Electricity Use Patterns Research Articles

Wintertime patterns of residential electricity use and indoor temperature in the composite climate of India

Wintertime electricity use in India has received little attention due to the growth of residential air conditioning (AC) in the summer and monsoon seasons. This paper combines monitoring (time-series) and survey (contextual) data to examine the wintertime pattern of daily electricity current, indoor temperature, and relative humidity profiles across a sample of 64 dwellings in the city of Hyderabad representing the composite climate of India. Monitoring data were recorded for 31 days (December 2021 with a mean daily outdoor temperature of 25°C) using a CT clamp meter and Bluetooth enabled sensors. Contextual data covered dwelling and household characteristics, and use of appliances. The winter peak period of electricity use was found to occur from 6:30-10:30 in the morning, likely to be for hot water (geysers) and cooking. This is different to the late evening/night-time peak observed in the summer due to use of air conditioning (AC). Only one dwelling showed potential signs of using space heating. Higher electricity use aligned with lower peak temperatures in some homes particularly in the high-income group, indicating the potential use of AC during winter. The wintertime morning electricity peak in India could be managed using thermal stores in geysers through pre-heating which could also offer demand response opportunities.

Identification of Energy Saving Potential Through Energy Audit at PT. ABC

Increasing energy efficiency is one of the most important functions for reducing energy costs as well as production costs. One way to reduce the use of electrical energy consumption is by saving energy. This study aims to determine how much energy savings at PT ABC through an energy audit. An energy audit is carried out with two main activities, namely identifying the profile of patterns of electricity use and analysis of power quality. The results of the analysis are then expected to be the basis for knowing the potential for energy savings and energy conservation in the industry. The method used in this study begins with field observations, measurements of electrical systems, and measurements of production machines and production support equipment. Based on some data, both primary data and secondary data, an analysis is carried out through calculations to determine potential energy-saving opportunities. The main energy source used at PT. ABC is from PLN with 1 subscription to a 3-phase system with an installed power of 197 kVA. Energy use or consumption for a year is 804,279.97 kWh. Opportunities for energy savings through energy audits that have been carried out are expected to have an impact on the company, namely energy cost savings of IDR 137,344,162 per year

A comparative analysis of patterns of electricity use and flexibility potential of domestic and non-domestic building archetypes through data mining techniques

The large-scale deployment of smart meters has led to significant amount of electricity demand data available, driving it into the realm of Big Data. It is a major challenge to exploit this Big Data in order to characterise electricity use patterns and to support demand response policies. In this paper, we perform a featured-based cluster analysis on nine building archetypes (hospitals, schools, offices, hotels, flats, houses etc.) to identify electricity use patterns. Then, four metrics are developed, which are entropy, load curviness, peak intensity and index of hourly ramp rates, to measure these archetypes’ suitability to be involved in demand response schemes. A significant difference in electricity use patterns between the archetypes is found, as well as among the seasons and days of the week. We present a number of metrics for each archetype to establish which type of archetype should be prioritised for demand response programmes in terms of peak management, ramp rates as well as demand flexibility. A key finding of our study is that households offer more demand flexibility than the non-domestic sector and should therefore be incentivized to participate in dynamic electricity tariffs.

Reported Behavioural Patterns of Electricity Use among Low-Income Households in Makhanda, South Africa

Households consume up to 20% of overall electricity consumption globally; hence, they are important role players in efforts towards promoting sustainable consumption. Research on electricity use behaviour is important for informing intervention strategies; however, relative to developed countries, research on this subject is lacking in developing countries where electricity access is limited. In South Africa, electricity use behaviour among poor neighbourhoods remains little studied and understood. This study was carried out among low-income households in Makhanda, South Africa, characterised by high poverty and unemployment rates, low education levels, and limited access to basic services. Using a self-reporting approach, electricity use behaviour of low-income households was assessed against a list of common household electricity use actions. A survey of 297 households was conducted. The findings show mixed results, with households reporting both good electricity use behaviour (e.g., cooling down hot food before refrigeration and using washing machines on full load) and wasteful actions (e.g., leaving appliances on standby). Our results show that electricity use behaviour was influenced by socio-psychological values including universalism, benevolence, hedonism, and power. Some of the reported electricity behavioural patterns are consistent with those previously reported among high-income households. The theoretical and practical implications of these results are discussed.

