Electricity consumption and household characteristics: Implications for census-taking in a smart metered future

This paper assesses the feasibility of determining key household characteristics based on temporal load profiles of household electricity demand. It is known that household characteristics, behaviours and routines drive a number of features of household electricity loads in ways which are currently not fully understood. The roll out of domestic smart meters in the UK and elsewhere could enable better understanding through the collection of high temporal resolution electricity monitoring data at the household level. Such data affords tremendous potential to invert the established relationship between household characteristics and temporal load profiles. Rather than use household characteristics as a predictor of loads, observed electricity load profiles, or indicators based on them, could instead be used to impute household characteristics. These micro level imputed characteristics could then be aggregated at the small area level to produce ‘census-like’ small area indicators. This work briefly reviews the nature of current and future census taking in the UK before outlining the household characteristics that are to be found in the UK census and which are also known to influence electricity load profiles. It then presents descriptive analysis of two smart meter-like datasets of half-hourly domestic electricity consumption before reporting on the results from a multilevel modelling-based analysis of the same data. The work concludes that a number of household characteristics of the kind to be found in UK census-derived small area statistics may be predicted from particular load profile indicators. A discussion of the steps required to test and validate this approach and the wider implications for census taking is also provided

Impacts of Raw Data Temporal Resolution Using Selected Clustering Methods on Residential Electricity Load Profiles

There is growing interest in discerning behaviors of electricity users in both the residential and commercial sectors. With the advent of high-resolution time-series power demand data through advanced metering, mining this data could be costly from the computational viewpoint. One of the popular techniques is clustering, but depending on the algorithm the resolution of the data can have an important influence on the resulting clusters. This paper shows how temporal resolution of power demand profiles affects the quality of the clustering process, the consistency of cluster membership (profiles exhibiting similar behavior), and the efficiency of the clustering process. This work uses both raw data from household consumption data and synthetic profiles. The motivation for this work is to improve the clustering of electricity load profiles to help distinguish user types for tariff design and switching, fault and fraud detection, demand-side management, and energy efficiency measures. The key criterion for mining very large data sets is how little information needs to be used to get a reliable result, while maintaining privacy and security

A reduced-dimension feature extraction method to represent retail store electricity profiles

Copyright © 2022 The Author(s). Characterising the inter-seasonal energy performance of buildings is a useful tool for a business to understand what is ‘normal’ for its portfolio of premises and to detect anomalous patterns of energy demand. When adding a new building to the portfolio, it will be useful to predict what will be the likely energy use as part of on-going monitoring of the site. For a large portfolio of buildings with, say, half-hourly energy use measurements (48 dimensions), analysis and prediction will require machine learning tools. Even so, it is advantageous to minimise the amount of data and number of dimensions and features required to find useful patterns in the measurement stream. Our aim is to devise a reduced feature set that can generate a statistically reasonable representation of daily electricity load profiles of retail stores and small supermarkets. We then test if our method is sufficiently accurate to predict and cluster measured patterns of demand. We propose an automatic method to extract features such as times and average demands from electricity load profiles. We used four regression models for prediction and six clustering methods to compare with the results obtained using all of the readings in the load profile. We found that the reduced feature set gave a good representation of the load profile, with only small prediction and clustering errors. The results are robust as prediction is supervised learning and clustering is unsupervised. This simplified feature set is a concise way to represent profiles without using small variances of the demand that do not add useful information to the overall picture. As modern sensor systems increase the volume, availability, and immediacy of data, using reduced dimensional datasets will be key to extracting useful information from high-resolution data streams

Estratégias de agrupamento de consumidores residenciais para o melhoramento de ações de eficiência energética.

