.

Development of Smart IOt Energy Meter with Energy Saving Estimator

Energy meter is a device that is widely used to measure home energy consumption for the billing tariff. The meter measuring energy used (in kWh) by the home appliance. Conventional energy meter only provide payable billing tariff by monthly or quarter which results over usage of energy problem. This problem lead to higher energy billing per house. Despite that, there are no billing indication levels that tell the user about how much they had to pay. The problem that happens in this situation is that the user cannot tell how much they have used the energy along with it appliance. This problem cause the user to use the energy without hesitation and uncontrolled. To solve this problem, there are solutions that can help to solve the problem. Then development of the device in which monitor the watt used along with the billing tariff. With this device, the user can tell by how much they had been charges throughout the day. The Smart IoT Energy Meter is a device that is developed to fulfil the requirement in the electrical billing system. This product has come with a realistic design and functional. It consist of microcontroller and electronic part which measure the current flow through the wire. This device will help the two-way communication between the user and the electrical company. The device are developed to measure and monitor the current, voltage, watt, energy and billing tariff that can be monitor through the Thing Speak channel. Since that, user can monitor the electrical consumption usage throughout the smartphone or laptop. The development of project in the future can give awareness to the user about their energy consumption usage in daily life.

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

Abstract The energy interaction between smart homes can be a solution for developing renewable energy systems in residential sections and optimal energy consumption in homes. The main objectives of such energy interactions are to increase consumer participation in energy management‘ boost economic efficiency‘ increase the user’s satisfaction by choosing between electricity sellers and buyers‘ and reduce the electricity purchased from the grid especially at peak hours. Thus, the innovations of this study includes defining an energy exchange method between smart buildings in an off-grid mode considering renewable energy systems, considering both thermal and electrical equilibrium and studying the lightning loads. it is assumed, here, that smart homes are off-grid‘ and the critical loads are supplied by the energy transfer between the homes using mixed integer linear programming. A compromise between the cost and time interval for using home appliances is considered to provide consumer’s comfort. An objective function is introduced considering programmable and non-programmable loads‘ thermal and electrical storages and lighting loads aiming to optimize the cost of energy between different smart buildings. Based on the method, which is tested in two different cases not only does the total cost of the smart buildings decrease but also the cost is reduced significantly when lightning loads are managed.

Empirical Examination of Intention to Continue to Use Smart Home Services

With the continuous expansion of sub-ecological problems, it is the question of how to effectively reduce environmental pollution and improve energy efficiency that has become a major challenge. At the same time, the energy consumption of family homes accounts for a large proportion, and whether smart homes can become a part of sustainable development is getting more and more attention. Although there are hundreds of smart home products and many investors on the market, consumers are relatively less accepting of this technology. How to make consumers adopt and continue to use has become a key issue in the development of smart homes. From the perspective of consumers, this study aims first to explore the relevant factors that affect the sustainable use of smart homes, and second empirically analyze the hypothetical model by using structural equation modeling. The empirical analysis results are based on the sample data of 488 Chinese respondents with experience in smart home use. The results showed that the service quality and perceived usefulness of smart home services positively impact users’ degree of satisfaction, and a higher degree of satisfaction, in turn, contributes to users’ formation of habits. Users’ desire to continuously use smart home services is influenced by perceived usefulness, degree of satisfaction, and habit; among them, habit is the key factor affecting users’ willingness. These findings expand our understanding of users’ desire to continuously use smart homes and provide theoretical support for the sustainable development of the smart home industry.