Data-based method for creating electricity use load profiles using large amount of customer-specific hourly measured electricity use data

The recent technological developments monitoring the electricity use of small customers provides with a whole new view to develop electricity distribution systems, customer-specific services and to increase energy efficiency. The analysis of customer load profile and load estimation is an important and popular area of electricity distribution technology and management. In this paper, we present an efficient methodology, based on self-organizing maps (SOM) and clustering methods (K-means and hierarchical clustering), capable of handling large amounts of time-series data in the context of electricity load management research. The proposed methodology was applied on a dataset consisting of hourly measured electricity use data, for 3989 small customers located in Northern-Savo, Finland. Information for the hourly electricity use, for a large numbers of small customers, has been made available only recently. Therefore, this paper presents the first results of making use of these data. The individual customers were classified into user groups based on their electricity use profile. On this basis, new, data-based load curves were calculated for each of these user groups. The new user groups as well as the new-estimated load curves were compared with the existing ones, which were calculated by the electricity company, on the basis of a customer classification scheme and their annual demand for electricity. The index of agreement statistics were used to quantify the agreement between the estimated and observed electricity use. The results indicate that there is a clear improvement when using data-based estimations, while the new-estimated load curves can be utilized directly by existing electricity power systems for more accurate load estimates.     

Value-based system facility planning - a rational response to conflicting customer and regulatory demands

In a competitive energy market in which power supply reliability influence customer's purchasing decisions, utilities throughout the world are rapidly recognizing that they cannot ignore customer preferences. Reliability of electric service should be based on balancing the costs to a utility and the value of the benefits received by its customers. The customer survey approach is based on the assumption that customers are in the best position to understand how interruptions impact their activities that depend on electricity supply. A challenge to electric utilities is to increase the market value of the services they provide with the right amount of reliability and to lower its costs of operation, maintenance, and construction to provide lower rates for customers. Lastly, the value-based reliability cost-benefit assessment helps electric utilities to achieve the goal of providing reliable energy service at the lowest possible cost, which their customer values.     

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.     

Semi-automatic construction of a domain ontology for wind energy using Wikipedia articles

Domain ontologies are important information sources for knowledge-based systems. Yet, building domain ontologies from scratch is known to be a very labor-intensive process. In this study, we present our semi-automatic approach to building an ontology for the domain of wind energy which is an important type of renewable energy with a growing share in electricity generation all over the world. Related Wikipedia articles are first processed in an automated manner to determine the basic concepts of the domain together with their properties and next the concepts, properties, and relationships are organized to arrive at the ultimate ontology. We also provide pointers to other engineering ontologies which could be utilized together with the proposed wind energy ontology in addition to its prospective application areas. The current study is significant as, to the best of our knowledge, it proposes the first considerably wide-coverage ontology for the wind energy domain and the ontology is built through a semi-automatic process which makes use of the related Web resources, thereby reducing the overall cost of the ontology building process.     

The effect of sociodemographic diversity of residential customers on the financial risk experienced in the retail electricity market

The sociodemographic diversity of residential customers can affect the level of financial risk that an electricity provider experiences in the retail market. To demonstrate the relationship between sociodemographic diversity and financial risk, electricity consumption data drawn from the United Kingdom Power Networks ‘Low Carbon London’ project was analyzed to explore the relationship between sociodemographic diversity and financial risk experienced by electricity retailers. The results show that when increasing the sociodemographic diversity amongst a group of residential customers the effect on financial risk depends on the electricity consumption patterns of individual customers and the relationship of consumption patterns between residential customers. Increasing sociodemographic diversity amongst residential customers with very distinct energy consumption patterns can decrease the overall financial risk associated with the aggregated revenue received from these customers. However, the results showed that adding customers to a customer base without consideration for their sociodemographic background can cause the overall financial risk associated with the aggregated revenue received to change erratically. Whilst previous studies have considered customer diversity and its influence on peak electricity demand, this research advances the state-of-the-art by showing the importance of customer diversity to the financial quantity risk experienced by electricity retailers. This finding has serious implications for electricity providers seeking to mitigate financial risk in the retail electricity market.     

