GREEND: An Energy Consumption Dataset of Households in Italy and Austria

Home energy management systems can be used to monitor and optimize consumption and local production from renewable energy. To assess solutions before their deployment, researchers and designers of those systems demand for energy consumption datasets. In this paper, we present the GREEND dataset, containing detailed power usage information obtained through a measurement campaign in households in Austria and Italy. We provide a description of consumption scenarios and discuss design choices for the sensing infrastructure. Finally, we benchmark the dataset with state-of-the-art techniques in load disaggregation, occupancy detection and appliance usage mining

Artificial Intelligence based Anomaly Detection of Energy Consumption in Buildings: A Review, Current Trends and New Perspectives

Enormous amounts of data are being produced everyday by sub-meters and smart sensors installed in residential buildings. If leveraged properly, that data could assist end-users, energy producers and utility companies in detecting anomalous power consumption and understanding the causes of each anomaly. Therefore, anomaly detection could stop a minor problem becoming overwhelming. Moreover, it will aid in better decision-making to reduce wasted energy and promote sustainable and energy efficient behavior. In this regard, this paper is an in-depth review of existing anomaly detection frameworks for building energy consumption based on artificial intelligence. Specifically, an extensive survey is presented, in which a comprehensive taxonomy is introduced to classify existing algorithms based on different modules and parameters adopted, such as machine learning algorithms, feature extraction approaches, anomaly detection levels, computing platforms and application scenarios. To the best of the authors' knowledge, this is the first review article that discusses anomaly detection in building energy consumption. Moving forward, important findings along with domain-specific problems, difficulties and challenges that remain unresolved are thoroughly discussed, including the absence of: (i) precise definitions of anomalous power consumption, (ii) annotated datasets, (iii) unified metrics to assess the performance of existing solutions, (iv) platforms for reproducibility and (v) privacy-preservation. Following, insights about current research trends are discussed to widen the applications and effectiveness of the anomaly detection technology before deriving future directions attracting significant attention. This article serves as a comprehensive reference to understand the current technological progress in anomaly detection of energy consumption based on artificial intelligence.Comment: 11 Figures, 3 Table

Occupancy monitoring and prediction in ambient intelligent environment

Occupancy monitoring and prediction as an influential factor in the extraction of occupants' behavioural patterns for the realisation of ambient intelligent environments is addressed in this research. The proposed occupancy monitoring technique uses occupancy detection sensors with unobtrusive features to monitor occupancy in the environment. Initially the occupancy detection is conducted for a purely single-occupant environment. Then, it is extended to the multipleoccupant environment and associated problems are investigated. Along with the occupancy monitoring, it is aimed to supply prediction techniques with a suitable occupancy signal as the input which can enhance efforts in developing ambient intelligent environments. By predicting the occupancy pattern of monitored occupants, safety, security, the convenience of occupants, and energy saving can be improved. Elderly care and supporting people with health problems like dementia and Alzheimer disease are amongst the applications of such an environment. In the research, environments are considered in different scenarios based on the complexity of the problem including single-occupant and multiple-occupant scenarios. Using simple sensory devices instead of visual equipment without any impact on privacy and her/his normal daily activity, an occupant is monitored in a living or working environment in the single-occupant scenario. ZigBee wireless communication technology is used to collect signals from sensory devices such as motion detection sensors and door contact sensors. All these technologies together including sensors, wireless communication, and tagging are integrated as a wireless sensory agent

A review of smart homes in healthcare

The technology of Smart Homes (SH), as an instance of ambient assisted living technologies, is designed to assist the homes’ residents accomplishing their daily-living activities and thus having a better quality of life while preserving their privacy. A SH system is usually equipped with a collection of inter-related software and hardware components to monitor the living space by capturing the behaviour of the resident and understanding his activities. By doing so the system can inform about risky situations and take actions on behalf of the resident to his satisfaction. The present survey will address technologies and analysis methods and bring examples of the state of the art research studies in order to provide background for the research community. In particular, the survey will expose infrastructure technologies such as sensors and communication platforms along with artificial intelligence techniques used for modeling and recognizing activities. A brief overview of approaches used to develop Human–Computer interfaces for SH systems is given. The survey also highlights the challenges and research trends in this area

