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

Development of an anomaly alert system triggered by unusual behaviors at home

In many countries, the number of elderly people has grown due to the increase in the life expectancy of the population, many of whom currently live alone and are prone to having accidents that they cannot report, especially if they are immobilized. For this reason, we have developed a non-intrusive IoT device, which, through multiple integrated sensors, collects information on habitual user behavior patterns and uses it to generate unusual behavior rules. These rules are used by our SecurHome system to send alert messages to the dependent person's family members or caregivers if their behavior changes abruptly over the course of their daily life. This document describes in detail the design and development of the SecurHome system.SecurHome is a multidisciplinary research project on ageing in the framework of the International Centre on Ageing (CENIE). It is a project evaluated by the Spanish State Agency for Research and co-financed by the European Regional Development Fund in the framework of the Interreg V-A Spain–Portugal Cooperation Programme (POCTEP) 2014–2020

Exploring The Responsibilities Of Single-Inhabitant Smart Homes With Use Cases

DOI: 10.3233/AIS-2010-0076This paper makes a number of contributions to the field of requirements analysis for Smart Homes. It introduces Use Cases as a tool for exploring the responsibilities of Smart Homes and it proposes a modification of the conventional Use Case structure to suit the particular requirements of Smart Homes. It presents a taxonomy of Smart-Home-related Use Cases with seven categories. It draws on those Use Cases as raw material for developing questions and conclusions about the design of Smart Homes for single elderly inhabitants, and it introduces the SHMUC repository, a web-based repository of Use Cases related to Smart Homes that anyone can exploit and to which anyone may contribute

Advances in Technologies and Methods for Behavior, Emotion, and Health Monitoring in Pets

This research offers a detailed descriptions of existing technologies and approaches for monitoring pets in the areas of behavior, emotion, and health. The first section discusses behavior and emotion monitoring. It includes wearable devices like smart collars that are fitted with sensors for monitoring heart rate, activity levels, and temperature. These devices communicate with AI-based anomaly detection systems that send real-time alerts through various channels such as SMS, email, and mobile app notifications. Additionally, smart cameras and sound capturing devices are employed to analyze behavior and emotional states. The second section discusses health monitoring and assistance. Users can input data such as pet breed, age, and observed behaviors into dashboards. Subsequent AI algorithms analyze the data, providing health forecasts and preventive measures. Moreover, imaging technologies employ image acquisition, preprocessing, and feature extraction to detect abnormalities, the results of which are stored in databases and can trigger alerts to medical staff. The review identifies distinct modules for each sector, including data capture, processing, and alerting mechanisms. While each module specializes in specific tasks, common functionalities such as real-time alerting and data storage are pervasive across both sectors. The study asserts that current technological advancements have significantly enhanced the ability to monitor pets in real-time, providing actionable insights for pet owners and veterinary professionals