Effectiveness of an energy-consumption information system for residential buildings

The authors have proposed a method of reducing the energy consumption in residential buildings by providing household members with information on energy consumptions in their own houses. An on-line interactive “energy-consumption information system” that displays power consumptions of, at most, 18 different appliances, power and city-gas consumption of the whole house and room temperature, for the purpose of motivating energy-saving activities has been constructed and the effectiveness of the system investigated by installing it in 10 residential buildings. The experiment showed that energy-saving consciousness was raised and energy consumption was in fact reduced by the energy-saving activities of the household members. In this paper, the system is described in detail and the effectiveness of reducing energy-consumption of the whole house and for space heating will be discussed. Also the energy-saving activities in a certain household are shown by using load duration curves.     

Prediction of greenhouse gas reduction potential in Japanese residential sector by residential energy end-use model

A model is developed that simulates nationwide energy consumption of the residential sector by considering the diversity of household and building types. Since this model can simulate the energy consumption for each household and building category by dynamic energy use based on the schedule of the occupants’ activities and a heating and cooling load calculation model, various kinds of energy-saving policies can be evaluated with considerable accuracy. In addition, the average energy efficiency of major electric appliances used in the residential sector and the percentages of housing insulation levels of existing houses is predicted by the “stock transition model.” In this paper, energy consumption and CO₂ emissions in the Japanese residential sector until 2025 are predicted. For example, as a business – as-usual (BAU) case, CO₂ emissions will be reduced by 7% from the 1990 level. Also evaluated are mitigation measures such as the energy efficiency standard for home electric appliances, thermal insulation code, reduction of standby power, high-efficiency water heaters, energy-efficient behavior of occupants, and dissemination of photovoltaic panels.     

When do energy-efficient appliances generate energy savings? Some evidence from Canada

Improvements in the energy efficiency of household appliances have the potential to decrease residential energy use, but these reductions accrue gradually over time as newer appliances replace older models. SHEU-2003 data are used to examine appliance replacement patterns in Canada for refrigerators, freezers, dishwashers, clothes washers and clothes dryers. The data indicate that the ages at which appliances are replaced tend to be lowest for dishwashers and highest for freezers, with over 40% of freezers in use for more than 20 years before being retired. The life spans of Canadian appliances are compared to the underlying assumptions regarding appliance lifetimes used in models of residential energy demand. We find that Canadian appliance retirement patterns differ from those assumed in the previous literature. Socioeconomic factors related to appliance replacement are also examined. We find that replacement patterns can be sensitive to household characteristics such as income, providing evidence that there may be scope for targeted policies aimed at inducing earlier replacements of older household appliances with new energy-efficient models.     

Energy use and appliance ownership in Ireland

This paper examines household energy use and appliance ownership in Ireland. Logit regression analyses on a large micro-dataset reveal how household characteristics can help explain the ownership of energy using appliances. Using OLS regression models, we explore the factors affecting residential energy demand conditional on appliance ownership. Results suggest that the methods of space and water heating employed by a household are even more important than electrical appliances in explaining domestic energy usage. However, the stock of appliances must be included in such models so that results will not be biased. The methods employed in this paper can be easily adopted for studies of household energy use in other countries where household expenditure survey data are available.     

Short-term prediction of household electricity consumption: Assessing weather sensitivity in a Mediterranean area

Urban microclimatic variations, along with a rapid reduction of unit cost of air-conditioning (AC) equipments, can be addressed as some of the main causes of the raising residential energy demand in the more developed countries. This paper presents a forecasting model based on an Elman artificial neural network (ANN) for the short-time prediction of the household electricity consumption related to a suburban area. Due to the lack of information about the real penetration of electric appliances in the investigated area and their utilization profiles it was not possible to implement a statistical model to define the weather and climate sensitivities of appliance energy consumption. For this reason an ANN model was used to predict the household electric energy demand of the investigated area and to evaluate the influence of the AC equipments on the overall consumption.The data used to train the network were recorded in Palermo (Italy) and include electric current intensity and weather variables as temperature, relative humidity, global solar radiation, atmospheric pressure and wind speed values between June 1, 2002 and September 10, 2003.The work pointed out the importance of a thermal discomfort index, the Humidex index, for a simple but effective evaluation of the conditions affecting the occupant behaviour and thus influencing the household electricity consumption related to the use of heating, ventilation and air conditioning (HVAC) appliances. The prediction performances of the model are satisfying and bear out the ability of ANNs to manage incomplete and noisy data, solve nonlinear problems and learn complex underlying relationships between input and output patterns.     

