Creative User-Centered Visualization Design for Energy Analysts and Modelers

We enhance a user-centered design process with techniques that deliberately promote creativity to identify opportunities for the visualization of data generated by a major energy supplier. Visualization prototypes developed in this way prove effective in a situation whereby data sets are largely unknown and requirements open – enabling successful exploration of possibilities for visualization in Smart Home data analysis. The process gives rise to novel designs and design metaphors including data sculpting. It suggests: that the deliberate use of creativity techniques with data stakeholders is likely to contribute to successful, novel and effective solutions; that being explicit about creativity may contribute to designers developing creative solutions; that using creativity techniques early in the design process may result in a creative approach persisting throughout the process. The work constitutes the first systematic visualization design for a data rich source that will be increasingly important to energy suppliers and consumers as Smart Meter technology is widely deployed. It is novel in explicitly employing creativity techniques at the requirements stage of visualization design and development, paving the way for further use and study of creativity methods in visualization design

Understanding domestic appliance use through their linkages to common activities

Activities are a descriptive term for the common ways households spend their time. Examples include daily routines such as cooking, doing laundry, and Computing. Smart energy meter data can be used to generate time profiles of activities that are meaningful to households’ own lived experience. Activities are therefore a lens through which energy feedback to households can be made salient and understandable. This paper demonstrates how hourly time profiles of household activities can be inferred from smart energy meter data, supplemented by appliance monitors and environmental sensors. In-depth interviews and home surveys are used to identify appliances and devices used for a range of activities. These relationships between te chnologies and activities are captured in an ‘activity ontology’ that can be applied to smart meter data to make inferences on hourly time profiles of up to nine everyday activities. Results are presented from six homes participating in a UK trial of smart home technologies. The duration of activities and when they are carried out is examined within households. The time profile of domestic activities has routine characteristics but these tend to vary widely between households with different socio-demo graphic characteristics. Analysing the energy consumption associated with different activities leads to a useful means of providing activity-itemised energy feedback, and also reveals certain households to be high energy-using across a range of activities

