NILM techniques for intelligent home energy management and ambient assisted living: a review

The ongoing deployment of smart meters and different commercial devices has made electricity disaggregation feasible in buildings and households, based on a single measure of the current and, sometimes, of the voltage. Energy disaggregation is intended to separate the total power consumption into specific appliance loads, which can be achieved by applying Non-Intrusive Load Monitoring (NILM) techniques with a minimum invasion of privacy. NILM techniques are becoming more and more widespread in recent years, as a consequence of the interest companies and consumers have in efficient energy consumption and management. This work presents a detailed review of NILM methods, focusing particularly on recent proposals and their applications, particularly in the areas of Home Energy Management Systems (HEMS) and Ambient Assisted Living (AAL), where the ability to determine the on/off status of certain devices can provide key information for making further decisions. As well as complementing previous reviews on the NILM field and providing a discussion of the applications of NILM in HEMS and AAL, this paper provides guidelines for future research in these topics.Agência financiadora: Programa Operacional Portugal 2020 and Programa Operacional Regional do Algarve 01/SAICT/2018/39578 Fundação para a Ciência e Tecnologia through IDMEC, under LAETA: SFRH/BSAB/142998/2018 SFRH/BSAB/142997/2018 UID/EMS/50022/2019 Junta de Comunidades de Castilla-La-Mancha, Spain: SBPLY/17/180501/000392 Spanish Ministry of Economy, Industry and Competitiveness (SOC-PLC project): TEC2015-64835-C3-2-R MINECO/FEDERinfo:eu-repo/semantics/publishedVersio

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

Towards sustainable energy-efficient communities based on a scheduling algorithm

The Internet of Things (IoT) and Demand Response (DR) combined have transformed the way Information and Communication Technologies (ICT) contribute to saving energy and reducing costs, while also giving consumers more control over their energy footprint. Unlike current price and incentive based DR strategies, we propose a DR model that promotes consumers reaching coordinated behaviour towards more sustainable (and green) communities. A cooperative DR system is designed not only to bolster energy efficiency management at both home and district levels, but also to integrate the renewable energy resource information into the community's energy management. Initially conceived in a centralised way, a data collector called the "aggregator" will handle the operation scheduling requirements given the consumers' time preferences and the available electricity supply from renewables. Evaluation on the algorithm implementation shows feasible computational cost (CC) in different scenarios of households, communities and consumer behaviour. Number of appliances and timeframe flexibility have the greatest impact on the reallocation cost. A discussion on the communication, security and hardware platforms is included prior to future pilot deployment.Comunidad de Madri

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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Internet of Things (IoT) and the road to happiness

Purpose: This study aims to provide a systematic review about the Internet of Things (IoT) and its impacts on happiness. It intends to serve as a platform for further research as it is sparse in in-depth analysis. Design/methodology/approach: This systematic review initially observed 2,501 literary articles through the ScienceDirect and WorldCat search engines before narrowing it down to 72 articles based on subject matter relevance in the abstract and keywords. Accounting for duplicates between search engines, the count was reduced to 66 articles. To finally narrow down all the literature used in this systematic review, 66 articles were given a critical readthrough. The count was finally reduced to 53 total articles used in this systematic review. Findings: This paper necessitates the claim that IoT will likely impact many aspects of our everyday lives. Through the literature observed, it was found that IoT will have some significant and positive impacts on people's welfare and lives. The unprecedented nature of IoTs impacts on society should warrant further research moving forward. Research limitations/implications: While the literature presented in this systematic review shows that IoT can positively impact the perceived or explicit happiness of people, the amount of literature found to supplement this argument is still on the lower end. They also necessitate the need for both greater depth and variety in this field of research. Practical implications: Since technology is already a pervasive element of most people’s contemporary lives, it stands to reason that the most important factors to consider will be in how we might benefit from IoT or, more notably, how IoT can enhance our levels of happiness. A significant implication is its ability to reduce the gap in happiness levels between urban and rural areas. Originality/value: Currently, the literature directly tackling the quantification of IoTs perceived influence on happiness has yet to be truly discussed broadly. This systematic review serves as a starting point for further discussion in the subject matter. In addition, this paper may lead to a better understanding of the IoT technology and how we can best advance and adapt it to the benefits of the society

Short Term Probabilistic Load Forecasting at Local Level in Distribution Networks

