Structural-behavioral determinants of residential energy use: Summer electricity use in Davis

Engineering models of the physical processes of energy use in individual houses are quite complex. We investigate simple statistical models of summer electricity use and compare them to engineering models. Our data include interviews, energy audits, and utility billings for a random sample of residences in Davis, California. We predict summer kWh using appliance and cooling-load models. The appliance model is based on manufacturers' or published data on average annual kWh used by major appliances; refinements for appliance location, seasonality and frequency of use have mixed success. The cooling-load model includes the major variables used in the DOE 2.1A simulation; coefficients estimated by a multiple regression model closely resemble interpolation parameters derived from DOE 2.1A. The appliance and cooling-load models explain over 50% of the variation in summer kWh in single-family detached houses. Using the appliance model and only two variables from the cooling-load model, house area and self-reported frequency of air-conditioner use, we explain nearly 60% of summer kWh in houses. The simple interview question on frequency of air-conditioner use captures most of the effects of structural features such as insulation and glazing. Finally, the appliance and cooling-load models are applied successfully to other house types (common-wall houses and apartments).     

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.     

A model for generating household electricity load profiles

Electricity consumption data profiles that include details on the consumption can be generated with a bottom‐up load models. In these models the load is constructed from elementary load components that can be households or even their individual appliances. In this work a simplified bottom‐up model is presented. The model can be used to generate realistic domestic electricity consumption data on an hourly basis from a few up to thousands of households. The model uses input data that is available in public reports and statistics. Two measured data sets from block houses are also applied for statistical analysis, model training, and verification. Our analysis shows that the generated load profiles correlate well with real data. Furthermore, three case studies with generated load data demonstrate some opportunities for appliance level demand side management (DSM). With a mild DSM scheme using cold loads, the daily peak loads can be reduced 7.2% in average. With more severe DSM schemes the peak load at the yearly peak day can be completely levelled with 42% peak reduction and sudden 3 h loss of load can be compensated with 61% mean load reduction. Copyright © 2005 John Wiley & Sons, Ltd.     

CO2-emissions reduction potential and costs of a decentralized energy system for providing electricity,cooling and heating in an office-building in Tokyo

Decentralized energy systems are thought to have great potential for supplying electricity, cooling, and heating to buildings. A decentralized system combining a solid oxide fuel cell (SOFC) with an absorption chiller-heater (ACH) is proposed. The CO₂-emissions and costs of using different configurations of this SOFC-based system to provide an office building in Tokyo with electricity, cooling and heating are calculated by using an SOFC-model and an absorption-chiller model together with data for cooling and heating loads measured at an office building in downtown Tokyo. The results are compared with the CO₂-emissions and costs of a conventional system that obtains the base electricity requirements as well as electricity for an electric chiller–heater system from the central power grid. The fully decentralized SOFC-based energy system could result in a potential CO₂ reduction of over 30% at an estimated cost increase of about 70% compared to the conventional system.     

Linking appliance efficiency and space conditioning loads in residential energy-demand projections

We describe the incorporation of interaction between appliance efficiencies and spaceconditioning loads into a version of the Oak Ridge National Laboratory (ORNL) computer-simulation model, used by the Northwest Power Planning Council to project residential demand for electricity in the Pacific Northwest. Projections of total demand and estimated impacts of an illustrative appliance efficiency standard are compared with projections generated by the unimproved version of the ORNL model. Projections of total demand by the new model are not greatly different from the projections of the original. However, the new model produces estimates of savings from the illustrative appliance efficiency standard which are substantially less than the original model. We point out unique conditions of the Pacific Northwest which make it impossible to use these results to draw conclusions for other regions.     

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.     

Patterns of residential wood and electricity use: Results from the Hood River Conservation Project

We identify significantly different daily patterns of wood and electricity use among 100 houses that contained both wood heat output and electricity use submeters installed as part of the Hood River Conservation Project. It is found that patterns include low wood/high electricity, high wood/low electricity, and low wood/low electricity profiles. Profiles are not differentiable by day of week but differ widely as outdoor temperatures drop. Houses that are small in size tend to use high electricity/low wood patterns whereas low electricity/high wood patterns are utilized by larger households and houses.     

