Real-life energy use in the UK: How occupancy and dwelling characteristics affect domestic electricity use

The patterns of electricity consumption were studied for 27 representative dwellings in Northern Ireland. The type of dwelling, its location, ownership and size, household appliances, attributes of the occupants including number of occupants, income, age and occupancy patterns have differing but significant impacts on electricity consumption. A clear correlation was found between average annual electricity consumption and floor area. The monthly consumption of detached houses is between 3.57 and 5.17 kWh m⁻²; semi-detached between 3.44 and 4.59 kWh m⁻² and terraced houses between 2.5 and 3.9 kWh m⁻². The average winter consumption exceeded the average summer consumption by 1.59 kWh m⁻² for detached, by 1.16 kWh m⁻² for semi-detached and by 1.78 kWh m⁻² for terraced houses. The difference in the annual demand on the grid between detached and terraced houses is between 24 and 30%. The electricity consumption per person decreases as the number of occupants increases. This is particularly significant in large dwellings but smaller numbers of occupants.     

Characterization of the household electricity consumption in the EU, potential energy savings and specific policy recommendations

Although significant improvements in energy efficiency have been achieved in home appliances and lighting, the electricity consumption in the European Union household has increased by 2% per year during the past 10 years. Some reasons are associated with an increased degree of basic comfort and level of amenities and with the widespread utilisation of new types of loads. Wishing to increase the understanding of the energy consumption in the EU households for the different types of equipment including the consumers’ behaviour and comfort levels, and to identify demand trends, an energy monitoring campaign, was carried out in 12 geographically representative EU countries, accompanied by a lifestyle survey. From the measurements carried out it was concluded that Information Technologies and entertainment loads are key contributors to the power demand. In basically all types of loads there is wide range of performance levels in the models available in the market. Available technology, associated with responsible consumer behaviour, can reduce wasteful consumption. Based on a bottom up approach the European residential sector potential electricity savings that can be implemented by existing technologies and improved behaviour can reach 48%. The paper presents policy recommendations promoting market transformation and behavioural changes in the equipment selection and operation.     

The impact of renewable energy on household electricity prices in liberalized electricity markets: A cross-national panel data analysis

The main research questions addressed in this paper are: first, have electricity market reforms achieved lower household electricity prices and, second, has the introduction of renewable energy increased household electricity prices in deregulated markets Answers to the questions were derived using static and dynamic panel data analysis from 1991 to 2014 employing explanatory variables such as the extent of electricity market reform and the share of generation from renewable energy resources. The dynamic model suggests that a lower household electricity price is associated with the degree of electricity market reform, while the share of renewable energy in electricity generation is not statistically significant.     

Industrial electricity consumption efficiency and energy policy in Japan

This study analyzes the potential for energy savings by identifying inefficiencies in electricity use and their determinants in the Japanese industrial sector. Specifically, we used stochastic frontier analysis to estimate inefficiencies in electricity consumption based on data obtained from electric power companies. We identified significant determinants of electricity consumption efficiency and that changes in national energy policy following the Great East Japan Earthquake changed electricity consumption behavior. The contribution of this study is that its findings can be used to improve the cost-effectiveness of policies aimed at improving energy efficiency.     

Predicting residential electricity consumption patterns based on smart meter and household data: A case study from the Republic of Ireland

We use machine learning algorithms to investigate various aspects of residential electricity consumption for households in the Republic of Ireland. Temperature, day of week, and month of year have an apparent causal effect on consumption. The prevalence of six distinct intra-day load profiles, identified by clustering, changes dramatically between weekdays and weekends as well as seasonally. Key socio-demographic and dwelling characteristics associated with annual load profiles include household makeup and size and occupation of the primary income earner. We further discuss policy and management implications of our findings and propose avenues for future research.     