Along with the growing inclusion of smart technologies into the electrical power grids, benefits, which can be originated form advanced metering infrastructure (AMI), have grabbed noticeable attention from distribution utilities. Since the number of meters are severely ample in practical systems, the utilities now is able to create virtual meter data by aggregating loads for distribution substations, feeders, transformers, or regions with the help of geographic information system. Such an important change brought by smart meter rollout is considered as the main factor which motivates this thesis to delve more into the load pattern modeling and forecasting at local level and find approaches which can yield to the enhanced applications in distribution networks. However, low aggregation level leads to high volatile load characteristic. In this regard, this thesis proposes a comprehensive methodology for uncertainty modeling and short-term probabilistic load forecasting (STPLF) in distribution networks. Existing methods related to uncertainty modeling and forecasting are rarely applied to local level loads and they suffer from over- or under-fitting of data when there is a misfit between the complexity of the model and the amount of data available. These models are limited to specific situations due to the great diversity of loads in distribution networks and need to be tuned every time when the load aggregation level changes. They also need a relatively large data set to support the recovery of the predictive densities. Our proposed method addresses this issue and is based on Bayesian nonparametric model which has unbounded complexity and allow the complexity to automatically grow and be inferred from the observed data. The uncertainty underlying load patterns can be endowed with any type of prior distribution and is given in a nonparametric form, i.e. a mixture model with countably infinite number of mixtures, inferred from the posterior using the Gibbs Sampling, which is a Markov Chain Monte Carlo (MCMC) technique. All effective samples from the sampling procedure along with the exogenous variables are fed to an ensemble learning machine. The final result of the probabilistic load forecasting (PLF) is averaged on the outputs of all learning models, thus reducing the model variance and enhancing the model consistency. The proposed method is tested on both a public data set and a local data set from the Saskatoon Light &Power AMI Meter Replacement Program which offers electricity consumption at a granularity of 30 minutes of more than 65,000 electricity customers including industrial, commercial and residential sectors in the city of Saskatoon, Canada

Demand response performance and uncertainty: A systematic literature review

The present review has been carried out, resorting to the PRISMA methodology, analyzing 218 published articles. A comprehensive analysis has been conducted regarding the consumer's role in the energy market. Moreover, the methods used to address demand response uncertainty and the strategies used to enhance performance and motivate participation have been reviewed. The authors find that participants will be willing to change their consumption pattern and behavior given that they have a complete awareness of the market environment, seeking the optimal decision. The authors also find that a contextual solution, giving the right signals according to the different behaviors and to the different types of participants in the DR event, can improve the performance of consumers' participation, providing a reliable response. DR is a mean of demand-side management, so both these concepts are addressed in the present paper. Finally, the pathways for future research are discussed.This article is a result of the project RETINA (NORTE-01-0145- FEDER-000062), supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). We also acknowledge the work facilities and equipment provided by GECAD research center (UIDB/00760/2020) to the project team, and grants CEECIND/02887/2017 and SFRH/BD/144200/2019.info:eu-repo/semantics/publishedVersio

Internet of Things (IoT) and the road to happiness

Purpose – This study aims to provide a systematic review about the Internet of Things (IoT) and its impacts on happiness. It intends to serve as a platform for further research as it is sparse in in-depth analysis. Design/methodology/approach – This systematic review initially observed 2,501 literary articles through the ScienceDirect and WorldCat search engines before narrowing it down to 72 articles based on subject matter relevance in the abstract and keywords. Accounting for duplicates between search engines, the count was reduced to 66 articles. To finally narrow down all the literature used in this systematic review, 66 articles were given a critical readthrough. The count was finally reduced to 53 total articles used in this systematic review. Findings – This paper necessitates the claim that IoT will likely impact many aspects of our everyday lives. Through the literature observed, it was found that IoT will have some significant and positive impacts on people's welfare and lives. The unprecedented nature of IoTs impacts on society should warrant further research moving forward. Research limitations/implications – While the literature presented in this systematic review shows that IoT can positively impact the perceived or explicit happiness of people, the amount of literature found to supplement this argument is still on the lower end. They also necessitate the need for both greater depth and variety in this field of research. Practical implications – Since technology is already a pervasive element of most people’s contemporary lives, it stands to reason that the most important factors to consider will be in how we might benefit from IoT or, more notably, how IoT can enhance our levels of happiness. A significant implication is its ability to reduce the gap in happiness levels between urban and rural areas. Originality/value – Currently, the literature directly tackling the quantification of IoTs perceived influence on happiness has yet to be truly discussed broadly. This systematic review serves as a starting point for further discussion in the subject matter. In addition, this paper may lead to a better understanding of the IoT technology and how we can best advance and adapt it to the benefits of the society

Effective demand response gathering and deployment in smart grids for intensive renewable integration using aggregation and machine learning