Read the label! Energy Star appliance label awareness and uptake among U.S. consumers

The Energy Star label program to promote the diffusion of energy efficient home appliances is arguably the most significant government effort to reduce U.S. residential energy consumption. Program effectiveness requires that consumers are aware of the labeling scheme and also change their purchase decisions based on label information. This paper examines the factors associated with consumer awareness of the Energy Star label of recently purchased ‘white’ major appliances and the factors associated with the choice of Energy Star labeled appliances. The paper finds that household characteristics have a much stronger association with consumer awareness of labels than with the choice of Energy Star appliances. Renting the home, Hispanic ethnicity, being poor or near poor, and living in regions with lower ACEEE scores do, however, decrease the propensity for households to purchase Energy Star appliances. Eliminating these gaps in Energy Star appliance adoption would result in house electricity cost savings of $164 million per year and associated carbon emission reductions of about 1.1 million metric tons per year.     

Evaluation of city-scale impact of residential energy conservation measures using the detailed end-use simulation model

Energy conservation policies for the residential sector are evaluated by a model that simulates city-scale energy consumption in the residential sector by considering the diversity of household and building types. In this model, all the households in the city are classified into 380 categories based on the household and building type. The energy consumption for each household category is simulated by the dynamic energy simulation model, which includes an energy use schedule model and a heating and cooling load calculation model. Since the energy usage of each appliance is simulated for every 5 min according to the occupants’ energy usage activity, this model can evaluate not only the energy conservation measures by improving the buildings and appliances but also the measures that involve changing the occupants’ activities. The accuracy of the model is verified by comparing its results with the statistical and the measured data on Osaka City, Japan. Various types of energy conservation measures planned by the Japanese government for the residential sector are simulated and their effects on Osaka City are evaluated quantitatively. The future effects of these combined measures on the energy consumption are also predicted.     

Modelling of electricity savings in the Danish households sector: from the energy system to the end-user

In this paper, we examine the value of investing in energy-efficient household appliances from both an energy system and end-user perspectives. We consider a set of appliance categories constituting the majority of the electricity consumption in the private household sector, and focus on the stock of products which need to be replaced. First, we look at the energy system and investigate whether investing in improved energy efficiency can compete with the cost of electricity supply from existing or new power plants. To assess the analysis, Balmorel, a linear optimization model for the heat and power sectors, has been extended in order to endogenously determine the best possible investments in more efficient home appliances. Second, we propose a method to relate the optimal energy system solution to the end-user choices by incorporating consumer behaviour and electricity price addition due to taxes. The model is non-exclusively tested on the Danish energy system under different scenarios. Computational experiments show that several energy efficiency measures in the household sector should be regarded as valuable investments (e.g. an efficient lighting system) while others would require some form of support to become profitable. The analysis quantifies energy and economic savings from the consumer side and reveals the impacts on the Danish power system and surrounding countries. Compared to a business-as-usual energy scenario, the end-user attains net economic savings in the range of 30–40 EUR per year, and the system can benefit of an annual electricity demand reduction of 140–150 GWh. The paper enriches the existing literature about energy efficiency modelling in households, contributing with novel models, methods, and findings related to the Danish case.     

A systematic approach in appliance disaggregation using k-nearest neighbours and naive Bayes classifiers for energy efficiency

One of the ways to achieve energy efficiency in various residential electrical appliances is with energy usage feedback. Research work done showed that with energy usage feedback, behavioural changes by consumers to reduce electricity consumption contribute significantly to energy efficiency in residential energy usage. In order to improve on the appliance-level energy usage feedback, appliance disaggregation or non-intrusive appliance load monitoring (NIALM) methodology is utilized. NIALM is a methodology used to disaggregate total power consumption into individual electrical appliance power usage. In this paper, the electrical signature features from the publicly available REDD data set are extracted by the combination of identifying the ON or OFF events of appliances and goodness-of-fit (GOF) event detection algorithm. The k-nearest neighbours (k-NN) and naive Bayes classifiers are deployed for appliances’ classification. It is observed that the size of the training sets effects classification accuracy of the classifiers. The novelty of this paper is a systematic approach of NIALM using few training examples with two generic classifiers (k-NN and naive Bayes) and one feature (power) with the combination of ON-OFF based approach and GOF technique for event detection. In this work, we demonstrated that the two trained classifiers are able to classify the individual electrical appliances with satisfactory accuracy level in order to improve on the feedback for energy efficiency.     

Energy labeling for electric fans in Malaysia

To reduce energy consumption in the residential sector, Malaysia Energy Commission is considering implementing energy labels for household electrical appliances including electric fans in 2005. The purpose of the energy labels is to provide the consumers a guideline to compare the size, features, price and efficiency of the appliance. This paper discusses the energy label for electric fans in this country based on Malaysian Standards developed by a technical committee that reviewed the performance of household electrical appliances. This study includes methodology for the calculation of the energy efficiency star rating and projected energy usage, performance requirements, details of the energy label and the requirements for the valid application in Malaysia. The label also can be adopted for other household electrical appliances with only slight modifications.     