Heat consumption for preparing domestic hot water in hospitals

This study analyses heat consumption for heating domestic water in large hospital facilities with over 600 hospital beds. The tests were carried out in 2 hospitals: the 715-bed University Hospital in Bydgoszcz and the 690-bed Provincial Hospital. The tests were performed over a period of 4 years: 2005-2008 for the first hospital and 2003-2006 for the second.The aim of this study is to analyse the variations and seasonal changes in the heat consumed to produce domestic hot water during the specified time periods. The results of this study show the yearly, monthly, daily, and hourly consumption of heat for domestic hot water. Particular emphasis is placed on the daily and hourly heat-consumption irregularity coefficients for domestic hot water.The results of this study may be used, for example, to predict heat consumption when designing new hospital facilities with 500-800 beds, to determine the heating power of the heat source and individual centres. Data on the constant and low-temperature heat consumption for domestic hot water throughout the whole year are particularly useful, as these data may be used to analyse existing large hospitals that are seeking alternative solutions (such as renewable energy or energy recycling) to reduce fossil fuel consumption.     

Measuring the use of residential standby power in Taiwan

Due to the scientific and economic growth of recent years, the on-going innovation in electrical products has enabled people to live more comfortably and conveniently. The standby power function is one such innovation. Unfortunately, as an increasing number of products are equipped with standby power, more energy is wasted and more carbon dioxide is emitted into the atmosphere. This paper combines a two-step proportional stratified sampling technique with systematic random sampling to select representative households in Taiwan. We adopted the measurement method of the International Electro technical Commission (IEC) in measuring the overall use of standby power in Taiwanese households. We then proposed a model for the estimation of standby power consumption, before calculating annual standby power consumption among all households in Taiwan. This paper could serve as a reference for the government and relevant authorities in the establishment of corresponding standards, providing suggestions for the minimization of standby power consumption and the unnecessary waste of energy.     

Rapid analysis of time series data to identify changes in electricity consumption patterns in UK secondary schools

This paper presents a methodology for energy professionals to identify potential electricity saving opportunities in buildings from the analysis of half-hourly electricity consumption data. The technique recommended in UK government good practice guidance for use with monthly gas data has been applied to half-hourly electricity data from 37 secondary schools. The technique monitors consumption over time, identifying any changes in patterns and quantifying their effects. It has the advantage of being both high resolution and quick to employ. The analysis produces results that allow energy professionals to rapidly detect changes in electricity consumption.     

Using a pattern recognition approach to disaggregate the total electricity consumption in a house into the major end-uses

The method presented in this paper shows a promising potential for application in residential buildings. The results prove that the whole-house electricity consumption can be disaggregated into its major end-uses, using a pattern recognition approach and only one sensor installed on the main electric entrance of the house. It also required a one-time submetering of the target appliances during the training period, of about a week, to find the electric characteristics of appliances. The results are provided in terms of daily load profiles, energy consumption and energy contribution of selected appliances. The proposed method was tested with monitored data from 3 weeks: (i) the training period of 1 week in October, (ii) the near-to-date testing period of 1 week in November and (iii) the far-to-date testing period of 1 week in January. For instance, the difference between monitored and estimated contribution is, for the month of October 1996, as follows: (i) 13 kW h or $0.85 for the DHW heater and (ii) 6 kW h or $0.36 for the refrigerator. The overall difference for both appliances does not exceed $1.25 for the month of October, for a total electricity bill of 912 kW h and $60.60, which appears to be acceptable for every homeowner. The errors in evaluating the daily energy consumption is between −10.5% and 15.9% for both the DWH heater and the refrigerator.     