O consumo de energia elétrica vem crescendo a cada dia. Precisamos utilizar a energia elétrica de forma consciente, pois os recursos naturais que são utilizados para a geração de energia podem acabar devido ao seu uso ineficiente. O crescimento populacional das últimas décadas, o aparecimento de mais aparelhos eletrônicos e eletrodomésticos geram um consumo excessivo de energia. Devido ao crescimento no consumo de energia elétrica é necessária a implantação de programas de eficiência energética, que se dá através da introdução de novas tecnologias, incentivo à mudança de hábito do próprio consumidor e uso racional de energia elétrica. O foco deste trabalho é no setor residencial, que é o segundo maior consumidor de energia elétrica no Brasil, e como há consumidores que compartilham características e padrões de carga semelhantes, isso possibilita o uso de agrupamento de dados. Pensando nisso é proposto o uso de agrupamento para auxiliar programas de eficiência energética na análise dos dados dos consumidores e na criação de grupos representativos de uma população. A criação de grupos ajuda a concessionária de energia a fornecer ofertas comerciais ou recomendações específicas para grupos específicos, diminuir a complexidade das análises que teriam que ser feitas em uma população e obter relacionamentos personalizados, mais eficazes e equitativos entre os fornecedores de energia e seus clientes. O agrupamento irá proporcionar a aplicação de soluções que ajudem o consumidor a utilizar energia elétrica de forma eficiente, a partir do momento em que ele recebe informações sobre seu consumo e como ele poderá utilizar essas informações, sabendo o que elas irão proporcionar como resultado. Este trabalho iniciou-se com a investigação de medidas de dissimilaridade para representar a semelhança entre perfis de consumo de energia elétrica (um dos fatores utilizados para os agrupamentos) e entre as três medidas utilizadas a distância Euclidiana se destacou com os melhores resultados nos experimentos feitos, seja variando a quantidade de observações das séries ou a base de dados. Após isso foram feitos agrupamentos utilizando 4 fatores extraídos da base de dados e assim criados 15 cenários de agrupamentos a partir da combinação desses fatores. Por meio dos resultados desses agrupamentos foi possível reduzir a quantidade de cenários por serem semelhantes e também escolher os cenários (fatores) mais relevantes a serem considerados quando se quer criar grupos de consumidores residenciais.The consumption of electric energy has been increasing every day. We need to use electric power in a conscious way, because the natural resources that are used for the generation of energy can end up due to its inefficient use. The population growth of the last decades, the appearance of more electronic devices and appliances generate an excessive consumption of energy. Due to the growth in the consumption of electric energy, it is necessary to implement energy efficiency programs, which are carried out through the introduction of new technologies, an incentive to change the consumer’s habit and rational use of electric energy. The focus of this work is on the residential sector, which is the second largest consumer of electricity in Brazil, and since there are consumers who share similar characteristics and load patterns, this allows the use of data grouping. Thinking about that, the use of clustering to support energy efficiency programs in the analysis of consumer data and in the creation of representative groups of a population is proposed. Groups creation helps the utility to provide commercial offers or specific recommendations for specific groups, reduce the complexity of the analyzes that would have to be done in a population, and get personalized, more effective and equitable relationships between energy suppliers and their customers. The clustering will provide the application of solutions that help the consumer to use electricity efficiently, from the moment he receives information about his consumption and how he can use that information, knowing what they will provide as a result. This work began with the investigation of measures of dissimilarity to represent the similarity between profiles of electric energy consumption (one of the factors used for the clustering) and among the three measures used the Euclidean distance stood out with the best results in the experiments made, either by varying the number of observations of the series or the database. After that, clusters were made using 4 factors extracted from the database and thus 15 clustering scenarios were created from the combination of these factors. Through the results of these clustering it was possible to reduce the number of scenarios to be similar and also to choose the most relevant scenarios to consider when creating groups of residential consumers

Low-dimensional representations of neural time-series data with applications to peripheral nerve decoding