The impact of retail rate structures on the economics of commercial photovoltaic systems in California

This article examines the impact of retail electricity rate design on the economic value of grid-connected photovoltaic (PV) systems, focusing on commercial customers in California. Using 15-min interval building load and PV production data from a sample of 24 actual commercial PV installations, we compare the value of the bill savings across 20 commercial-customer retail electricity rates currently offered in the state. Across all combinations of customers and rates, we find that the annual bill savings from PV, per kWh generated, ranges from $0.05 to $0.24/kWh. This sizable range in rate-reduction value reflects differences in rate structures, revenue requirements, the size of the PV system relative to building load, and customer load shape. The most significant rate design issue for the value of commercial PV is found to be the percentage of total utility bills recovered through demand charges, though a variety of other factors are also found to be of importance. The value of net metering is found to be substantial, but only when energy from commercial PV systems represents a sizable portion of annual customer load. Though the analysis presented here is specific to California, our general results demonstrate the fundamental importance of retail rate design for the customer-economics of grid-connected, customer-sited PV.     

The prerequisite for competition in the restructured wholesale Saudi electricity market

Protection of customers against monopoly is the first and main objective of the Saudi Electricity and Co-generation Regulatory Authority (ECRA). The second important objective, as recommended by the present study, is regulating natural monopoly businesses [Saudi electricity national grid (SENG) and Saudi electricity distribution (SED)] in addition to promoting real competition in competitive businesses [power supply providers (PSPs) and customer service providers (CSPs)]. Another four main objectives of ECRA are to promote the efficient use of energy and natural resources, to ensure a reasonable rate of return for PSPs and CSPs and at the same time to be fair to end-users, to ensure reasonable charges to SENG and SED services to be adequate for them to run the organization in a break-even manner and to maintain the system's security and reliability. The present paper discusses the way to improve and restructure the Saudi electricity market.     

一种基于大数据的低压单相用户窃电分析方法

低压单相用户具有分布广、基数大的特点,随着人们用电量日益增大,低压单用户窃电现象越来越严重.目前窃电排查主要以人工逐户核查的方式为主,存在缺乏针对性、工作量大和效率低下等问题.文章提出了一种新型窃电分析方法,通过对用户电压、零火电流、电能表上报事件、台区等多维信息进行大数据分析,可有效、精准定位窃电用户,明确窃电手段....     

Matching policy and people? Household responses to the promotion of renewable electricity

In this paper, we study the responses among households to the promotion of renewable electricity. We analyse an experiment conducted by a Norwegian power company that offered Guarantees of Origin of supply to 5,000 of their customers. In the experiment, five different groups of 1,000 customers each received information about a renewable electricity certificate and how to purchase it. The information and the reasons given for why the customers should accept the offer was framed differently to each of the groups. The experiment produced minimal responses, and we use material from focus group discussions and in-depth interviews for interpreting and explaining the results. The analysis shows that customers tend to disregard information coming from their supplier, while there is also a low degree of commensurability between the message presented in the information and the understandings and perceptions held by the customers. For example, whereas the information contained the argument that customers must purchase certificates to obtain renewable electricity, Norwegians, because of their awareness of the country’s hydro-based production system, perceive electricity to be renewable as it is. Additionally, focus group participants found the presented terms and figures to be incomprehensible to the extent that the information can be said to have produced ignorance in them. In turn, this negatively affected people’s trust in the message and also its sender, as relevance and reliability are disclosure’s main challenges in Norway. We use the case of electricity labelling in Norway to demonstrate some of the general challenges associated with using information as a tool for changing people’s consumption patterns in deregulated energy markets.     

Economic optimization and sensitivity analysis of photovoltaic system in residential buildings

This paper deals with the problem of optimal size of grid-connected photovoltaic (PV) system for residential application. It is assumed the PV system can input or output liberally the electricity to the utility electricity grid. A simple linear programming model is developed. The objective is to minimize the annual energy cost of a given customer, including PV investment cost, maintenance cost, utility electricity cost, subtracting the revenue from selling the excess electricity. The model reports the optimal PV capacity that customers adopt with their electricity requirements. Using this model, an investigation is conducted of economically optimal PV investment under several conditions for a typical residential building. Additionally, the sensitivity of levelized cost and simple payback period to various economic and technical circumstances has been analyzed.     