Real-time feedback on electricity consumption: evidence from a field experiment in Italy

Smart meters can help citizens in optimizing energy consumption patterns. However, mixed evidence exists on their effectiveness in reducing energy demand and especially in levelling off the daily peaks of electricity load curves. Here, we evaluate the impact of providing real-time feedback on electricity consumption from a field trial in Italy. We combine standard regressions with machine learning techniques on high-frequency data to quantify impacts on both levels and patterns of electricity use. Results indicate that real-time feedback can moderately decrease electricity consumption (between 0.5 and 1.9% depending on model specification), but that it does not promote load shifting throughout the day by itself. Machine learning reveals evidence of significant household heterogeneity in the behavioral response.

Electrical Customer Profile Using Fuzzy Logic Theory

Considering the increasing electrical energy demand in residences, it is necessary to know the detailed pattern of electricity use, to change the behavior of the final consumer and to reduce the global consumption. Knowing the load curve profile in advance is very important for detecting the peaks and valleys to change habits of energy consumption, principally during periods when the prices are lower, i.e., following the electrical power industry opportunities. This work studies how the consumer behaves individually, as well as identifies similarities with other consumers. Thus, we herein propose a procedure for obtaining the residential load profile using fuzzy-logic theory. As the residential electrical energy consumption is highly correlated to the active occupation, the quantity of dwellers and the different periods of the day for typical consumers are considered. However, the ideas presented in this work can be applied to other regions in different countries. To verify the efficiency of the proposed system, the obtained results are compared with real load curves.

Environmental assessment of future electricity mix – Linking an hourly economic model with LCA

Life Cycle Assessment (LCA) is an increasingly widespread method for the environmental accounting of products and services. Since almost all production processes use grid electricity, the environmental impact of power generation plays a key role in LCA. There is high potential both on a local and a regional scale for improving electricity generation to achieve decarbonisation goals in the near future. The environmental impact of electricity supply from the grid varies both in the short (intra-annual) and in the long term (over several years). This variation is usually highly simplified in current LCA practice, to yearly average or a specific year’s impact. In this paper, a method is presented for linking a detailed economic model and life cycle assessment to evaluate both intra-annual and long-term variation in the environmental impact of grid electricity. The model is applied for the case study of Hungary for three future scenarios. The “Decarbon” and “Delayed” scenarios include an emission reduction target of 94% for 2050 compared to 1990 for the EU with a less intensive support of renewables until 2035 in the “Delayed” scenario. The “No target” scenario sets no long-term goal for carbon-dioxide emission reduction. Our results show that in the next 30 years, 87% reduction is expected in the Global Warming Potential compared to 2018 in the Hungarian electricity mix if the decarbonisation of the grid is fulfilled. However, without support for renewable energy, only 30% reduction is foreseen. While the effect of intra-annual variation is relatively low in the current fossil-based electricity market and in the “No target” scenario, its significance is expected to increase in the future with a change in the coefficient of variation to 77% from 10% by 2050. The results indicate that dynamic modelling of electricity taking into account variation due to cross-border trading and renewable penetration will influence the LCA results for products depending on their lifetime and pattern of electricity use.

Rising with the sun? Encouraging solar electricity self-consumption among apartment owners in Sweden