Quantity and electricity consumption of plug load equipment on a university campus

The percent of energy consumed by plug load equipment in commercial buildings is on the rise. Research conducted in the past has included surveying plug load equipment, measuring plug load electricity consumption and equipment operating patterns, and studying plug load reduction solutions in office buildings, but plug load energy use across other building types is poorly understood. A university campus, which houses many building types, presents a unique opportunity to understand plug load profiles across building types. In this study, an equipment inventory was performed in 220 buildings on Stanford University’s campus, totaling 8,901,911 ft² of building space and encompassing lab buildings, office buildings, recreation facilities, public space, and service buildings. Within these buildings, 110,529 pieces of plug load equipment were recorded. Energy consumption estimates were developed from published values and used to evaluate the aggregate plug load energy consumption of this equipment by equipment type and by building type. In total, it is estimated that the plug loads from these buildings consume nearly 50 million kWh per year and comprise 32% of the electricity consumption of the buildings surveyed. This data can be used to better target energy conservation efforts throughout multiple sectors.     

Residential demand for electricity

Short- and long-run responses by households to changes in the price of electricity are estimated using data which permit measurement of the marginal price of electricity, the infra-marginal demand charge, and estimates of household appliance stocks. The price elasticities of high- and low-level users of electricity are compared. The theoretical bias in price elasticity estimates resulting from neglect of the infra-marginal demand charge is shown to be empirically insignificant.     

Trends in unit energy consumption: The performance of end-use models

This paper estimates models of electricity and gas consumption for individual households using the Miracle 4 to 6 data sets collected by San Diego Gas and Electricity Company. Two types of model were constructed: the first involves typical end-use models with consumption explained by appliance ownership, household demographic characteristics, house dimensions and household income; the second class uses these variables plus consumption data for the previous year. The latter models consistently fitted better, while the end-use variables afforded little explanatory power. The results thus suggest that simple end-use models are of little value, at least for short-run forecasting. Their use for long-run forecasting has yet to be evaluated.     

Ecological and economic efficiency of the use of alternative energy technologies including hydrogen to reduce of the anthropogenic load in the central ecological area of the Baikal natural territory

The central ecological area of the Baikal natural territory covers some districts of the Irkutsk oblast and the Republic of Buryatia, located on the coast of the Lake Baikal. Due to the natural uniqueness and special status of doing economic activity, the assessment of the impact on the environment in this territory is very importance.An analysis of the functioning of energy objects showed that a significant part of the territory is provided with a centralized electricity supply with developed electric grid infrastructure. There are only a few remote settlements with autonomous electricity supply from diesel power plants.The main sources of pollution are numerous boiler houses that provide heat to the population, social and administrative institutions. In all, there are 98 heat energy sources in the territory, of which 66 (or 70%) use coal.The problems of environmental pollution are mainly caused by the use of coal in a small boiler house, worn-out equipment, and the lack of an appropriate level of flue gas treatment. The total estimated emission of pollutants into the atmosphere from heat energy sources is estimated at 20–25 thousand tons per year.In order to reduce the anthropogenic impact from energy objects, it is advisable to use renewable energy sources, hydrogen technologies, coal substitution with environmentally friendly fuels, use of electricity for heat energy supply, installation of environmental protection equipment and the implementation of energy-saving measures.The methodological approach and simulation models developed at MESI SB RAS were used to determine the competitiveness conditions of alternative technologies and energy carriers.The studies evaluated the environmental and economic efficiency of energy production technologies by using specific indicators: the capital intensity of reducing 1 ton of emissions and environmental capital return by 1 million rubles for the conditions of the central ecological area.The potential for reducing emissions into the atmosphere by use of renewable energy sources in autonomous energy supply areas is less than 1% of the current level of total emissions from energy objects. The potential for reducing emissions by replacing boiler houses with a capacity of less than 0,2 Gcal/h by a heat pump units is no more than 12%.The biggest environmental effect can be achieved by using alternative energy carriers including hydrogen instead of coal. Moreover, the potential for reducing emissions is 60% of the total emissions. In addition to these activities are the least capital intensive.The most effectively is the replacement of coal with natural gas. Rational gas consumption in the coastal areas of Lake Baikal is estimated at 175–190 thousand tons of equivalent fuel. The real possibility of transferring small boiler houses to gas arises during the construction of an export gas pipeline from Russia (through the territory of the Irkutsk oblast) to China via Mongolia, or by the small-scale production of liquefied natural gas.The most currently implemented direction is the use of electricity for heat energy supply. The potential volume of electricity to replace coal in boiler houses of the central ecological area is 1,3 TWh per year, however, the competitive electricity tariff is estimated less than 2 US c/kWh, which is several times lower than current tariffs.Hydrogen technology is currently very capital-intensive, but using it in a way similar to using electricity for heat eliminates pollutant and greenhouse gas emissions.Now days, there are no effective financial mechanisms aimed at stimulating the reduction of the anthropogenic pressure on the environment from existing energy sources, including for the use of alternative technologies. As the result, significant financial support is required in the form of special cost compensation mechanisms for energy producers and/or consumers.     