A prototype tool for automatically generating energy-saving advice based on smart meter data

As many countries and regions have started large-scale deployment of smart meters, there is a growing amount of data on electricity use available for energy efficiency services. We have developed a novel tool that, based on smart meter data, automatically generates customised energy-saving advice to commercial and industrial customers. This type of audit tool could enormously expand the target of energy audits to almost all small- and medium-sized enterprises (SMEs) with smart metering at a low cost per customer. In this paper, we explain the structure of and approaches that we used in our prototype tool, such as fault detection, energy disaggregation, social comparison and benchmarking and selective visualisation. We also show test case results for the tool by using smart meter data from 34 public buildings in Japan. While the prototype tool presented in this paper has some limitations, the approach and the basic structure of the tool are valuable and provide the basis for more sophisticated tools.     

A comparison of the accuracy of building energy analysis in Bahrain using data from different weather periods

Weather data are important in building design and energy analysis. In Bahrain, the weather data currently used are based on far past climatic information. Climate variability during the last few decades has raised concern over the ability of these data to provide accurate results when analysing the energy performance of buildings. This study discusses issues related to climate variability and evaluates its impact on the performance of weather data used in building simulation. An evaluation was performed using two methods: firstly, a comparison of measured climatic elements and secondly, a comparison of the thermal performance of two statistically based weather data files. With respect to their impact on typical Bahraini building thermal systems, the comparison was carried out between simulation results and the actual energy consumption of two case studies. This paper shows a 14.5% difference between simulation results based on far past data and present electricity consumption and concludes that the prediction of present and future performance based on recent updated data gives better results.     

Automated prediction system of household energy consumption in cities using web crawler and optimized artificial intelligence

The supply of electrical energy is critical to convenient and comfortable living. However, people consume a large amount of energy, contributing to an energy crisis and global warming, and damaging some ecological cycles. Residential electricity consumption has greater elasticity than industrial and business consumption; it therefore has high energy-saving potential. This work establishes an automated platform, which provides information about residential electricity consumption in each city in Taiwan. Machine learning was used to forecast future residential electricity demand. A nature-inspired optimization method was applied to enhance the accuracy of the best machine learner, yielding an even better hybrid ensemble model. Performance measures indicate that the resulting model is accurate and provides effective information for reference. An automatic web-based system based on the model was combined with a web crawler and scheduled to run automatically to provide information on monthly residential electricity consumption in each county and city. By providing energy consumption information across the country, power providers and government can discuss policy and set different goals for energy use. The results of this study can facilitate the early implementation of energy-saving and carbon emission-reducing in cities and aid utility companies in establishing energy conservation guidelines.     

Electricity tariff design for transition economies: Application to the Libyan power system

This paper presents a general electricity tariff design methodology, especially applicable for transition economies. These countries are trying to modernize their power systems from a centralized environment (with normally, a public vertically integrated electric company) to a liberalized framework (unbundling electricity companies and, eventually, starting a privatization process). Two issues arise as crucial to achieving a successful transition: i) ensuring cost recovery for all future unbundled activities (generation, transmission, distribution and retailing), and ii) sending the right price signals to electricity customers, avoiding cross-subsidies between customer categories. The design of electricity tariffs plays a pivotal role in achieving both objectives. This paper proposes a new tariff design methodology that, complying with these two aforementioned criteria, requires a low amount of information regarding system data and customer load profiles. This is important since, typically, volume and quality of data are poor in those countries. The presented methodology is applied to computing tariffs for the Libyan power system in 2006, using real data.     

An exploratory analysis of California residential customer response to critical peak pricing of electricity

This paper summarizes the results from an exploratory analysis of residential customer response to a critical peak pricing (CPP) experiment in California, in which 15 times per year participating customers received high price signals dispatched by a local electricity distribution company. The high prices were about three times the on-peak price for the otherwise applicable time-of-use rate. Using hourly load data collected during the 15-month experiment, we find statistically significant load reduction for participants both with and without automated end-use control technologies. During 5-h critical peak periods, participants without control technology used up to 13% less energy than they did during normal peak periods. Participants equipped with programmable communicating thermostats used 25% and 41% less for 5 and 2 h critical events, respectively. Thus, this paper offers convincing evidence that the residential sector can provide substantial contributions to retail demand response, which is considered a potential tool for mitigating market power, stabilizing wholesale market prices, managing system reliability, and maintaining system resource adequacy.     