Studies suggest that householders who turn prosumers become more energy aware, change their pattern of electricity use and may even start to engage in other pro-environmental activities. However, few of these studies were equipped to investigate such causal relationships—mainly because most prosumers are inherently self-selected. However, as real estate companies and building owners have begun installing photovoltaics on their customers’ behalf, a new breed of non-self-selected prosumers is emerging, which presents new opportunities to address questions of causality and thus improve our understanding of the possible implications of a more prosumer-dense future. Using a sample of 54 apartment households with a shared rooftop PV installation, this article presents the first causal analysis on non-self-selected prosumers’ response to information about having become prosumers, what that means for themselves and the collective they are part of, how to self-consume solar electricity and why they should do so. Using a stepped wedge design and stratified randomization procedure we were able to design an experimental study with sufficient power. A panel regression model and various statistical analyses on pre and post treatment survey- and electricity use data were used to evaluate the intervention. In line with studies of self-selected prosumers, the self-reported measures suggest that householders have shifted the use of major appliances to increase their self-consumption. However, based on the electricity use data, we find no evidence of a such a shift and no indications of spillovers to other pro-environmental behaviours—highlighting the need to use multiple measures to assess behavioural change.

Analysis of Building Electricity Use Pattern Using K-Means Clustering Algorithm by Determination of Better Initial Centroids and Number of Clusters

Energy demands in the building sector account for more than 30% of the total energy use and more than 55% of the global electricity demand. Efforts to develop sustainable buildings are progressing but are still not keeping pace with the growing building sector and the rising demand for energy. Analyzing the energy use pattern of buildings and planning for energy conservation in existing buildings are essential. In this research, we propose a method to analyze the energy use pattern in a building using the K-means clustering method. Initial centroids in K-means clustering are chosen randomly so that the clustering result changes every time. This instability is removed in the proposed method by the selection of initial centroids using a percentile method based on empirical cumulative distribution. The results from the proposed method have better accuracy, and the internal cohesion and separation between clusters are better than the random initialization method. Analyzing yearly electricity use using the proposed clustering method, the daily pattern of electricity use can be categorized according to the operation of buildings. For this purpose, in this research, electricity use pattern was analyzed for three to six clusters. In comparison with the university schedule, six clusters were found to be appropriate and the accuracy was 89.3%. Once daily electricity use are categorized, base electricity consumption, electricity consumption by human activities, and electricity consumption by air-conditioning can be determined. As energy consumption by usage is clarified, measures for energy consumption in university buildings can be proposed.

Convenience before coins: Household responses to dual dynamic price signals and energy feedback in Sweden

To enable and cope with an increase of intermittent electricity generation, the power industry and regulators are trying to incentivise users to use electricity at certain times through dynamic pricing and feedback. The effects of such interventions on patterns of electricity use have been extensively studied, yet little is known about how and why householders do or do not respond to such interventions. Using interviews and activity-based diaries, this study provides a qualitative exploration of how and why householders who are subject to a demand-based time-of-use distribution tariff, real-time retail pricing and real-time feedback, do or do not respond to such interventions in their daily lives. We find that the householders have adapted a range of existing practices and have started to engage in new ones that aim to reduce peak demand during peak hours, partly without the support of feedback. Drawing on theories of practice, we challenge common preconceptions about how and why price signals work by demonstrating how the size of the financial incentive that a price signal provides does not have that much influence on householders’ willingness to engage in demand response. We argue that price signals work by providing new meanings to practices that use electricity, that feedback can mediate these meanings, and that what matters for householders’ willingness to engage in demand response is that the changes they undertake do not cause them any inconvenience by limiting the temporal flexibility of other doings in their daily lives.

The pattern of electricity use in residential sector: The experiences from 133 economies

While growing electricity consumption in residential sectors worldwide will draw more attention in economic and policy related decisions, the fast-growing developing world still requires enormous amounts of electric power to achieve better living conditions. This dilemma can hardly be addressed without a thorough understanding of the relationship between economic development and residential electricity demand. Based on a country-level panel data set covering 133 countries or regions over four decades, this paper aims to investigate how the historical relationship between economic development and residential electricity use evolved. Different to previous studies, we are not assuming the curve shape of the electricity use and economy in advance. A flexibly piecewise spline model is employed rather than the commonly used linear, quadratic, cubic polynomial or S shape saturation curve model. We capture an inverted-U relationship, and the turning point is at about $20,000 (purchasing power parity, 2005 constant price). This pattern is robust in the sense that it is not sensitive to eliminating any special countries or to alternative model specifications. These results are important for understanding the future trend of electricity consumption in the less developed world.