Bioelectronic medicines, implanted devices that influence physiological states by peripheral neuromodulation, have promise as a new way of treating diverse conditions from rheumatism to diabetes. We here explore ways of creating nerve-based feedback for the implanted systems to act in a dynamically adapting closed loop. In a first empirical component, we carried out decoding studies on in vivo recordings of cat and rat bladder afferents. In a low-resolution data-set, we selected informative frequency bands of the neural activity using information theory to then relate to bladder pressure. In a second high-resolution dataset, we analysed the population code for bladder pressure, again using information theory, and proposed an informed decoding approach that promises enhanced robustness and automatic re-calibration by creating a low-dimensional population vector. Coming from a different direction of more general time-series analysis, we embedded a set of peripheral nerve recordings in a space of main firing characteristics by dimensionality reduction in a high-dimensional feature-space and automatically proposed single efficiently implementable estimators for each identified characteristic. For bioelectronic medicines, this feature-based pre-processing method enables an online signal characterisation of low-resolution data where spike sorting is impossible but simple power-measures discard informative structure. Analyses were based on surrogate data from a self-developed and flexibly adaptable computer model that we made publicly available. The wider utility of two feature-based analysis methods developed in this work was demonstrated on a variety of datasets from across science and industry. (1) Our feature-based generation of interpretable low-dimensional embeddings for unknown time-series datasets answers a need for simplifying and harvesting the growing body of sequential data that characterises modern science. (2) We propose an additional, supervised pipeline to tailor feature subsets to collections of classification problems. On a literature standard library of time-series classification tasks, we distilled 22 generically useful estimators and made them easily accessible.Open Acces

A Temporal Approach to Characterizing Electrical Peak Demand: Assessment of GHG Emissions at the Supply Side and Identification of Dominant Household Factors at the Demand Side

When electricity demand is at its highest it is most costly to generate and transmit and is usually considered to produce the greatest greenhouse gas (GHG) emissions, so reducing peaks can have a double benefit. In most nations, these peaks in demand occur daily in the mornings and evenings. This PhD research developed a new way of assessing GHG emissions produced by the generation of electricity at different times of the day, including at peak time. It also investigated the dominant factors driving peaks in household demand and developed a new analytical approach to identify these factors so as to assist in developing targeted demand management. Two contrasting countries - New Zealand and Bangladesh - were chosen to apply the research. These two countries have very different climatic conditions, economic conditions, socio-demographic characteristics, electricity generation sector, and emissions from electricity generation, so the research findings could be tested and compared. To assess GHG emissions, an analytical approach was developed - ‘time-varying carbon intensity analysis (TVCIA)’ - to explore the relationships between GHG emissions and peaks in demand. Applied to 2015 data from New Zealand, a country with around 80% renewable generation dominated by hydro, it was found that New Zealand’s carbon intensity was largely uncorrelated with demand. This finding was counter to some perceptions in the electricity sector in New Zealand where it is assumed that peak demand always means higher GHG emissions. In contrast, when the method was applied to Bangladesh, which has an electricity system dominated by fossil fuel generation, it showed that daily peaks in demand had the highest GHG emissions. Therefore, reduction in demand at peak times could be a potential option to reduce GHG emissions in Bangladesh. In New Zealand, seasonal demand management could be beneficial as GHG emissions can increase significantly in a dry year when hydro lakes are low. For the latter part of the research, a methodology called ‘time-segmented regression analysis (TSRA)’ was developed to identify the dominant factors driving peak electricity demand in households. Applied to a New Zealand dataset, the analysis revealed that methods of water and space heating were the dominant factors in determining peak demand in New Zealand’s households. In contrast, the number of occupants and the number of electrical appliances were dominant factors in determining peak demand in Bangladeshi households. Together the new approaches that have been developed can assist nations in determining the GHG emissions from electricity generation at different times (over days, weeks, months or years) and also determining what factors in households are driving peaks in demand. This is important to help design more effective, targeted energy efficiency and demand management strategies. Together these methods can help in devising programmes for reducing GHG emissions from electricity use