A new approach to energy consumption prediction of domestic heat pump water heater based on grey system theory

This paper presented a new approach to energy consumption prediction from a domestic air source heat pump water heater (ASHPWH) based on grey system theory. An improved GM (1, 1) (i.e. a single-variable first-order grey model) applied in domestic hot water system was developed and its prediction accuracy was tested. Comparison of the measured and predicted values of heat from hot water and electricity consumed by the ASHPWH for two typical families (with 4 members and 2 members) were done. Results showed that, the prediction accuracy of the improved GM (1, 1) increased with increase in the data sample interval. The data sample interval of four weeks produced the best prediction accuracy. Based on the improved GM (1, 1) and weather data of Shanghai, the electricity cost saving, the monthly average heat and electricity consumption and the annual carbon emission reduction related to the use of the ASHPWH for the two typical families were evaluated and compared with those of the conventional electric resistance water heater.     

Cybersecurity and resilience in the swiss electricity sector: Status and policy options

Electricity systems are critical infrastructure. With increasing digitalization, they become particularly vulnerable to cyberattacks. Cybersecurity hence becomes increasingly crucial for the security of supply. Based on a detailed analysis of the status of cybersecurity in the Swiss electricity sector, we derive a set of policy recommendations on how to raise countries’ cybersecurity levels in electricity systems. The analysis builds on a national E-survey that solicited self-assessment of cybersecurity maturity levels of 124 Swiss energy market participants. It was complemented by a detailed, comparative analysis of cybersecurity measures in Switzerland and its surrounding European neighbors. On average, we found a cybersecurity maturity that needs to be ameliorated regarding information and operation technology in the Swiss electricity sector. This situation calls for improved regulatory measures and monitoring to stimulate cyber resilience among market participants.     

A study on the energy consumption unit of subway stations in Korea

As the Korean economy and society continues to develop, the number of subways are expected to increase. This increase will demand greater heating and cooling energy as well as convey a sharp rise in the overall energy consumption. This study aims to develop energy conservation and analysis for subways. This study shows the information, that is, tables, graphs, and charts concerning the trend in energy efficiency in subways between 1999 and 2001. We suggest a practical application for energy conservation methods and efficient energy use based on the investigations and analyses.     

Urban energy generation: Influence of micro-wind turbine output on electricity consumption in buildings

Small scale wind turbines installed within the built environment is classified as microgeneration technology. Such turbines may soon become a commercial reality in the UK as a result of both advancements in technology and new financial incentives provided by the government. In addition, microgeneration technologies, especially those with appreciable resource, have the potential to reduce built environment related CO₂ emissions coupled with reductions in consumers’ electricity costs. In many cases payback on capital investment is within the lifetime of the device. Micro-wind turbines installed in certain areas in the UK will fit within such criteria. The work presented here addresses modelling of such installations around the UK and presents a methodology to assess the suitability and the economic viability of micro-wind turbines for domestic dwellings. A modelling tool “μ-Wind” has been developed specifically for studying both energy yields and the payback periods for micro-wind turbines. μ-Wind predicts wind turbine performance prior to installation according to specific power curves either defined by turbine manufacturers or the user. Numerical consideration of wind speed data at specific UK sites was used to estimate energy yields and the results are projected to real electricity demand data from monitored dwellings in the UK. The results show that it is possible to predict with a good degree of accuracy the expected financial payback period for a typical domestic dwelling. Furthermore, the paper postulates that micro-wind technology could have the potential to make a significant impact upon domestic electricity generation when located at the windiest sites in the UK. The likelihood of a proliferation of these turbines in the urban or suburban environment is low but at coastal or inland high elevation sites the technology appears to have a promising future.     

Does market concentration affect wholesale electricity prices? An analysis of the Indian electricity sector in the COVID-19 pandemic context

This study explores whether the market concentration affects the wholesale prices of electricity generation among Indian companies by drawing 15-min frequency data related to electricity production, wholesale prices, and electricity demand in August 2020 during the COVID-19 pandemic. The ARDL model estimates show a positive relationship between market concentration and wholesale energy prices. The study recommends the adoption of policies to reduce market concentration and enhance clean energy development.     