Assessing the impact of solar PV on domestic electricity consumption: Exploring the prospect of rebound effects

This study examines patterns of electricity use by households in Sydney who have installed solar photovoltaic (PV) technology compared to those who have not in order to assess the impact of government solar incentive schemes, and to identify whether conservation or rebound (increased consumption) effects are associated with rooftop PV. Findings have significance in determining whether a rebound effect needs to be factored into projected energy/carbon savings from solar PV installation. At issue is the robustness of carbon mitigation estimates included in future rounds of international climate change agreements as well as local forecasts of future electricity demand affecting the national grid. Analysis and modelling was undertaken on billing data for the period 2007–2014 on a representative sample of 4819 households. The sample comprised three groups: households who were early adopters and installed PV under a 60c/kWh gross feed-in tariff scheme, a group who installed under a 20c/KWh gross feed-in scheme and a control group with no PV. Econometric modelling undertaken on energy consumption behaviour of households with versus without local renewable energy generation revealed that on a kWh basis, the rebound effect is estimated to erode up to one fifth of the carbon benefit of renewable energy generated by solar PV.

A clustering approach to domestic electricity load profile characterisation using smart metering data

The availability of increasing amounts of data to electricity utilities through the implementation of domestic smart metering campaigns has meant that traditional ways of analysing meter reading information such as descriptive statistics has become increasingly difficult. Key characteristic information to the data is often lost, particularly when averaging or aggregation processes are applied. Therefore, other methods of analysing data need to be used so that this information is not lost. One such method which lends itself to analysing large amounts of information is data mining. This allows for the data to be segmented before such aggregation processes are applied. Moreover, segmentation allows for dimension reduction thus enabling easier manipulation of the data.Clustering methods have been used in the electricity industry for some time. However, their use at a domestic level has been somewhat limited to date. This paper investigates three of the most widely used unsupervised clustering methods: k-means, k-medoid and Self Organising Maps (SOM). The best performing technique is then evaluated in order to segment individual households into clusters based on their pattern of electricity use across the day. The process is repeated for each day over a six month period in order to characterise the diurnal, intra-daily and seasonal variations of domestic electricity demand. Based on these results a series of Profile Classes (PC’s) are presented that represent common patterns of electricity use within the home. Finally, each PC is linked to household characteristics by applying a multi-nominal logistic regression to the data. As a result, households and the manner with which they use electricity in the home can be characterised based on individual customer attributes.

General Nexus Between Water and Electricity Use and Its Implication for Urban Agricultural Sustainability: A Case Study of Shenzhen, South China

Although water and energy resources are well-recognized concerns regarding economic and social development sustainability, little specific research has focused on both water and energy problems at the same time. This study analyzed the water and electricity-use patterns in Shenzhen, South China during 2001-2009. A curve regression method was used to examine the relationship between water and electricity use per gross domestic product (GDP) in Shenzhen and its three sectors, i.e., agriculture, industry & construction, and residential life & services. Results showed that agriculture only covered less than 10% of water and electricity use in Shenzhen, while industry & construction and residential life & services accounted for more than 90% of water and electricity use in Shenzhen, which coincided with the city's industrial structure. The water and electricity use per GDP in agriculture was the biggest among three sectors in Shenzhen during 2001-2009, which means inefficiency of water and electricity use in agriculture. Due to transitioning to advanced materials and manufacturing, both water and electricity use per GDP in industry & construction decreased during 2001-2009 and their utilization efficiencies gradually increased over time. The same held true for those in residential life & services transformed toward modern business, creative culture, finance services, etc. Derived from the survival of the fittest in competing for limited water and electricity resources, agriculture in Shenzhen has been gradually substituted by industry & construction and residential life & services, with much higher efficiencies of water and electricity use. And traditional agriculture will not be sustainable in the process of urbanization and industrialization, except high-tech intensive agriculture with low water and energy cost. Furthermore, by means of curve regression, we found that there was a significant quadratic relationship between water use per GDP and electricity use per GDP in the entire city and its three sectors. Suitable industrial transformation and advancement was a very effective way to save water and energy for modern cities. This can provide some reference for systematic planning and design of water and electricity allocation and use in agriculture, industry & construction and residential life & services in a city.

