Identification of the electrical load by C-means from non-intrusive monitoring of electrical signals in non-residential buildings

Producción CientíficaLa acción combinada de diferentes equipos conectados a una instalación eléctrica es capaz de provocar cambios inesperados en el tipo de carga dentro de la instalación; estas variaciones de carga son responsables de algunas fallas eléctricas. En este artículo se presenta una metodología para clasificar e identificar los tipos de carga en entornos industriales. Las cantidades de energía (EPQ) y los valores actuales se utilizan para establecer índices con el fin de utilizarlos como características para un algoritmo C-means y realizar la clasificación de carga. La experimentación se realiza en un centro de salud recogiendo datos eléctricos en diferentes tableros de distribución eléctrica. Los resultados obtenidos del método de clasificación muestran variaciones en el comportamiento de la carga a lo largo del día. Además, algunas clases se pueden utilizar para reconocer equipos en la instalación eléctrica para su posterior inspección o detección de fallas

Appliance Classification and Scheduling in Residential Environments with Limited Data and Reduced Intrusiveness

The United Kingdom aims for a 78% reduction in greenhouse gas emissions by 2035, with a specific carbon budget for 2033–2037. Despite rising CO2 emissions from 2021 to 2022 due to increased energy demands, this thesis presents novel strategies to reduce residential electricity consumption, a major emissions driver. It addresses two critical gaps in energy management: First, it develops a feature extraction methodology using machine learning and deep learning for accurately classifying high-power household appliances with smart meter data. Traditional methods often require complex setups or large datasets, leading to intrusiveness and implementation challenges. This research introduces the Spectral Entropy – Instantaneous Frequency (SE-IF) method, effective with limited datasets and enhancing usability (Chapter 3). Second, it proposes an optimisation model that intelligently schedules household appliance usage to balance costs, emissions, and user comfort, incorporating renewable energy and battery storage systems. Existing scheduling techniques typically overlook significant CO2 reductions and user comfort. The thesis utilises the Multiobjective Immune Algorithm (MOIA) to demonstrate this model’s effectiveness, achieving a 9.67% cost reduction and a 16.58% decrease in emissions (Chapter 5). Chapters 4 and 5 further detail how the SE-IF method, paired with a Bidirectional Long Short-Term Memory (BiLSTM) network, achieves a 94% accuracy in identifying appliances from aggregated data and applies the multi-objective optimisation in various scenarios. This research advances the integration of energy efficiency, environmental sustainability, and user-centric solutions in smart homes, contributing significantly to national goals of reducing energy consumption and emissions

Investigating the potential for a user-driven electricity monitoring application to provide useful electricity consumption patterns

Conventional electricity usage monitoring involves complex data collection via costly and intrusive hardware installation. There is a perceived need for a simple and affordable tool that provides users with feedback about their electricity consumption without the hardware installation. This study involves the design and development of a user driven mobile and desktop application that provides users with information on electricity usage patterns and historical trends. The application was designed using Ionic Framework, a tool ideal for the design of hybrid applications that are compatible with both desktop Windows devices and mobile Android devices. The goal of the research will be that the user will track their appliance usage on the application whilst taking electricity meter readings at regular intervals to calculate appliance-specific consumption. The data is added to the mobile or desktop application, which then provides users with a comprehensive display of the electricity usage patterns and trends. The objective is to provide users with the information required so that they can start understanding their electricity consumption better and it is a first step towards empowering the user to make smart decisions at home that will reduce their electricity consumption. The USE (Usefulness, Satisfaction, Ease of Use and Ease of Learning) questionnaire was used to gather user experience feedback from participants about user experience. The USE questionnaire tests the perceived Usefulness, Satisfaction, Ease of Use and Ease of Learnability The 31 individuals who initially volunteered to take part in the study are all residents of the City of Cape Town Municipality, aged between 20 and 80 years old. Not all participants are home owners; some are tenants in their premises. The sample group was selected on a convenience basis, and social media group posts were also used to reach individuals with a potential interest in the study. The two motivating factors that were considered to identify individuals who could potentially have an interest in the study were cost saving and environmental impact. 21 volunteers completed the study and returned the USE questionnaire. The study findings showed that all participants believe that using the application helped them to better understand their electricity consumption