Investigating the feasibility of positive energy residential buildings in tropical climates

Positive energy residential buildings are houses that generate more energy from renewable sources than they consume while maintaining appropriate thermal comfort levels. However, their design, construction and operation present several critical challenges. In particular, the considerable load reductions are not always compatible with the increased level of comfort expected in modern houses. Tropical climates, meanwhile, should be more amenable to the implementation of positive energy houses for two reasons. Firstly, negligible heating is generally required as compared to colder climates, where the heating energy requirements are considerable. Then, renewable energy resources are usually abundant in tropical climates. This paper investigates the feasibility of positive energy residential buildings in the tropical island of Mauritius. A baseline model representing a typical Mauritian house is designed using DesignBuilder software. The energy efficiency of the model is then optimised by investigating a whole range of passive building design strategies, many of them adapted from vernacular architecture. Results reveal that the application of passive strategies such as shading, insulation and natural ventilation have precluded the need for artificial cooling and ventilation in the positive energy (PE) house. The resulting electricity consumption of the house decreases from 24.14 to 14.30 kWh/m²/year. A 1.2 kW photovoltaic system provides the most cost-effective solution to exceed the annual electricity requirements of the house.     

Economic analysis of small wind-energy conversion systems

The financial costs of obtaining electricity from small wind-energy conversion systems are calculated and compared with the cost of electricity from traditional utility companies. A 3 kW rated wind electric system for residential use is examined. The amount of energy from this system is estimated by using a computer-operated simulation model which incorporates wind speeds, residential electricity demands and parameters from the generator, inverter and storage components. Variations in electricity costs as a function of available wind energy, residential consumption patterns and system components and costs are examined. The cost of electricity from small wind-energy conversion systems has considerable variations under differing assumptions or parameters but may, in some cases, be competitive with utility company costs.     

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.     

Heating technology and energy use: a discrete/continuous choice approach to Norwegian household energy demand

The aim of this paper is to describe the structure of the household’s energy demand as a discrete/continuous choice and, on this basis, establish an econometric model suitable for the data available in the Norwegian Energy Surveys. The discrete appliance choice is specified as a multinomial logit model, with a mixture of appliance attributes (operating costs) and individual characteristics (income, housing unit characteristics, etc.) as explanatory variables. In the next step the continuous choice of energy use is modelled conditional on the appliance choice. The energy prices turn out to be significant both when estimating the appliance choice and the conditional energy demand. The estimated price elasticity for energy exceeds minus unity. The paper discusses how this relatively strong price response should be interpreted in the context of other econometric analysis with no explicit appliance dependence. Finally, the significance of the many household characteristics at both stages of the model signals a high degree of heterogeneity within the households, which justifies the use of detailed micro-data in the modelling of the energy demand.     

Long term building energy demand for India: Disaggregating end use energy services in an integrated assessment modeling framework

With increasing population, income, and urbanization, meeting the energy service demands for the building sector will be a huge challenge for Indian energy policy. Although there is broad consensus that the Indian building sector will grow and evolve over the coming century, there is little understanding of the potential nature of this evolution over the longer term. The present study uses a technologically detailed, service based building energy model nested in the long term, global, integrated assessment framework, GCAM, to produce scenarios of the evolution of the Indian buildings sector up through the end of the century. The results support the idea that as India evolves toward developed country per-capita income levels, its building sector will largely evolve to resemble those of the currently developed countries (heavy reliance on electricity both for increasing cooling loads and a range of emerging appliance and other plug loads), albeit with unique characteristics based on its climate conditions (cooling dominating heating and even more so with climate change), on fuel preferences that may linger from the present (for example, a preference for gas for cooking), and vestiges of its development path (including remnants of rural poor that use substantial quantities of traditional biomass).     

