Determination of electricity consumers’ load profiles via weighted evidence accumulation clustering using subsampling

With the electricity market liberalization, the distribution and retail companies are looking for better market strategies based on adequate information upon the consumption patterns of its electricity consumers. A fair insight on the consumers’ behavior will permit the definition of specific contract aspects based on the different consumption patterns. In order to form the different consumers’ classes, and find a set of representative consumption patterns we use electricity consumption data from a utility client’s database and two approaches: Two-step clustering algorithm and the WEACS approach based on evidence accumulation (EAC) for combining partitions in a clustering ensemble. While EAC uses a voting mechanism to produce a co-association matrix based on the pairwise associations obtained from N partitions and where each partition has equal weight in the combination process, the WEACS approach uses subsampling and weights differently the partitions. As a complementary step to the WEACS approach, we combine the partitions obtained in the WEACS approach with the ALL clustering ensemble construction method and we use the Ward Link algorithm to obtain the final data partition. The characterization of the obtained consumers’ clusters was performed using the C5.0 classification algorithm. Experiment results showed that the WEACS approach leads to better results than many other clustering approaches.

Typical load profiles in the smart grid context – a clustering methods comparison

The present research paper presents five different clustering methods to identify typical load profiles of medium voltage (MV) electricity consumers. These methods are intended to be used in a smart grid environment to extract useful knowledge about customer’s behaviour. The obtained knowledge can be used to support a decision tool, not only for utilities but also for consumers. Load profiles can be used by the utilities to identify the aspects that cause system load peaks and enable the development of specific contracts with their customers. The framework presented throughout the paper consists in several steps, namely the pre-processing data phase, clustering algorithms application and the evaluation of the quality of the partition, which is supported by cluster validity indices. The process ends with the analysis of the discovered knowledge. To validate the proposed framework, a case study with a real database of 208 MV consumers is used.

A combined engineering and statistical model of UK domestic appliance electrical load profiles

The development of a combined engineering and statistical Artificial Neural Network model of UK domestic appliance load profiles is presented. The model uses diary-style appliance use data and a survey questionnaire collected from 51 suburban households and 46 rural households during the summer of 2010 and2011 respectively. It also incorporates measured energy data and is sensitive to socioeconomic, physical dwelling and temperature variables. A prototype model is constructed in MATLAB using a two layer feed forward network with back propagation training which has a 12:10:24 architecture. Model outputs include appliance load profiles which can be applied to the fields of energy planning (microrenewables and smart grids), building simulation tools and energy policy.

Definition of MV Load Diagrams via Weighted Evidence Accumulation Clustering using Subsampling

A definition of medium voltage (MV) load diagrams was made, based on the data base knowledge discovery process. Clustering techniques were used as support for the agents of the electric power retail markets to obtain specific knowledge of their customers’ consumption habits. Each customer class resulting from the clustering operation is represented by its load diagram. The Two-step clustering algorithm and the WEACS approach based on evidence accumulation (EAC) were applied to an electricity consumption data from a utility client’s database in order to form the customer’s classes and to find a set of representative consumption patterns. The WEACS approach is a clustering ensemble combination approach that uses subsampling and that weights differently the partitions in the co-association matrix. As a complementary step to the WEACS approach, all the final data partitions produced by the different variations of the method are combined and the Ward Link algorithm is used to obtain the final data partition. Experiment results showed that WEACS approach led to better accuracy than many other clustering approaches. In this paper the WEACS approach separates better the customer’s population than Two-step clustering algorithm.

Knowledge extraction from medium voltage load diagrams to support the definition of electrical tariffs

With the electricity market liberalization, distribution and retail companies are looking for better market strategies based on adequate information upon the consumption patterns of its electricity customers. In this environment all consumers are free to choose their electricity supplier. A fair insight on the customer´s behaviour will permit the definition of specific contract aspects based on the different consumption patterns. In this paper Data Mining (DM) techniques are applied to electricity consumption data from a utility client’s database. To form the different customer´s classes, and find a set of representative consumption patterns, we have used the Two-Step algorithm which is a hierarchical clustering algorithm. Each consumer class will be represented by its load profile resulting from the clustering operation. Next, to characterize each consumer class a classification model will be constructed with the C5.0 classification algorithm.

A Data Mining Framework for Electric Load Profiling

This paper consists in the characterization of medium voltage (MV) electric power consumers based on a data clustering approach. It is intended to identify typical load profiles by selecting the best partition of a power consumption database among a pool of data partitions produced by several clustering algorithms. The best partition is selected using several cluster validity indices. These methods are intended to be used in a smart grid environment to extract useful knowledge about customers’ behavior. The data-mining-based methodology presented throughout the paper consists in several steps, namely the pre-processing data phase, clustering algorithms application and the evaluation of the quality of the partitions. To validate our approach, a case study with a real database of 1.022 MV consumers was used.