Demographics,energy and our homes

The projected growth in households in the UK is a key factor in future domestic energy consumption, particularly electricity consumption. While every household needs a home and its heating, lighting and appliances, increasing incomes have historically led to significantly higher appliance ownership, higher expectations of levels of energy service and greater usage. In the past this trend was combined with increasing household numbers to drive growth in domestic electricity demand. Official projections for population growth and household composition indicate significant drivers for future growth in energy demand. Curbing this will require policies to reverse the tendency for energy–efficiency improvements to be overwhelmed by growing numbers of households, more widespread appliance ownership and increased service expectations.     

Correction of load curves estimated by electrical appliances ownership surveys using mass memory meters

Through the PPHs (Energy Audit on Ownership and Usage of Electrical Appliances), one has a rough idea of the daily load shape curves by appliance. However, the curves obtained this way tend to be a little inaccurate, as they are generated by the consumer survey information of usage of the equipments, which tend to be imprecise information. Despite its inaccuracy, the energy audits (PPHs) are a simple and cheap way to understand equipment ownership and consumption habits of the residential consumers of such a large country as Brazil. In this work, it presented a statistical-based model that allows a better calibration of the load shape curve for appliances for residential consumers using information from two sources: PPHs and household measurements through specific devices that provide real-time measures of the total consumption. Two methodologies using linear regression were tested, one considering a two parameter linear model and another one considering only the slope parameter. It is shown that the latter produced better results.     

A systematic approach in load disaggregation utilizing a multi-stage classification algorithm for consumer electrical appliances classification

The potential to save energy in existing consumer electrical appliances is very high. One of the ways to achieve energy saving and improve energy use awareness is to recognize the energy consumption of individual electrical appliances. To recognize the energy consumption of consumer electrical appliances, the load disaggregation methodology is utilized. Non-intrusive appliance load monitoring (NIALM) is a load disaggregation methodology that disaggregates the sum of power consumption in a single point into the power consumption of individual electrical appliances. In this study, load disaggregation is performed through voltage and current waveform, known as the V-I trajectory. The classification algorithm performs cropping and image pyramid reduction of the V-I trajectory plot template images before utilizing the principal component analysis (PCA) and the k-nearest neighbor (k-NN) algorithm. The novelty of this paper is to establish a systematic approach of load disaggregation through V-I trajectory-based load signature images by utilizing a multi-stage classification algorithm methodology. The contribution of this paper is in utilizing the “k-value,” the number of closest data points to the nearest neighbor, in the k-NN algorithm to be effective in classification of electrical appliances. The results of the multi-stage classification algorithm implementation have been discussed and the idea on future work has also been proposed.     

Energy efficiency standards for refrigerators in Brazil: A methodology for impact evaluation

In Brazil energy efficiency standards for cold appliances was established in 2007. A specified single set of MEPS (minimum energy performance standards) for refrigerators, freezers and freezer refrigerators was implemented without evaluating its impacts and estimation of potential electricity savings. This paper presents a methodology for assessing the impacts of the Brazilian MEPS for cold appliances. It uses a bottom-up approach to estimate residential end-use consumption and to evaluate the energy saving potential for refrigerators. The household electricity consumption is projected by modeling appliance ownership using an econometric approach based on the recent household survey data. A cost–benefit analysis for more stringent standards is presented from the perspective of the society and electricity customers. The results showed that even considering the current market conditions (high discount rate for financing new efficient equipment) some MEPS options are advantageous for customers. The analysis also demonstrates significant cost-effective saving potential from the society perspective that could reach 21 TWh throughout the period of 2010–2030—about 25% of current residential consumption.     

Household electricity demand forecast and energy savings potential for Vientiane,Lao PDR

This paper uses the survey data on household electricity demand from five districts of Vientiane, Lao PDR, for the demand projection up to 2030 using the end-use model. The scenario analysis is used to verify the potential of an energy-saving program by alternating selected appliances with more energy-efficient ones following the labelling standard of Thailand. The demographic structure of electrified households and the energy efficiency of electric appliances are considered as the dominant factors affecting electricity demand. Under the base-case scenario, the total electricity demand of Vientiane increased from 593?GWh in 2013 to 965?GWh in 2030. In the energy efficiency scenario, it is revealed that the appliance standard enhancement program can save total electricity demand in 2030 by 147?GWh (?15.2%), where 117?GWh (?12.1%) of which is contributed by the air conditioner and 30?GWh (?3.1%) by the lighting equipment.     

