Household Appliances Identification : Hands-on integrative workshop and its adaptation to a social distancing context.

Theoretical and practical knowledge integration is essential in Electrical Engineering. Throughout the degree, students must tackle specific problems where they can put into practice difficult concepts and test their learning. Taking this into consideration, an integrative workshop is implemented in the third year that seeks to strengthen skills such as: analysis and design of signal conditioning circuits, designing and printing circuits, signal acquisition and processing, pattern recognition and classification, integration of a system. The workshop is based on the topic of load identification which is a challenging problem, suitable for developing multiple electrical engineering concepts and also an interesting subject for the initiation of the students to research. This article shares the 2019 edition experience and its adaptation to non-classroom classes in 2020 in the context of social distancing restrictions due the coronavirus disease. The 2019 didactic proposal and the necessary modifications for 2020 are presented and analyzed. Although loosing the fruitful interaction between teachers and students in the lab in 2020, most of the hands-on activities could be maintained helped by the use of USB oscilloscopes/analyzers that give students the functionality of a lab at home. It is concluded that both editions, in spite of their different teaching modalities, achieved good academic results

Non-intrusive load monitoring techniques for activity of daily living recognition

Esta tesis nace con la motivación de afrontar dos grandes problemas de nuestra era: la falta de recursos energéticos y el envejecimiento de la población. Respecto al primer problema, nace en la primera década de este siglo el concepto de Smart Grids con el objetivo de alcanzar la eficiencia energética. Numerosos países comienzan a realizar despliegues masivos de contadores inteligentes ("Smart Meters"), lo que despierta el interés de investigadores que comienzan a desarrollar nuevas técnicas para predecir la demanda. Así, los sistemas NILM (Non-Intrusive Load Monitoring) tratan de predecir el consumo individual de los dispositivos conectados a partir de un único sensor: el contador inteligente. Por otra parte, los grandes avances en la medicina moderna han permitido que nuestra esperanza de vida aumente considerablemente. No obstante, esta longevidad, junto con la baja fertilidad en los países desarrollados, tiene un efecto secundario: el envejecimiento de la población. Unos de los grandes avances es la incorporación de la tecnología en la vida cotidiana, lo que ayuda a los más mayores a llevar una vida independiente. El despliegue de una red de sensores dentro de la vivienda permite su monitorización y asistencia en las tareas cotidianas. Sin embargo, son intrusivos, no escalables y, en algunas ocasiones, de alto coste, por lo que no están preparados para hacer frente al incremento de la demanda de esta comunidad. Esta tesis doctoral nace de la motivación de afrontar estos problemas y tiene dos objetivos principales: lograr un modelo de monitorización sostenible para personas mayores y, a su vez, dar un valor añadido a los sistemas NILM que despierte el interés del usuario final. Con este objetivo, se presentan nuevas técnicas de monitorización basadas en NILM, aunando lo mejor de ambos campos. Esto supone un ahorro considerable de recursos en la monitorización, ya que únicamente se necesita un sensor: el contador inteligente; lo cual da escalabilidad a estos sistemas. Las contribuciones de esta tesis se dividen en dos bloques principales. En el primero se proponen nuevas técnicas NILM optimizadas para la detección de la actividad humana. Así, se desarrolla una propuesta basada en detección de eventos (conexiones de dispositivos) en tiempo real y su clasificación a un dispositivo. Con el objetivo de que pueda integrarse en contadores inteligentes. Cabe destacar que el clasificador se basa en modelos generalizados de dispositivos y no necesita conocimiento específico de la vivienda. El segundo bloque presenta tres nuevas técnicas de monitorización de personas mayores basadas en NILM. El objetivo es proporcionar una monitorización básica pero eficiente y altamente escalable, ahorrando en recursos. Los procesos Cox, log Gaussian Cox Processes (LGCP), monitorizan un único dispositivo si la rutina está estrechamente ligada a este. Así, se propone un sistema de alarmas si se detectan cambios en el comportamiento. LGCP tiene la ventaja de poder modelar periodicidades e incertidumbres propias del comportamiento humano. Cuando la rutina no depende de un único dispositivo, se proponen dos técnicas: una basada en gaussianas mixtas, Gaussian Mixture Models (GMM); y la otra basada en la Teoría de la Evidencia de Dempster-Shafer (DST). Ambas monitorizan y detectan deterioros en la actividad, causados por enfermedades como la demencia y el alzhéimer. Únicamente DST usa incertidumbres que simulan mejor el comportamiento humano y, por tanto, permite alarmas en caso de un repentino desvío. Finalmente, todas las propuestas han sido validadas mediante la evaluación de métricas y la obtención de resultados experimentales. Para ello, se han usado medidas de escenarios reales que han sido recopiladas en bases de datos. Los resultados obtenidos han sido satisfactorios, demostrando que este tipo de monitorización es posible y muy beneficioso para nuestra sociedad. Además, se ha dado a lugar nuevas propuestas que serán desarrolladas en el futuro. Códigos UNESCO: 120320 - sistemas de control medico, 332201 – distribución de la energía, 120701 – análisis de actividades, 120304 – inteligencia artificial, 120807 – plausibilidad, 221402 – patrones

Improving Residential Load Disaggregation for Sustainable Development of Energy via Principal Component Analysis

