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

A Distributed Demand-Side Management Framework for the Smart Grid

This paper proposes a fully distributed Demand-Side Management system for Smart Grid infrastructures, especially tailored to reduce the peak demand of residential users. In particular, we use a dynamic pricing strategy, where energy tariffs are function of the overall power demand of customers. We consider two practical cases: (1) a fully distributed approach, where each appliance decides autonomously its own scheduling, and (2) a hybrid approach, where each user must schedule all his appliances. We analyze numerically these two approaches, showing that they are characterized practically by the same performance level in all the considered grid scenarios. We model the proposed system using a non-cooperative game theoretical approach, and demonstrate that our game is a generalized ordinal potential one under general conditions. Furthermore, we propose a simple yet effective best response strategy that is proved to converge in a few steps to a pure Nash Equilibrium, thus demonstrating the robustness of the power scheduling plan obtained without any central coordination of the operator or the customers. Numerical results, obtained using real load profiles and appliance models, show that the system-wide peak absorption achieved in a completely distributed fashion can be reduced up to 55%, thus decreasing the capital expenditure (CAPEX) necessary to meet the growing energy demand

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

Football fans in training: the development and optimization of an intervention delivered through professional sports clubs to help men lose weight, become more active and adopt healthier eating habits

<p>Background: The prevalence of obesity in men is rising, but they are less likely than women to engage in existing weight management programmes. The potential of professional sports club settings to engage men in health promotion activities is being increasingly recognised. This paper describes the development and optimization of the Football Fans in Training (FFIT) programme, which aims to help overweight men (many of them football supporters) lose weight through becoming more active and adopting healthier eating habits.</p> <p>Methods: The MRC Framework for the design and evaluation of complex interventions was used to guide programme development in two phases. In Phase 1, a multidisciplinary working group developed the pilot programme (p-FFIT) and used a scoping review to summarize previous research and identify the target population. Phase 2 involved a process evaluation of p-FFIT in 11 Scottish Premier League (SPL) clubs. Participant and coach feedback, focus group discussions and interviews explored the utility/acceptability of programme components and suggestions for changes. Programme session observations identified examples of good practice and problems/issues with delivery. Together, these findings informed redevelopment of the optimized programme (FFIT), whose components were mapped onto specific behaviour change techniques using an evidence-based taxonomy.</p> <p>Results: p-FFIT comprised 12, weekly, gender-sensitised, group-based weight management classroom and ‘pitch-side’ physical activity sessions. These in-stadia sessions were complemented by an incremental, pedometer-based walking programme. p-FFIT was targeted at men aged 35-65 years with body mass index ≥ 27 kg/m2. Phase 2 demonstrated that participants in p-FFIT were enthusiastic about both the classroom and physical activity components, and valued the camaraderie and peer-support offered by the programme. Coaches appreciated the simplicity of the key healthy eating and physical activity messages. Suggestions for improvements that were incorporated into the optimized FFIT programme included: more varied in-stadia physical activity with football-related components; post-programme weight management support (emails and a reunion session); and additional training for coaches in SMART goal setting and the pedometer-based walking programme.</p> <p>Conclusions: The Football Fans in Training programme is highly acceptable to participants and SPL coaches, and is appropriate for evaluation in a randomised controlled trial.</p&gt

Open-Source, Open-Architecture SoftwarePlatform for Plug-InElectric Vehicle SmartCharging in California

This interdisciplinary eXtensible Building Operating System–Vehicles project focuses on controlling plug-in electric vehicle charging at residential and small commercial settings using a novel and flexible open-source, open-architecture charge communication and control platform. The platform provides smart charging functionalities and benefits to the utility, homes, and businesses.This project investigates four important areas of vehicle-grid integration research, integrating technical as well as social and behavioral dimensions: smart charging user needs assessment, advanced load control platform development and testing, smart charging impacts, benefits to the power grid, and smart charging ratepayer benefits