Load Profiling Tool to Support Smart Grid Operation Scenarios

This paper presents the characterization of high voltage (HV) electric power consumers based on a data clustering approach. The typical load profiles (TLP) are obtained selecting the best partition of a power consumption database among a pool of data partitions produced by several clustering algorithms. The choice of the best partition is supported using several cluster validity indices. The proposed data-mining (DM) based methodology, that includes all steps presented in the process of knowledge discovery in databases (KDD), presents an automatic data treatment application in order to preprocess the initial database in an automatic way, allowing time saving and better accuracy during this phase. These methods are intended to be used in a smart grid environment to extract useful knowledge about customers’ consumption behavior. To validate our approach, a case study with a real database of 185 HV consumers was used.

Load and Season Adapted Solar Collectors

In Sweden solar irradiation and space heating loads are unevenly distributed over the year. Domestic hot water loads may be nearly constant. Test results on solar collector performance are often reported as yearly output of a certain collector at fixed temperatures, e g 25, 50 and 75 C. These data are not suitable for dimensioning of solar systems, because the actual performance of the collector depends heavily on solar fraction and load distribution over the year.At higher latitudes it is difficult to attain high solar fractions for buildings, due to overheating in summer and small marginal output for added collector area. Solar collectors with internal reflectors offer possibilities to evade overheating problems and deliver more energy at seasons when the load is higher. There are methods for estimating the yearly angular irradiation distribution, but there is a lack of methods for describing the load and the storage in such a way as to enable optical design of season and load adapted collectors.This report describes two methods for estimation of solar system performance with relevance for season and load adaption. Results regarding attainable solar fractions as a function of collector features, load profiles, load levels and storage characteristics are reported. The first method uses monthly collector output data at fixed temperatures from the simulation program MINSUN for estimating solar fractions for different load profiles and load levels. The load level is defined as estimated yearly collector output at constant collector temperature divided be yearly load. This table may examplify the results:CollectorLoadLoadSolar Improvementtypeprofile levelfractionover flat plateFlat plateDHW 75 %59 %Load adaptedDHW 75 %66 %12 %Flat plateSpace heating 50 %22 %Load adaptedSpace heating 50 %28 %29 %The second method utilises simulations with one-hour timesteps for collectors connected to a simplified storage and a variable load. Collector output, optical and thermal losses, heat overproduction, load level and storage temperature are presented as functions of solar incidence angles. These data are suitable for optical design of load adapted solar collectors. Results for a Stockholm location indicate that a solar combisystem with a solar fraction around 30 % should have collectors that reduce heat production at solar heights above 30 degrees and have optimum efficiency for solar heights between 8 and 30 degrees.

Load profiling and data mining techniques in electricity deregulated market

This paper presents load profiles of electricity customers, using the knowledge discovery in databases (KDD) procedure, a data mining technique, to determine the load profiles for different types of customers. In this paper, the current load profiling methods are compared using data mining techniques, by analysing and evaluating these classification techniques. The objective of this study is to determine the best load profiling methods and data mining techniques to classify, detect and predict non-technical losses in the distribution sector, due to faulty metering and billing errors, as well as to gather knowledge on customer behaviour and preferences so as to gain a competitive advantage in the deregulated market. This paper focuses mainly on the comparative analysis of the classification techniques selected; a forthcoming paper will focus on the detection and prediction methods.

Connection design without accurate LV feeder load data:an argument for LV monitoring

Since privatisation, maintenance of DNO LV feeder maximum demand information has gradually demised in some Utility Areas, and it is postulated that lack of knowledge about 11kV and LV electrical networks is resulting in a less economical and energy efficient Network as a whole. In an attempt to quantify the negative impact, this paper examines ten postulated new connection scenarios for a set of real LV load readings, in order to find the difference in design solutions when LV load readings were and were not known. The load profiles of the substations were examined in order to explore the utilisation profile. It was found that in 70% of the scenarios explored, significant cost differences were found. These cost differences varied by an average of 1000%, between schemes designed with and without load readings. Obviously, over designing a system and therefore operating more, underutilised transformers becomes less financially beneficial and less energy efficient. The paper concludes that new connection design is improved in terms of cost when carried out based on known LV load information and enhances the case for regular maximum feeder demand information and/or metering of LV feeders. © 2013 IEEE.

