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

Energy consumption management in Smart Homes: An M-Bus communication system

Energy consumption management in Smart Home environments relies on the implementation of systems of cooperative intelligent objects named Smart Meters. In order for devices to cooperate to smart metering applications' execution, they need to make their information available. In this paper we propose a framework that aims at managing energy consumption of controllable appliances in groups of Smart Homes belonging to the same neighbourhood or condominium. We consider not only electric power distribution, but also alternative energy sources such as solar panels. We define a communication paradigm based on M-Bus for the acquisition of relevant data by managing nodes. We also provide a lightweight algorithm for the distribution of the available alternative power among houses. Performance evaluation of experiments in simulation mode prove that the proposed framework does not jeopardise the lifetime of Smart Meters, particularly in typical situations where managed devices do not continuously turn on and off

A decision support system for fostering smart energy efficient districts

The role of ICT is becoming prominent in tackling some of the urban societal challenges such as energy wastage and increasing carbon emissions. In this context, the concept of DAREED aims to deliver an integrated decision support system (DSS) to drive energy efficiency and low carbon activities at both a building and district level. The main aim of this paper is to present the technical concept of the Best Practices recommendation component of the DAREED system. This component seeks to compare and identify existing best practices to recommend practical actions to various stakeholders (e.g. building managers, citizens) in order to improve energy performance considering the global needs of a building. This paper also discusses the context of the three field trial sites (based in UK, Spain and Italy) in which the DAREED platform along with the best practices tool is to be tested and validated.This work evolved in the context of the project DAREED (Decision support Advisor for innovative business models and useR engagement for smart Energy Efficient Districts), www.dareed.eu, a project cofunded by the EC within FP7, Grant agreement no: 609082

Smart home energy management

The new challenges on Information and Communication Technologies (ICT) in Automatic Home Systems (AHS) focus on the methods useful to monitor, control, and optimize the data management flow and the use of energy. An AHS is a residential dwelling, in some cases with a garden or an outdoor space, equipped with sensors and actuators to collect data and send controls according to the activities and expectations of the occupants/users. Home automation provides a centralized or distributed control of electrical appliances. Adding intelligence to the home environment, it would be possible to obtain, not only excellent levels of comfort, but also energy savings both inside and outside the dwelling, for instance using smart solutions for the management of the external lights and of the garden

Activity Recognition and Prediction in Real Homes

In this paper, we present work in progress on activity recognition and prediction in real homes using either binary sensor data or depth video data. We present our field trial and set-up for collecting and storing the data, our methods, and our current results. We compare the accuracy of predicting the next binary sensor event using probabilistic methods and Long Short-Term Memory (LSTM) networks, include the time information to improve prediction accuracy, as well as predict both the next sensor event and its mean time of occurrence using one LSTM model. We investigate transfer learning between apartments and show that it is possible to pre-train the model with data from other apartments and achieve good accuracy in a new apartment straight away. In addition, we present preliminary results from activity recognition using low-resolution depth video data from seven apartments, and classify four activities - no movement, standing up, sitting down, and TV interaction - by using a relatively simple processing method where we apply an Infinite Impulse Response (IIR) filter to extract movements from the frames prior to feeding them to a convolutional LSTM network for the classification.Comment: 12 pages, Symposium of the Norwegian AI Society NAIS 201