Building appliances energy performance assessment

Trabalho de Projeto de Mestrado, Informática, 2021, Universidade de Lisboa, Faculdade de CiênciasO consumo de energia tem vindo a crescer na União Europeia todos os anos, sendo de prever que, a curto prazo, se torne insustentável. No sentido de prevenir este cenário, a Comissão Europeia decidiu definir uma Estratégia Energética para a União Europeia, destacando dois objetivos: aumentar a eficiência energética e promover a descarbonização. Atualmente, cerca de 72% dos edifícios existentes na União Europeia não são energeticamente eficientes. Este problema motivou-nos à pesquisa e criação de soluções que permitam uma melhor avaliação do consumo energético por dispositivos elétricos em edifícios residenciais. Neste contexto, o trabalho desenvolvido nesta tese consiste no desenho de uma solução de monitorização remota que recolhe informações de consumo energético recorrendo a técnicas de intrusive load monitoring, onde cada dispositivo elétrico individual é continuamente monitorizado quanto ao seu consumo energético. Esta abordagem permite compreender o consumo de energia, em tempo real e no dia-a-dia. Este conhecimento oferece-nos a capacidade de avaliar as diferenças existentes entre as medições laboratoriais (abordagem utilizada no sistema de rotulagem de equipamentos elétricos de acordo com a sua eficiência energética) e os consumos domésticos estimados. Para tal, nesta tese exploram-se abordagens de machine learning que pretendem descrever padrões de consumo, bem como reconhecer marcas, modelos e que funções os dispositivos elétricos estarão a executar. O principal objetivo deste trabalho é desenhar e implementar um protótipo de uma solução de IoT flexível e de baixo custo para avaliar equipamentos elétricos. Será utilizado um conjunto de sensores que recolherá dados relacionados com o consumo de energia e os entrega à plataforma SATO para serem posteriormente processados. O sistema será usado para monitorar aparelhos comumente encontrados em residências. Além disso, o sistema terá a capacidade de monitorizar o consumo de água de aparelhos que necessitem de abastecimento de água, como máquinas de lavar e de lavar louça. Os dados recolhidos serão usados para classificação dos aparelhos e modos de operação dos mesmos, em tempo real, permitindo fornecer relatórios sobre o consumo energético e modo de uso dos aparelhos, com grande grau de detalhe. Os relatórios podem incluir o uso de energia por vários ciclos de operação. Por exemplo, um aparelho pode executar vários ciclos de operação, como uma máquina de lavar que consume diferentes quantidades de energia elétrica e água consoante o modo de operação escolhido pelo utilizador. Toda a informação recolhida pode ser posteriormente utilizada em novos serviços de recomendação que ajudaram os utilizadores a definir melhor as configurações adequadas a um determinado dispositivo, minimizando o consumo energético e melhorando a sua eficiência. Adicionalmente toda esta informação pode ser utilizada para o diagnóstico de avarias e/ou manutenção preventiva. Em termos de proposta, o trabalho desenvolvido nesta tese tem as seguintes contribuições: Sistema de monitorização remota: o sistema de monitorização desenhado e implementado nesta tese avança o estado da arte dos sistemas de monitorização propostos pela literatura devido ao facto de incluir uma lista aprimorada de sensores que podem fornecer mais informações sobre os aparelhos, como o consumo de água da máquina de lavar. Além disso, é altamente flexível e pode ser implementado sem esforço em dispositivos novos ou antigos para monitorização de consumo de recursos. Conjunto de dados de consumo de energia de eletrodomésticos: Os dados recolhidos podem ser usados para futura investigação científica sobre o consumo de consumo de energia, padrões de uso de energia pelos eletrodomésticos e classificação dos mesmos. Abordagem de computação na borda (Edge Computing): O sistema de monitorização proposto explora o paradigma de computação na borda, onde parte da computação de preparação de dados é executada na borda, libertando recursos da nuvem para cálculos essenciais e que necessitem de mais poder computacional. Classificação precisa de dispositivos em tempo real: Coma proposta desenhada nesta tese, podemos classificar os dispositivos com alta precisão, usando os dados recolhidos pelo sistema de monitorização desenvolvido na tese. A abordagem proposta consegue classificar os dispositivos, que são monitorizados, com baixas taxas de falsos positivos. Para fácil compreensão do trabalho desenvolvido nesta tese, de seguida descreve-se a organização do documento. O Capítulo 1 apresenta o problema do consumo de energia na União Europeia e discute o aumento do consumo da mesma. O capítulo apresenta também os principais objetivos e contribuições do trabalho. No Capítulo 2 revê-se o trabalho relacionado em termos de sistema de monitorização remota, que inclui sensores, microcontroladores, processamento e filtragem de sinal. Por fim, este capítulo revê os trabalhos existentes na literatura relacionados com o problema de classificação de dispositivos usando abordagens de machine learning. No Capítulo 3 discutem-se os requisitos do sistema e o projeto de arquitetura conceitual do sistema. Neste capítulo é proposta uma solução de hardware, bem como, o software e firmware necessários à sua operação. Os algoritmos de machine learning necessários à classificação são também discutidos, em termos de configurações necessárias e adequadas ao problema que queremos resolver nesta tese. O Capítulo 4 representa a implementação de um protótipo que servirá de prova de conceito dos mecanismos discutidos no Capítulo 3. Neste capítulo discute-se também a forma de integração do protótipo na plataforma SATO. Com base na implementação feita, no Capítulo 5 especificam-se um conjunto de testes funcionais que permitem avaliar o desempenho da solução proposta e discutem-se os resultados obtidos a partir desses testes. Por fim, o Capítulo 6 apresenta as conclusões e o trabalho futuro que poderá ser desenvolvido partindo da solução atual.Energy consumption is daily growing in European Union (EU). One day it will become hardly sustainable. For this not to happen European Commission decided to implement a European Union Strategy, emphasizing two objectives: increasing energy efficiency and decarbonization. About 72% of all buildings in the EU are not adapted to be energy efficient. This problem encourages us to create solutions that would help assess the energy consumption of appliances at residential houses. In this thesis, we proposed a system that collects data using an intrusive load monitoring approach, where each appliance will have a dedicated monitoring rig to collect the energy consumption data. The proposed solution will help us understand the real-life consumption of each device being monitored and compare the laboratory measurements observed versus domestic consumption estimated by the energy consumption based on the EU energy efficiency labelling system. The system proposed detects device consumption patterns and recognize its brand, model and what actions that appliance is executing, e.g., program of washing in a washing machine. To achieve our goal, we designed a hardware solution capable of collecting sensor data, filtering and send it to a cloud platform (the SATO platform). Additionally, in the cloud, we have a Machine Learning solution that deals with the data and recognizes the appliance and its operation modes. This recognition allows drawing a device/settings profile, which can detect faults and create a recommendation service that helps users define the better settings for a specific appliance, minimizing energy consumption and improving efficiency. Finally, we examine our prototype approach of the system implemented for targeted objectives in this project report. The document describes the experiments that we did and the final results. Our results show that we can identify the appliance and some of its operation modes. The proposed approach must be improved to make the identification of all operation modes. However, the current version of the system shows exciting results. It can be used to support the design of a new labelling system where daily operation measures can be used to support the new classification system. This way, we have an approach that allows improving the energy consumption, making builds more efficient