A User-Centred Methodology to Design and Simulate Smart Home Environments and Related Services

openI progressi nelle tecnologie di automazione e comunicazione all'interno degli edifici residenziali offrono molti interessanti vantaggi per lo sviluppo delle Smart Home, come l'aumento di efficienza energetica, il miglioramento il comfort per gli abitanti e la riduzione dei costi operativi per il proprietario. L'aggregazione e la condivisione dei dati all'interno delle reti possono essere garantite dal moderno approccio denominato Internet delle cose (IoT) e supportati dalle nuove tecnologie dell'informazione e della comunicazione (ICT). Tali tecnologie si stanno evolvendo e le abitazioni stanno diventando luoghi tecnologici popolati da una moltitudine di dispositivi in grado di raccogliere una grande quantità di dati e di cooperare in modo intelligente per controllare tutti i dispositivi connessi, come gli elettrodomestici, l'illuminazione, i sistemi di riscaldamento, ecc. Da un lato, l’intelligenza crescente dei dispositivi connessi produce una grande quantità di dati; dall'altro lato, la complessità di tali dati crea difficoltà di classificazione, trasmissione ed interpretazione delle informazioni utili. Entrambi gli aspetti possono ridurre drasticamente i potenziali vantaggi e limitare la diffusione dei cosiddetti dispositivi “smart”. Mentre a livello aziendale già esistono soluzioni di automazione affermate ed ampiamente utilizzate, le applicazioni per le abitazioni private sono ancora di difficile diffusione a causa della mancanza di standard di comunicazione e della presenza di dispositivi e sistemi altamente eterogenei e quindi di difficile integrazione. Inoltre, anche quando la connessione tra due dispositivi viene stabilita, renderli interoperabili è un’altra grande sfida a causa delle differenze nelle modalità di funzionamento e della difficoltà di integrazione dell'interfaccia. Infatti, le Smart Home non consentono ancora una elevata interoperabilità e gli studi fatti sono spesso fortemente orientati alla tecnologia e concentrati sulle potenzialità dei singoli sottosistemi, trascurando i benefici per gli utenti finali. A tale scopo, questo lavoro definisce un modello di gestione delle informazioni per ambienti domestici intelligenti con lo scopo di supportare la progettazione e la simulazione dei dispositivi “smart” nonché dei servizi sviluppati. Tale modello considera diverse tipologie di dispositivi, le relazioni esistenti tra loro, i flussi informativi e le modalità di interazione dell’utente per modellare correttamente l'ambiente e definirne il comportamento. Il modello sviluppato supporta la progettazione della Smart Home ed è in grado di simulare le funzionalità dei dispositivi con lo scopo finale di valutare i benefici dei servizi forniti.The advances in home automation and communication technologies offer several attractive benefits for the modern smart home, such as increased energy efficiency, improved residential comfort and reduced operative costs for the homeowner. Data aggregation and sharing within the networks can be guaranteed by modern Internet of Things (IoT) approaches and supported by available Information and Communication Technologies (ICT) tools. Such technologies are evolving and the private houses are becoming technological places populated by a multitude of devices able to collect a huge quantity of data and to cooperate in an intelligent way to control different domains, from household appliances to lighting or heating and ventilation. On one hand, the rising intelligence of smart devices makes a large amount of data available; on the other hand, data complexity creates difficulties in classifying, transmitting and interpreting essential data. Both aspects may drastically reduce the potential advantages and limit the diffusion smart devices. While in building automation proven solutions already exist, tailored applications for private houses and integration among heterogeneous devices and systems are still challenging due to the lack of standards and the variety of adopted communication protocols and data model schemas. Furthermore, even when the device connection and consolidation are achieved, making them cooperate in an interoperable way is another big challenge due to differences in usage paradigms, operation modes and interface integration. In fact, Smart Homes still lack of high interoperability and researches are often strongly technology-oriented and focused on single sub-system potentialities neglecting the expected benefits for the final users. For this purpose, the presented research defines an information management model for the smart home environment to support design and simulation of its devices as well as the enabled services. Such a model considers different device typologies, their mutual relationships, the information flows and the user interaction modalities in order to properly model the environment and define its behavior. It supports the design of the smart home by simulating the devices’ functionalities and estimating the expected performances.INGEGNERIA MECCANICA E GESTIONALEopenCapitanelli, AndreaCapitanelli, Andre

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

Enhancing the efficiency of electricity utilization through home energy management systems within the smart grid framework

The concept behind smart grids is the aggregation of “intelligence” into the grid, whether through communication systems technologies that allow broadcast/data reception in real-time, or through monitoring and systems control in an autonomous way. With respect to the technological advancements, in recent years there has been a significant increment in devices and new strategies for the implementation of smart buildings/homes, due to the growing awareness of society in relation to environmental concerns and higher energy costs, so that energy efficiency improvements can provide real gains within modern society. In this perspective, the end-users are seen as active players with the ability to manage their energy resources, for example, microproduction units, domestic loads, electric vehicles and their participation in demand response events. This thesis is focused on identifying application areas where such technologies could bring benefits for their applicability, such as the case of wireless networks, considering the positive and negative points of each protocol available in the market. Moreover, this thesis provides an evaluation of dynamic prices of electricity and peak power, using as an example a system with electric vehicles and energy storage, supported by mixed-integer linear programming, within residential energy management. This thesis will also develop a power measuring prototype designed to process and determine the main electrical measurements and quantify the electrical load connected to a low voltage alternating current system. Finally, two cases studies are proposed regarding the application of model predictive control and thermal regulation for domestic applications with cooling requirements, allowing to minimize energy consumption, considering the restrictions of demand, load and acclimatization in the system