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

Integrating Smart Ceiling Fans and Communicating Thermostats to Provide Energy-Efficient Comfort

The project goal was to identify and test the integration of smart ceiling fans and communicating thermostats. These highly efficient ceiling fans use as much power as an LED light bulb and have onboard temperature and occupancy sensors for automatic operationbased on space conditions. The Center for the Environment (CBE) at UC Berkeley led the research team including TRC, Association for Energy Affordability (AEA), and Big Ass Fans (BAF). The research team conducted laboratory tests, installed99 ceiling fans and 12 thermostats in four affordable multifamily housing sites in California’s Central Valley, interviewed stakeholders to develop a case study, developed an online design tool and design guide, outlined codes and standards outreach, and published several papers.The project team raised indoor cooling temperature setpoints and used ceiling fans as the first stage of cooling; this sequencing of ceiling fans and air conditioningreducesenergy consumption, especially during peak periods, while providing thermal comfort.The field demonstration resulted in 39% measured compressor energy savings during the April–October cooling seasoncompared to baseline conditions, normalized for floor area. Weather-normalized energy use varied from a 36% increase to 71% savings, withmedian savings of 15%.This variability reflects the diversity in buildings, mechanical systems, prior operation settings, space types, andoccupants’ schedules,preferences, and motivations. All commercial spaces with regular occupancy schedules (and twoof the irregularly-occupied commercial spaces and one of the homes) showed energy savings on an absolute basis before normalizing for warmer intervention temperatures,and 10 of 13 sites showed energy savings on a weather-normalized basis. The ceiling fans provided cooling for one site for months during hot weather when the coolingequipment failed.Occupants reported high satisfaction with the ceiling fans and improved thermal comfort. This technology can apply to new and retrofit residential and commercial buildings

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

Influence of Adaptive Comfort Models in Execution Cost Improvements for Housing Thermal Environment in Concepción, Chile

Most of the operational energy needed by the housing sector is used to compensate energy losses or thermal gains through the building’s envelope. As a result, any improvement in the thermal behavior will provide important opportunities to reduce energy consumption. This research analyzes improvements in the thermal envelope in social housing in the Greater Concepción area in Chile using adaptive thermal comfort models and thermal insulation investments. The objective set out is to evaluate the economic reduction of thermal envelope improvement costs for dwellings, which entails using the adaptive thermal comfort model obtained through monitoring and the surveys applied to the users of social housing in Concepción (CAS), against the international adaptive thermal comfort models established by the EN 15251:2007 and ASHRAE 55-2017 standards. Finally, it is concluded that, on having applied the social housing adaptive thermal comfort model (CAS), execution costs are reduced by between 28.8% and 58.2%, reaching a time of comfort in free oscillation similar to that obtained from applying the models of the EN 15251:2007 (74.2%) and ASHRAE 55-2017 standards (59.9%)CONICYT FONDECYT 3160806VI PPIT-U

Challenges in Energy Awareness: a Swedish case of heating consumption in households

An efficient and sustainable energy system is an important factor when minimising the environmental impact caused by the cities. We have worked with questions on how to construct a more direct connection between customers-­‐citizens and a provider of district heating for negotiating notions of comfort in relation to heating and hot tap water use. In this paper we present visualisation concepts of such connections and reflect on the outcomes in terms of the type of data needed for sustainability assessment, as well as the methods explored for channelling information on individual consumption and environmental impact between customers and the provider of district heating. We have defined challenges in sustainable design for consumer behaviour change in the case of reducing heat and hot water consumption in individual households: (1) The problematic relation between individual behaviour steering and system level district heating, (2) The complexity of environmental impact as indicator for behaviour change, and (3) Ethical considerations concerning the role of the designer

Performance prediction tools for low impact building design

IT systems are emerging that may be used to support decisions relating to the design of a built enviroment that has low impact in terms of energy use and environmental emissions. This paper summarises this prospect in relation to four complementary application areas: digital cities, rational planning, virtual design and Internet energy services

Intelligent Coordination and Automation for Smart Home Accessories

Smarthome accessories are rapidly becoming more popular. Although many companies are making devices to take advantage of this market, most of the created smart devices are actually unintelligent. Currently, these smart home devices require meticulous, tedious configuration to get any sort of enhanced usability over their analog counterparts. We propose building a general model using machine learning and data science to automatically learn a user\u27s smart accessory usage to predict their configuration. We have identified the requirements, collected data, recognized the risks, implemented the system, and have met the goals we set out to accomplish