Progress in ambient assisted systems for independent living by the elderly

One of the challenges of the ageing population in many countries is the efficient delivery of health and care services, which is further complicated by the increase in neurological conditions among the elderly due to rising life expectancy. Personal care of the elderly is of concern to their relatives, in case they are alone in their homes and unforeseen circumstances occur, affecting their wellbeing. The alternative; i.e. care in nursing homes or hospitals is costly and increases further if specialized care is mobilized to patients’ place of residence. Enabling technologies for independent living by the elderly such as the ambient assisted living systems (AALS) are seen as essential to enhancing care in a cost-effective manner. In light of significant advances in telecommunication, computing and sensor miniaturization, as well as the ubiquity of mobile and connected devices embodying the concept of the Internet of Things (IoT), end-to-end solutions for ambient assisted living have become a reality. The premise of such applications is the continuous and most often real-time monitoring of the environment and occupant behavior using an event-driven intelligent system, thereby providing a facility for monitoring and assessment, and triggering assistance as and when needed. As a growing area of research, it is essential to investigate the approaches for developing AALS in literature to identify current practices and directions for future research. This paper is, therefore, aimed at a comprehensive and critical review of the frameworks and sensor systems used in various ambient assisted living systems, as well as their objectives and relationships with care and clinical systems. Findings from our work suggest that most frameworks focused on activity monitoring for assessing immediate risks while the opportunities for integrating environmental factors for analytics and decision-making, in particular for the long-term care were often overlooked. The potential for wearable devices and sensors, as well as distributed storage and access (e.g. cloud) are yet to be fully appreciated. There is a distinct lack of strong supporting clinical evidence from the implemented technologies. Socio-cultural aspects such as divergence among groups, acceptability and usability of AALS were also overlooked. Future systems need to look into the issues of privacy and cyber security

Hardware for recognition of human activities: a review of smart home and AAL related technologies

Activity recognition (AR) from an applied perspective of ambient assisted living (AAL) and smart homes (SH) has become a subject of great interest. Promising a better quality of life, AR applied in contexts such as health, security, and energy consumption can lead to solutions capable of reaching even the people most in need. This study was strongly motivated because levels of development, deployment, and technology of AR solutions transferred to society and industry are based on software development, but also depend on the hardware devices used. The current paper identifies contributions to hardware uses for activity recognition through a scientific literature review in the Web of Science (WoS) database. This work found four dominant groups of technologies used for AR in SH and AAL—smartphones, wearables, video, and electronic components—and two emerging technologies: Wi-Fi and assistive robots. Many of these technologies overlap across many research works. Through bibliometric networks analysis, the present review identified some gaps and new potential combinations of technologies for advances in this emerging worldwide field and their uses. The review also relates the use of these six technologies in health conditions, health care, emotion recognition, occupancy, mobility, posture recognition, localization, fall detection, and generic activity recognition applications. The above can serve as a road map that allows readers to execute approachable projects and deploy applications in different socioeconomic contexts, and the possibility to establish networks with the community involved in this topic. This analysis shows that the research field in activity recognition accepts that specific goals cannot be achieved using one single hardware technology, but can be using joint solutions, this paper shows how such technology works in this regard

Designing Artificial Neural Networks (ANNs) for Electrical Appliance Classification in Smart Energy Distribution Systems

En este proyecto se abordará el problema de la desagregación del consumo eléctrico a través del diseño de sistemas inteligentes, basados en redes neuronales profundas, que puedan formar parte de sistemas más amplios de gestión y distribución de energía. Durante la definición estará presente la búsqueda de una complejidad computacional adecuada que permita una implementación posterior de bajo costo. En concreto, estos sistemas realizarán el proceso de clasificación a partir de los cambios en la corriente eléctrica provocados por los distintos electrodomésticos. Para la evaluación y comparación de las diferentes propuestas se hará uso de la base de datos BLUED.This project will address the energy consumption disaggregation problem through the design of intelligent systems, based on deep artificial neural networks, which would be part of broader energy management and distribution systems. The search for adequate computational complexity that will allow a subsequent implementation of low cost will be present during algorithm definition. Specifically, these systems will carry out the classification process based on the changes caused by the different appliances in the electric current. For the evaluation and comparison of the different proposals, the BLUED database will be used.Máster Universitario en Ingeniería Industrial (M141