Design and performance of energy-efficient solar residential house in Andorra

This paper describes the design and building technology details of a three-storey single family house located in the Pyrenees, in Andorra. The house is owned by the first author, and has been occupied since 2004. A combination of active and passive solar energy systems and night-time electricity are used to supply the heating and cooling demands. The main goal of this paper is to provide detailed design information and an evaluation of performance. Data provided includes site information and climate, basic design options and decisions, energy saving strategies and energy end use data. The house has been in use since 2003. This allows adequate performance data to be presented and evaluated. Some general results and initial design problems are discussed.     

The residential demand for electricity by time-of-use: A survey of twelve experiments with peak load pricing

Information on customer response to time-of-use (TOU) rates plays a major part in utility resource planning, particularly in the design of cost-based rate structures and cost-effective load management programs. Several elasticity concepts have been used by economists to analyze customer response to TOU rates. We discuss the interrelationships between various concepts and show that total uncompensated price elasticities are the appropriate measure of customer response.Evidence from twelve pricing experiments involving about 7000 customers indicates that residential peak-period electricity consumption is generally price-sensitive. TOU rates generally reduce peak period electricity use, as well as daily use. Response is generally higher for high use customers.Short-run own-price elasticities of peak consumption range from nil to ?0.45. Off-peak elasticities lie in a similar range, but are less statistically significant. This wide range indicates that elasticities are not fixed constants but vary parametrically with several conditioning variables such as level of total (daily) electricity use, composition of appliance portfolio and duration of pricing periods. If proper allowance is made for these interactions, it may be possible to transfer elasticities between utility service areas, thus obviating the expensive and time-consuming need for every utility to conduct its own experiment.     

A procedure to group residential air-conditioning load profiles during the hottest days in summer

A methodology is proposed whereby residential customers can be grouped by their diurnal air-conditioner use profiles during the hottest days in summer. The procedure, illustrated with data from a load experiment study, involves three phases. The first is to form a subset of electricity use data corresponding solely to days when utilities are likely to face summer peaking problems. The second phase is to identify a characteristic diurnal A/C load profile for each customer which adequately represents his day-to-day behavior during these peak days. The final phase is to cluster these diurnal A/C load profiles into physically consistent discrete groups. The eventual practical applications of this methodology are that it could assist utilities in better planning and implementing cost-effective peak shaving strategies.     

Can envelope codes reduce electricity and CO2 emissions in different types of buildings in the hot climate of Bahrain?

The depletion of non-renewable resources and the environmental impact of energy consumption, particularly energy use in buildings, have awakened considerable interest in energy efficiency. Building energy codes have recently become effective techniques to achieve efficiency targets. The Electricity and Water Authority in Bahrain has set a target of 40% reduction of building electricity consumption and CO₂ emissions to be achieved by using envelope thermal insulation codes. This paper investigates the ability of the current codes to achieve such a benchmark and evaluates their impact on building energy consumption. The results of a simulation study are employed to investigate the impact of the Bahraini codes on the energy and environmental performance of buildings. The study focuses on air-conditioned commercial buildings and concludes that envelope codes, at best, are likely to reduce the energy use of the commercial sector by 25% if the building envelope is well-insulated and efficient glazing is used. Bahraini net CO₂ emissions could drop to around 7.1%. The simulation results show that the current energy codes alone are not sufficient to achieve a 40% reduction benchmark, and therefore, more effort should be spent on moving towards a more comprehensive approach.     

The impact of informational feedback on energy consumption—A survey of the experimental evidence

In theory, In-Home Displays (IHDs) can revolutionize the way utilities communicate information to customers because they can induce changes in customer behavior even when they are not accompanied by a change in electric prices or rebates for purchasing efficient equipment. IHDs provide consumers with direct feedback—real-time information on energy consumption and costs—and turn a once opaque and static electric bill into a transparent, dynamic, and controllable process. However, to what extent do consumers actually respond to the direct feedback provided by IHDs?