The useful planning and operation of the energy system requires a sustainability assessment of the system, in which the load model adopted is the most important factor in sustainability assessment. Having information about energy consumption patterns of the appliances allows consumers to manage their energy consumption efficiently. Non-intrusive load monitoring (NILM) is an effective tool to recognize power consumption patterns from the measured data in meters. In this paper, an unsupervised approach based on dimensionality reduction is applied to identify power consumption patterns of home electrical appliances. This approach can be utilized to classify household activities of daily life using data measured from home electrical smart meters. In the proposed method, the power consumption curves of the electrical appliances, as high-dimensional data, are mapped to a low-dimensional space by preserving the highest data variance via principal component analysis (PCA). In this paper, the reference energy disaggregation dataset (REDD) has been used to verify the proposed method. REDD is related to real-world measurements recorded at low-frequency. The presented results reveal the accuracy and efficiency of the proposed method in comparison to conventional procedures of NILM

Detailed energy analysis of a sheet-metal-forming press from electrical measurements

This paper presents a methodology that allows for the detection of the state of a sheet-metal-forming press, the parts being produced, their cadence, and the energy demand for each unit produced. For this purpose, only electrical measurements are used. The proposed analysis is conducted at the level of the press subsystems: main motor, transfer module, cushion, and auxiliary systems, and is intended to count, classify, and monitor the production of pressed parts. The power data are collected every 20 ms and show cyclic behavior, which is the basis for the presented methodology. A neural network (NN) based on heuristic rules is developed to estimate the press states. Then, the production period is determined from the power data using a least squares method to obtain normalized harmonic coefficients. These are the basis for a second NN dedicated to identifying the parts in production. The global error in estimating the parts being produced is under 1%. The resulting information could be handy in determining relevant information regarding the press behavior, such as energy per part, which is necessary in order to evaluate the energy performance of the press under different production conditions.Xunta de Galicia | Ref. IN854A 2020/0

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

Lockdown impacts on residential electricity demand in India: A data-driven and non-intrusive load monitoring study using Gaussian mixture models.

This study evaluates the effect of complete nationwide lockdown in 2020 on residential electricity demand across 13 Indian cities and the role of digitalisation using a public smart meter dataset. We undertake a data-driven approach to explore the energy impacts of work-from-home norms across five dwelling typologies. Our methodology includes climate correction, dimensionality reduction and machine learning-based clustering using Gaussian Mixture Models of daily load curves. Results show that during the lockdown, maximum daily peak demand increased by 150-200% as compared to 2018 and 2019 levels for one room-units (RM1), one bedroom-units (BR1) and two bedroom-units (BR2) which are typical for low- and middle-income families. While the upper-middle- and higher-income dwelling units (i.e., three (3BR) and more-than-three bedroom-units (M3BR)) saw night-time demand rise by almost 44% in 2020, as compared to 2018 and 2019 levels. Our results also showed that new peak demand emerged for the lockdown period for RM1, BR1 and BR2 dwelling typologies. We found that the lack of supporting socioeconomic and climatic data can restrict a comprehensive analysis of demand shocks using similar public datasets, which informed policy implications for India's digitalisation. We further emphasised improving the data quality and reliability for effective data-centric policymaking

Nonintrusive Load Monitoring (NILM) Using a Deep Learning Model with a Transformer-Based Attention Mechanism and Temporal Pooling

Nonintrusive load monitoring (NILM) is an important technique for energy management and conservation. In this paper, a deep learning model based on an attention mechanism, temporal pooling, residual connections, and transformers is proposed. This article presents a novel approach for NILM to accurately discern energy consumption patterns of individual household appliances. The proposed method entails a sequence of layers, including encoders, transformers, attention, temporal pooling, and residual connections, offering a comprehensive solution for NILM while effectively capturing appliance-specific energy usage in a household. The proposed model was evaluated using UK-DALE, REDD, and REFIT datasets in both seen and unseen cases. It shows that the proposed model in this paper performs better than other methods stated in other papers in terms of F1-score and total error of the results (in terms of SAE). This model achieved an F1-score equal to 92.96 as well as a total SAE equal to −0.036, which shows its effectiveness in accurately diagnosing and estimating the energy consumption of individual home appliances. The findings of this research show that the proposed model can be a tool for energy management in residential and commercial buildings

Evaluating Human Activity and Usage Patterns of Appliances with Smart Meters

2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA), 22-24 June 2022, Messina, Italy.Population ageing is becoming a key issue for most western countries, due to the challenges that it poses to the sustainability of future healthcare systems. In this context, many proposals and development are emerging trying to enhance the independent living of elderly and cognitive impaired people at their own homes. For that purpose, the massive deployment of smart meter at houses and buildings, initially focused on improving the energy management, has become a useful tool to provide the society with a variety of services and applications that can be employed for independent living. This work proposes the use of a commercial smart meter that delivers the disaggregated consumption per appliance every hour. This device has been installed on a test house during a training period of two months, in order to infer the behavior routines in the usage of the microwave. After the training, every new day can be compared to the obtained usage pattern of that appliance, in order to launch a notification when the day routine significantly differs. Similarly, since the use of the microwave is related to cooking, activities such as breakfast, lunch or dinner, may also be monitored and/or compared to a trained pattern. The proposal has been validated preliminary with experimental data coming from the aforementioned household.Agencia Estatal de InvestigaciónUniversidad de Alcal