How do household characteristics affect appliance usage? Application of conditional demand analysis to Japanese household data

Although both appliance ownership and usage patterns determine residential electricity consumption, it is less known how households actually use their appliances. In this study, we conduct conditional demand analyses to break down total household electricity consumption into a set of demand functions for electricity usage, across 12 appliance categories. We then examine how the socioeconomic characteristics of the households explain their appliance usage. Analysis of micro-level data from the Nation Survey of Family and Expenditure in Japan reveals that the family and income structure of households affect appliance usage. Specifically, we find that the presence of teenagers increases both air conditioner and dishwasher use, labor income and nonlabor income affect microwave usage in different ways, air conditioner usage decreases as the wife's income increases, and microwave usage decreases as the husband's income increases. Furthermore, we find that households use more electricity with new personal computers than old ones; this implies that the replacement of old personal computers increases electricity consumption.     

What's driving energy efficient appliance label awareness and purchase propensity?

The EU appliance energy consumption labeling scheme is a key component of efforts to increase the diffusion of energy-efficient household appliances. In this paper, the determinants of consumer knowledge of the energy label for household appliances and the choice of class-A energy-efficient appliances are jointly estimated using data from a large survey of more than 20,000 German households. The results for five major appliances suggest that lack of knowledge of the energy label can generate considerable bias in both estimates of rates of uptake of class-A appliances and in estimates of the underlying determinants of choice of class-A appliance. Simulations of the choice to purchase a class-A appliance, given knowledge of the labeling framework, reveal that residence characteristics and, in several cases, regional electricity prices strongly increase the propensity to purchase a class-A appliance, but socio-economic characteristics have surprisingly little impact on appliance energy-class choice.     

Energy in american homes: Changes and prospects

Average energy consumption per U.S. household has fallen by just under 20% in the last ten years. Much of this drop occurred after 1979, when gas and electricity prices as well as oil prices rose in real terms. The response of households to higher prices has involved physical modifications on and in the home and changes in behavior. Many actions have been taken by households, but the most important single factor has been a significant reduction in indoor temperatures. The greater energy efficiency of new homes and appliances has also helped to depress residential energy demand, although improvements have levelled off in the last few years. There are signs that the momentum of energy conservation is less now than it was 2 years ago, but it appears that energy prices will be high enough to discourage households from returning to former energy-using practices. Along with the continued replacement of homes and appliances with more efficient models, and other factors such as the migration to wanner regions and the movement to more apartments and smaller homes, this will probably keep U.S. residential energy consumption at about its present level through the 1980s.     

State identification of home appliance with transient features in residential buildings

Nonintrusive load monitoring (NILM) is crucial for extracting patterns of electricity consumption of household appliance that can guide users’ behavior in using electricity while their privacy is respected. This study proposes an online method based on the transient behavior of individual appliances as well as system steady-state characteristics to estimate the operating states of the appliances. It determines the number of states for each appliance using the density-based spatial clustering of applications with noise (DBSCAN) method and models the transition relationship among different states. The states of the working appliances are identified from aggregated power signals using the Kalman filtering method in the factorial hidden Markov model (FHMM). Thereafter, the identified states are confirmed by the verification of system states, which are the combination of the working states of individual appliances. The verification step involves comparing the total measured power consumption with the total estimated power consumption. The use of transient features can achieve fast state inference and it is suitable for online load disaggregation. The proposed method was tested on a high-resolution data set such as Labeled hIgh-Frequency daTaset for Electricity Disaggregation (LIFTED) and it outperformed other related methods in the literature.     

青岛市节能潜力评估

青岛市作为典型的能源输入型城市,未来面临着巨大的能源、环境压力.影响青岛市能源利用效率的因素主要包括能源消费结构、产业结构、重点用能企业单位产品综合能耗、技术进步、居民的生活方式和节能意识等.从结构节能、重点领域节能和管理节能三个方面对青岛市的节能潜力进行评估,结果表明,青岛市“十二五”期间每年的节能潜力合计约为813.34×104t标煤.其中,产业结构年均节能潜力约46.71×104t标煤,相关工业行业每年最大节能潜力约为494.92× 104t标煤,居住建筑和公共建筑每年节能潜力约为70.31×104t标煤,交通领域年节能潜力61.7×104t标煤,居民家用电器年均节能潜力约22.7×104t标煤,农业领域直接节能潜力每年约117×104t标煤.未来青岛市应努力降低煤炭消费比重,提高第三产业比重;加快地方性配套法规和标准建设;进一步完善节能目标责任制,建立和完善省、市、区市三级节能监察体制,强化约束性节能监管;围绕节能产业化方向,加大节能投入,完善节能技术创新体系建设;全面深入地开展节能宣传活动,培养公众低碳节能的生活方式.