Domestic electricity use: A high-resolution energy demand model

The pattern of electricity use in an individual domestic dwelling is highly dependent upon the activities of the occupants and their associated use of electrical appliances. This paper presents a high-resolution model of domestic electricity use that is based upon a combination of patterns of active occupancy (i.e. when people are at home and awake), and daily activity profiles that characterise how people spend their time performing certain activities. One-min resolution synthetic electricity demand data is created through the simulation of appliance use; the model covers all major appliances commonly found in the domestic environment. In order to validate the model, electricity demand was recorded over the period of a year within 22 dwellings in the East Midlands, UK. A thorough quantitative comparison is made between the synthetic and measured data sets, showing them to have similar statistical characteristics. A freely downloadable example of the model is made available and may be configured to the particular requirements of users or incorporated into other models.

What is the relationship between built form and energy use in dwellings?

Energy is used in dwellings to provide four services: space heating, hot water, lighting and to power appliances. This paper describes how the usage of energy in a UK home results from a complex interaction between built form, location, energy-using equipment, occupants and the affordability of fuel. Current models with standard occupancy predict that energy use will be strongly related to size and built form, but surveys of real homes show only weak correlations, across all types of dwelling. Recent research has given us insights into occupancy factors including preferred comfort, ‘take-back’ from thermal efficiency improvements, and patterns of electricity use. Space heating is on a downward trend and is low in new dwellings. Energy use for lights and appliances, which is only weakly related to built form, is increasing. Strong legislation, combined with low-carbon technologies, will be needed to counteract this trend. Future challenges discussed include increases in real energy prices and climate change mitigation efforts, which are likely to improve the existing stock. Challenging targets are now in place for new housing to move towards low or zero energy and carbon standards. In the longer term, dwellings will demand less energy. Alternatives to gas for space heating will be increasingly common, including ground source heat and local combined heat and power (CHP) from biomass, while electricity could come from a more decarbonised electricity system. However, these improvements must be set alongside a demand for many new homes, demographic trends towards smaller households, and a more holistic approach to overall carbon use including personal transport.

Ice slurry based thermal storage in multifunctional buildings

Ice slurry based thermal storage plays an important role in reshaping patterns of electricity use for space cooling and heating. It offers inherent advantages in energy efficiency, operating savings, load follow-up and flexible installation over conventional thermal storage technologies. This paper provides discussions on the generation mechanism and performance of ice slurry, as well as the operation principle of the ice slurry based thermal storage system. Details of the system design, control strategy and operation performance are given through a case study on a recent installation in Herbis Osaka, the largest simple building complex in Japan. An evaluation of different installations with ice slurry thermal storage reveals that it is a rewarding technology that provides significant operating savings for the building air-conditioning and improves energy utilization efficiency in modern society.

Improving appliance efficiency in Indonesia

We report on findings from a study of household electricity consumption and the potential impact on future electricity demand of improving appliance efficiency in urban Java, Indonesia. We describe the current pattern of household electricity use in urban Java, based on results from a recent survey of 2 700 households, and the market for and characteristics of appliances currently sold in Indonesia. We estimate the impact on future electricity demand of two scenarios of penetration of higher efficiency. Finally, we discuss the barriers to improvement of appliance efficiency, and describe various policy approaches that the government could take in order to accelerate the adoption of higher-efficiency appliances. While in this article we focus our attention on Indonesia, many of the conclusions would apply to other developing countries as well.

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.

Integrating Demand-Side Management into Utility Planning

Electric demand-side management is the concept of altering the pattern of the customer's electricity use "behind-the-meter." The scope of demand-side management is the planning, development and implementation of programs whose objective is to actively shape the daily and seasonal electric load profiles of customers to result in better overall system utilization, lower overall costs and maintained financial stability. This paper describes the integration of demand-side management into the utility planning process and is based on a panel held at the 1985 Winter Power Meeting.