PyDTS: A Python Toolkit for Deep Learning Time Series Modelling

© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/Abstract In this article, the topic of time series modelling is discussed. It highlights the criticality of analysing and forecasting time series data across various sectors, identifying five primary application areas: denoising, forecasting, nonlinear transient modelling, anomaly detection, and degradation modelling. It further outlines the mathematical frameworks employed in a time series modelling task, categorizing them into statistical, linear algebra, and machine- or deep-learning-based approaches, with each category serving distinct dimensions and complexities of time series problems. Additionally, the article reviews the extensive literature on time series modelling, covering statistical processes, state space representations, and machine and deep learning applications in various fields. The unique contribution of this work lies in its presentation of a Python-based toolkit for time series modelling (PyDTS) that integrates popular methodologies and offers practical examples and benchmarking across diverse datasets.Peer reviewe

Big Data Analytics in Smart Grids for Renewable Energy Networks: Systematic Review of Information and Communication Technology Tools

El desarrollo industrial y económico de los países industrializados, a partir del siglo XIX, ha ido de la mano del desarrollo de la electricidad, del motor de combustión interna, de los ordenadores, de Internet, de la utilización de datos y del uso intensivo del conocimiento centrado en la ciencia y la tecnología. La mayoría de las fuentes de energía convencionales han demostrado ser finitas y agotables. A su vez, las diferentes actividades de producción de bienes y servicios que utilizan combustibles fósiles y energía convencional, han aumentado significativamente la contaminación del medio ambiente, y con ello, han contribuido al calentamiento global. El objetivo de este trabajo fue realizar una aproximación teórica a las tecnologías de análisis de datos e inteligencia de negocio aplicadas a las redes de sistemas eléctricos inteligentes con energías renovables. Para este trabajo se realizó una revisión bibliométrica y bibliográfica sobre Big Data Analytics, herramientas TIC de la industria 4.0 y Business intelligence en diferentes bases de datos disponibles en el dominio público. Los resultados del análisis indican la importancia del uso de la analítica de datos y la inteligencia de negocio en la gestión de las empresas energéticas. El trabajo concluye señalando cómo se está aplicando la inteligencia de negocio y la analítica de datos en ejemplos concretos de empresas energéticas y su creciente importancia en la toma de decisiones estratégicas y operativasThe industrial and economic development of the industrialized countries, from the nineteenth century, has gone hand in hand with the development of electricity, the internal combustion engine, computers, the Internet, data use and the intensive use of knowledge focused on science and the technology. Most conventional energy sources have proven to be finite and exhaustible. In turn, the different production activities of goods and services using fossil fuels and conventional energy, have significantly increased the pollution of the environment, and with it, contributed to global warming. The objective of this work was to carry out a theoretical approach to data analytics and business intelligence technologies applied to smart electrical-system networks with renewable energies. For this paper, a bibliometric and bibliographic review about Big Data Analytics, ICT tools of industry 4.0 and Business intelligence was carried out in different databases available in the public domain. The results of the analysis indicate the importance of the use of data analytics and business intelligence in the management of energy companies. The paper concludes by pointing out how business intelligence and data analytics are being applied in specific examples of energy companies and their growing importance in strategic and operational decision makinghttps://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000192503https://scholar.google.com/citations?user=9HLAZYUAAAAJ&hl=eshttps://scienti.minciencias.gov.co/gruplac/jsp/visualiza/visualizagr.jsp?nro=00000000005961https://orcid.org/0000-0003-1166-198

Big data analytics in smart grids: a review

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Energy Data Analytics for Smart Meter Data

The principal advantage of smart electricity meters is their ability to transfer digitized electricity consumption data to remote processing systems. The data collected by these devices make the realization of many novel use cases possible, providing benefits to electricity providers and customers alike. This book includes 14 research articles that explore and exploit the information content of smart meter data, and provides insights into the realization of new digital solutions and services that support the transition towards a sustainable energy system. This volume has been edited by Andreas Reinhardt, head of the Energy Informatics research group at Technische Universität Clausthal, Germany, and Lucas Pereira, research fellow at Técnico Lisboa, Portugal

An Artificial Intelligence Energy Management System For An Education Building

The study of energy demand and consumption has become a topic of increasing importance as a result of the growing interest in energy sustainability. In the present energy crisis of South Africa, where Eskom is currently the only electricity provider, it cannot always meet the electricity needs of the customers. Therefore, the consideration of electrical power savings in education buildings can play a huge role, which was implemented in this study. University campuses represent a specific group of various buildings with significant energy demands and consumptions. Due to the various buildings, a University campus can be seen as a small town on its own, therefore, it offers an excellent test bed to monitor energy consumption and to understand the demand for electricity of the different buildings. In addition, it was possible to predict with an artificial intelligence concept using different prediction models when peak load will occur and to determine a maximum demand. A suitable database for the Engineering Technology Building (ETB) at the Central University of Technology (CUT), Free State, was created and available data of the electricity energy usage were collected and analysed for this purpose, with the aid of utilising methods, namely Moving Average, Straight Line and Kalman Filter. The available data were tested and evaluated by the switchgear, according to the priority list, and it proved to be successful. There was a total saving of approximately 5.83% on the power consumption per day. This proved to work well and a greater percentage of savings could be achieved by switching another circuit breaker according to the priority list