Residential hot water: A behaviorally-driven system

Residential water use was intensively studied in 7 houses, using electronic recorders and open-ended interviewing. These houses exhibited large variation in total hot water volume (89–5061. daily) and in heat used per person (6.5–16.9 MJ daily). The variation is attributed primarily to behavioral differences among the occupants.In most houses, bathing accounts for the largest volume use, while the kitchen accounts for the largest number of uses and the second largest volume. Bathing is investigated further by examination of individual showers. Combining interview and instrument data, a person who takes 201., 3 MJ showers is compared with one who takes 1001., 15 MJ showers.A measure of piping-distribution efficiency is defined as heat delivered to the tap over heat sent from the water tank. Small-volume uses are frequent and are very inefficient but account for only a small proportion of the total hot water. Total piping-distribution efficiencies, in the two houses for which they could be computed, were 0.91 and 0.88.     

Residential demand for wood: Results from the U.S. Pacific Northwest

We analyze household decisions to use wood for space heating. Logistic regression and simultaneous equation models are estimated by using billing history and survey data collected from households in the Pacific Northwest. Strong positive determinants of wood use are size of the household, heating degree days, and future electricity prices. Results from the simultaneous equation model indicate that wood and electricity demand are not strongly interrelated. Wood use is weakly positively related to electricity use in one equation and electricity use is weakly negatively related to wood use in the other.     

Common long-range dependence in a panel of hourly Nord Pool electricity prices and loads

Equilibrium electricity spot prices and loads are often determined simultaneously in a day-ahead auction market for each hour of the subsequent day. Hence daily observations of hourly prices take the form of a periodic panel rather than a time series of hourly observations. We consider novel panel data approaches to analyse the time series and the cross-sectional dependence of hourly Nord Pool electricity spot prices and loads for the period 2000–2013. Hourly electricity prices and load data are characterized by strong serial long-range dependence in the time series dimension in addition to strong seasonal periodicity, and along the cross-sectional dimension, i.e. the hours of the day, there is a strong dependence which necessarily has to be accounted for in order to avoid spurious inference when focusing on the time series dependence alone. The long-range dependence is modelled in terms of a fractionally integrated panel data model and it is shown that both prices and loads consist of common factors with long memory and with loadings that vary considerably during the day. Due to the competitiveness of the Nordic power market the aggregate supply curve approximates well the marginal costs of the underlying production technology and because the demand is more volatile than the supply, equilibrium prices and loads are argued to identify the periodic power supply curve. The estimated supply elasticities are estimated from fractionally co-integrated relations and range between 0.5 and 1.17 with the largest elasticities being estimated during morning and evening peak hours.     

Electricity end-uses in the residential sector of Brazil

The residential energy consumption has been studied in many countries as it usually accounts for a large percentage of the total energy consumption. Energy end-uses have also been a matter of concern as they can assist energy system planning. The objective of this paper is to assess the actual scenario of electricity consumption and estimate electricity end-uses in the residential sector of Brazil for different bioclimatic zones. The analysis is based on a survey performed by 17 energy utilities enclosing a total of 17,643 houses or flats over 12 states in Brazil. The survey was performed to obtain electricity consumption data for all household appliances found in houses and flats. The electricity end-uses were estimated by performing weighted averages according to the location of the dwellings in each bioclimatic zone. Results indicate that the largest end-uses are for refrigerator and freezer together, which account for about 38–49% of the electricity consumption in dwellings in Brazil. Air-conditioning and electric shower are the end-uses that are more dependent on the climatic conditions. The main conclusion that can be made from the analysis is that air-conditioning should be a major concern in the residential sector of Brazil in the near future as its ownership is still low, but its electricity consumption is already significant mainly over summer.