A simulation of the energy consumption monitoring in Mediterranean hotels: Application in Greece

Due to the competitiveness the importance of reducing cost and the growing sensitivity to environmental factors in the hotel design, is leading to the introduction of environmental friendly elements; if will be added the considerable increase in the cost of fossil fuel, it is obvious that all these factors create conditions favorable to the optimization of energy resources.     

临建生活区安全用电及节约用电措施

简单介绍了临建生活区用电量估算方法,针对目前临建生活区建设过程中存在的各种安全隐患及电气安全问题进行了分析,并着重阐述了相应的解决措施和节约用电措施,以期指导实践,保证施工人员生命和财产安全。     

Load prediction method for heat and electricity demand in buildings for the purpose of planning for mixed energy distribution systems

Energy planning for mixed energy distribution systems is important to increase the flexibility in the regional and national energy systems. Expected maximum loads, load profiles and yearly energy demands, all divided into heat and electricity purposes, are important input parameters to plan for the most economical, technical and environmental optimal energy distribution system for a planning area. First, this article presents a load prediction method which estimates heat and electricity load profiles for various building categories. The method is based on statistical analyses of hourly simultaneous measured district heat and electricity consumption in several buildings, as well as background information of the measured buildings. The heat load model is based on regression analyses, whereas the electricity load model is based on various statistical distributions. Second, a method for load aggregation based on the building categories’ load profiles is presented to estimate the maximum load demands, yearly load profiles, load duration profiles and yearly energy demands, all divided into heat and electricity purposes, for a planning area.     

Principal component analysis of electricity use in office buildings

Principal component analysis was conducted on five major climatic variables—dry-bulb temperature, wet-bulb temperature, global solar radiation, clearness index and wind speed. Twenty-eight year (1996–2000) long-term measured weather data were considered. A two-component solution was obtained, which could explain 80% of the variance in the original weather data. Monthly electricity consumption data recorded during a 5-year period (1979–2006) were gathered from 20 fully air-conditioned office buildings with centralised HVAC systems in subtropical Hong Kong. Electricity use per unit gross floor area ranged from 163 to 389 kWh/m². These consumption data were correlated with the corresponding principal components using linear multiple regression techniques. The coefficient of determination (R²) varied from 0.76 to 0.95 indicating reasonably strong correlation. It was found that the regression models developed could give a reasonably good indication (mostly within 3%) of the annual electricity use, but the monthly estimates might differ from the actual consumption by up to 9%. Attempt was also made to develop a general regression model for the 20 buildings, which had an R² of 0.84 with a maximum mean-biased error of 18.6% and a maximum root-mean-square error of 21.4%.     

The impact of the COVID-19 pandemic and behavioral restrictions on electricity consumption and the daily demand curve in Turkey

The rapid spread of COVID-19 has severely impacted many sectors, including the electricity sector. The reliability of the electricity sector is critical to the economy, health, and welfare of society; therefore, supply and demand need to be balanced in real-time, and the impact of unexpected factors should be analyzed. During the pandemic, behavioral restrictions such as lockdowns, closure of factories, schools, and shopping malls, and changing habits, such as shifted work and leisure hours at home, significantly affected the demand structure. In this research, the restrictions and their corresponding timing are classified and mapped with the Turkish electricity demand data to analyze the estimated impact of the restrictions on total demand and daily demand profile. A modulated Fourier Series Expansion evaluates deviations from normal conditions in the aggregate demand and the daily consumption profile. The aggregate demand shows a significant decrease in the early phase of the pandemic, during the period March–June 2020. The shape of the daily demand curve is analyzed to estimate how much demand shifted from daytime to night-time. A population-based restriction index is proposed to analyze the relationship between the strength and coverage of the restrictions and the total demand. The persistency of the changes in the daily demand curve in the post-contingency period is analyzed. These findings imply that new scheduling approaches for daily and weekly loads are required to avoid supply-demand mismatches in the future. The long-term policy implications for the energy transition and lessons learned from the COVID-19 pandemic experience are also presented.