Tesis por compendio de publicaciones.[EN] Distributed generation, namely renewables-based technologies, have emerged as a crucial component in the transition to mitigate the effects of climate change, providing a decentralized approach to electricity production. However, the volatile behavior of distributed generation has created new challenges in maintaining system balance and reliability. In this context, the demand response concept and corresponding programs arise giving the local energy communities prominence. In demand response concept, it is expected an empowerment of the consumer in the electricity sector. This has a significant impact on grid operations and brings complex interactions due to the volatile behavior, privacy concerns, and lack of consumer knowledge in the energy market context. For this, aggregators play a crucial role addressing these challenges. It is crucial to develop tools that allow the aggregators helping consumers to make informed decisions, maximize the benefits of their flexibility resources, and contribute to the overall success of grid operations. This thesis, through innovative solutions and resorting to artificial intelligence models, addresses the integration of renewables, promoting fair participation among all demand response providers. The thesis ultimately results in an innovative decision support system - MAESTRO, the Machine learning Assisted Energy System management Tool for Renewable integration using demand respOnse. MAESTRO is composed by a set of diversified models that together contribute for handling the complexity of managing energy communities with distributed generation resources, demand response providers, energy storage systems and electric vehicles. This PhD thesis comprises a comprehensive analysis of state-of-the-art techniques, system design and development, experimental results, and key findings. In this research were published twenty-six scientific papers, in both international journals and conference proceedings. Contributions to international projects and Portuguese projects was accomplished. [ES] La generación distribuida, en particular las tecnologías basadas en energías renovables, se ha convertido en un componente crucial en la transición para mitigar los efectos del cambio climático, al proporcionar un enfoque descentralizado para la producción de electricidad. Sin embargo, el comportamiento volátil de la generación distribuida ha generado nuevos desafíos para mantener el equilibrio y la confiabilidad del sistema. En este contexto, surge el concepto de respuesta de la demanda y los programas correspondientes, otorgando prominencia a las comunidades energéticas locales. En el concepto de "respuesta a la demanda" (DR por sus siglas en inglés), se espera un empoderamiento del consumidor en el sector eléctrico. Esto tiene un impacto significativo en la operación de la red y genera interacciones complejas debido al comportamiento volátil, las preocupaciones de privacidad y la falta de conocimiento del consumidor en el contexto del mercado energético. Para esto, los agregadores desempeñan un papel crucial al abordar estos desafíos. Es fundamental desarrollar herramientas que permitan a los agregadores ayudar a los consumidores a tomar decisiones informadas, maximizar los beneficios de sus recursos de flexibilidad y contribuir al éxito general de las operaciones de la red. Esta tesis, a través de soluciones innovadoras y utilizando modelos de inteligencia artificial, aborda la integración de energías renovables, promoviendo una participación justa entre todos los proveedores de respuesta de la demanda. La tesis resulta en última instancia en un sistema de apoyo a la toma de decisiones innovador: MAESTRO, Machine learning Assisted Energy System management Tool for Renewable integration using demand respOnse. MAESTRO está compuesto por un conjunto de modelos diversificados que contribuyen juntos para manejar la complejidad de la gestión de comunidades energéticas con recursos de generación distribuida, proveedores de respuesta de la demanda, sistemas de almacenamiento de energía y vehículos eléctricos. Esta tesis de doctorado comprende un análisis exhaustivo de las técnicas de vanguardia, el diseño y desarrollo del sistema, los resultados experimentales y los hallazgos clave. En esta investigación se publicaron veintiséis artículos científicos, tanto en revistas internacionales como en actas de conferencias. Se lograron contribuciones a proyectos internacionales y proyectos portugueses. [POR] A produção distribuída, nomeadamente as tecnologias baseadas em energias renováveis, emergiram como um componente crucial na transição para mitigar os efeitos das alterações climáticas, proporcionando uma abordagem descentralizada à produção de eletricidade. No entanto, o comportamento volátil da geração distribuída criou desafios na manutenção do equilíbrio e da fiabilidade do sistema. Nesse contexto, surge o conceito de resposta à procura e os programas correspondentes, conferindo proeminência às comunidades energéticas locais. No conceito de resposta à procura, espera-se um empoderamento do consumidor no setor elétrico. Isso tem um impacto significativo nas operações da rede e gera interações complexas devido ao comportamento volátil, preocupações com a privacidade e falta de conhecimento dos consumidores no contexto do mercado energético. Para isso, os agregadores desempenham um papel crucial ao lidar com esses desafios. É fundamental desenvolver ferramentas que permitam aos agregadores ajudar os consumidores a tomar decisões informadas, maximizar os benefícios de seus recursos de flexibilidade e contribuir para o sucesso global das operações da rede. Esta tese de doutoramento, através de soluções inovadoras e recorrendo a modelos de inteligência artificial, aborda a integração de energias renováveis, promovendo uma participação justa entre todos os fornecedores de resposta à procura. A tese resulta, em última instância, num sistema inovador de apoio à tomada de decisões - MAESTRO, Machine learning Assisted Energy System management Tool for Renewable integration using demand respOnse. A ferramenta MAESTRO é composta por um conjunto de modelos diversificados que, em conjunto, contribuem para lidar com a complexidade da gestão de comunidades energéticas com recursos de geração distribuída, fornecedores de resposta à procura, sistemas de armazenamento de energia e veículos elétricos. Esta tese de doutoramento abrange uma análise abrangente de técnicas de ponta, design e desenvolvimento do sistema, resultados experimentais e descobertas-chave. Nesta pesquisa, foram publicados vinte e seis artigos científicos, tanto em revistas internacionais como em atas de conferências. Foram realizadas contribuições para projetos internacionais e projetos portugueses