GA-ANN Short-Term Electricity Load Forecasting

This paper presents a methodology for short-term load forecasting based on genetic algorithm feature selection and artificial neural network modeling. A feed forward artificial neural network is used to model the 24-h ahead load based on past consumption, weather and stock index data. A genetic algorithm is used in order to find the best subset of variables for modeling. Three data sets of different geographical locations, encompassing areas of different dimensions with distinct load profiles are used in order to evaluate the methodology. The developed approach was found to generate models achieving a minimum mean average percentage error under 2 %. The feature selection algorithm was able to significantly reduce the number of used features and increase the accuracy of the models.

Impacto da integração dos veículos eléctricos nos edifícios multi-habitacionais, nas redes de distribuição de baixa tensão e no diagrama de carga nacional

Associado à escassez dos combustíveis fósseis e ao desejado controlo de emissões nocivas para a atmosfera, assistimos no mundo ao desenvolvimento do um novo paradigma — a mobilidade eléctrica. Apesar das variações de maior ou menor arbítrio político dos governos, do excelente ou débil desenvolvimento tecnológico, relacionados com os veículos eléctricos, estamos perante um caminho, no que diz respeito à mobilidade eléctrica, que já não deve ser encarado como uma moda mas como uma orientação para o futuro da mobilidade. Portugal tendo dado mostras que pretende estar na dianteira deste desafio, necessita equacionar e compreender em que condições existirá uma infra-estrutura nacional capaz de fazer o veículo eléctrico vingar. Assim, neste trabalho, analisa-se o impacto da mobilidade eléctrica em algumas dessas infra-estruturas, nomeadamente nos edifícios multi-habitacionais e redes de distribuição em baixa tensão. São criados neste âmbito, quatro perfis de carregamento dos EVs nomeadamente: nas horas de chegada a casa; nas horas de vazio com início programado pelo condutor; nas horas de vazio controlado por operador de rede (“Smart Grid”); e um cenário que contempla a utilização do V2G. Com a obrigação legal de nos novos edifícios serem instaladas tomadas para veículos eléctricos, é estudado, com os cenários anteriores a possibilidade de continuar a conceber as instalações eléctricas, sem alterar algumas das disposições legais, ao abrigo dos regulamentos existentes. É também estudado, com os cenários criados e com a previsão da venda de veículos eléctricos até 2020, o impacto deste novo consumo no diagrama de carga do Sistema Eléctrico Nacional. Mostra-se assim que a introdução de sistemas inteligentes de distribuição de energia [Smartgrid e vehicle to grid” (V2G)] deverá ser encarada como a solução que por excelência contribuirá para um aproveitamento das infra-estruturas existentes e simultaneamente um uso acessível para os veículos eléctricos.

Data mining contributions to characterize MV consumers and to improve the suppliers-consumers settlements

This paper deals with the establishment of a characterization methodology of electric power profiles of medium voltage (MV) consumers. The characterization is supported on the data base knowledge discovery process (KDD). Data Mining techniques are used with the purpose of obtaining typical load profiles of MV customers and specific knowledge of their customers’ consumption habits. In order to form the different customers’ classes and to find a set of representative consumption patterns, a hierarchical clustering algorithm and a clustering ensemble combination approach (WEACS) are used. Taking into account the typical consumption profile of the class to which the customers belong, new tariff options were defined and new energy coefficients prices were proposed. Finally, and with the results obtained, the consequences that these will have in the interaction between customer and electric power suppliers are analyzed.

MV producers and consumers agents characterization with DSM techniques

This paper consist in the establishment of a Virtual Producer/Consumer Agent (VPCA) in order to optimize the integrated management of distributed energy resources and to improve and control Demand Side Management DSM) and its aggregated loads. The paper presents the VPCA architecture and the proposed function-based organization to be used in order to coordinate the several generation technologies, the different load types and storage systems. This VPCA organization uses a frame work based on data mining techniques to characterize the costumers. The paper includes results of several experimental tests cases, using real data and taking into account electricity generation resources as well as consumption data.

An integrated system to support electricity tariff contract definition

This paper presents an integrated system that helps both retail companies and electricity consumers on the definition of the best retail contracts and tariffs. This integrated system is composed by a Decision Support System (DSS) based on a Consumer Characterization Framework (CCF). The CCF is based on data mining techniques, applied to obtain useful knowledge about electricity consumers from large amounts of consumption data. This knowledge is acquired following an innovative and systematic approach able to identify different consumers’ classes, represented by a load profile, and its characterization using decision trees. The framework generates inputs to use in the knowledge base and in the database of the DSS. The rule sets derived from the decision trees are integrated in the knowledge base of the DSS. The load profiles together with the information about contracts and electricity prices form the database of the DSS. This DSS is able to perform the classification of different consumers, present its load profile and test different electricity tariffs and contracts. The final outputs of the DSS are a comparative economic analysis between different contracts and advice about the most economic contract to each consumer class. The presentation of the DSS is completed with an application example using a real data base of consumers from the Portuguese distribution company.