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

Curve optimization for the anidolic daylight system counterbalancing energy saving, indoor visual and thermal comfort for Sydney dwellings

Daylight penetration significantly affects building thermal-daylighting performance, and serve a dual function of permitting sunlight and creating a pleasant indoor environment. More recent attention has focused on the provision of daylight in the rear part of indoor spaces in designing sustainable buildings. Passive Anidolic Daylighting Systems (ADS) are effective tools for daylight collection and redistribution of sunlight towards the back of the room. As affordable and low-maintenance systems, they can provide indoor daylight and alleviate the problem of daylight over-provision near the window and under-provision in the rear part of the room. Much of the current literature on the ADS pays particular attention to visual comfort and rarely to thermal comfort. Therefore, a reasonable compromise between visual and thermal comfort as well as energy consumption becomes the main issue for energy-optimized aperture design in the tropics and subtropics, in cities such as Sydney, Australia. The objective of the current study was to devise a system that could act as a double-performance of shade and reflective tool. The central aim of this paper is to find the optimum curve that can optimize daylight admission without an expensive active tracking system. A combination of in-detail simulation (considering every possible sky condition throughout a year) and multi-objective optimization (considering indoor visual and thermal comfort as well as the view to the outside), which was validated by field measurement, resulted in the optimum ADS for the local dwellings in Sydney, Australia. An approximate 62% increase in Daylight Factor, 5% decrease in yearly average heating load, 17% savings in annual artificial lighting energy, and 30% decrease in Predicted Percentage Dissatisfied (PPD) were achieved through optimizing the ADS curve

Simultaneous image color correction and enhancement using particle swarm optimization

Color images captured under various environments are often not ready to deliver the desired quality due to adverse effects caused by uncontrollable illumination settings. In particular, when the illuminate color is not known a priori, the colors of the objects may not be faithfully reproduced and thus impose difficulties in subsequent image processing operations. Color correction thus becomes a very important pre-processing procedure where the goal is to produce an image as if it is captured under uniform chromatic illumination. On the other hand, conventional color correction algorithms using linear gain adjustments focus only on color manipulations and may not convey the maximum information contained in the image. This challenge can be posed as a multi-objective optimization problem that simultaneously corrects the undesirable effect of illumination color cast while recovering the information conveyed from the scene. A variation of the particle swarm optimization algorithm is further developed in the multi-objective optimization perspective that results in a solution achieving a desirable color balance and an adequate delivery of information. Experiments are conducted using a collection of color images of natural objects that were captured under different lighting conditions. Results have shown that the proposed method is capable of delivering images with higher quality. © 2013 Elsevier Ltd. All rights reserved

A METHODOLOGY FOR ENERGY OPTIMIZATION OF BUILDINGS CONSIDERING SIMULTANEOUSLY BUILDING ENVELOPE HVAC AND RENEWABLE SYSTEM PARAMETERS

Energy is the vital source of life and it plays a key role in development of human society. Any living creature relies on a source of energy to exist. Similarly, machines require power to operate. Starting with Industrial Revolution, the modern life clearly depends on energy. We need energy for almost everything we do in our daily life, including transportation, agriculture, telecommunication, powering industry, heating, cooling and lighting our buildings, powering electric equipment etc. Global energy requirement is set to increase due to many factors such as rapid industrialization, urbanization, population growth, and growing demand for higher living standards. There is a variety of energy resources available on our planet and non-renewable fossil fuels have been the main source of energy ever since the Industrial Revolution. Unfortunately, unsustainable consumption of energy resources and reliance on fossil fuels has led to severe problems such as energy resource scarcity, global climate change and environmental pollution. The building sector compromising homes, public buildings and businesses represent a major share of global energy and resource consumption. Therefore, while buildings provide numerous benefits to society, they also have major environmental impacts. To build and operate buildings, we consume about 40 % of global energy, 25 % of global water, and 40 % of other global resources. Moreover, buildings are involved in producing approximately one third of greenhouse gas emissions. Today, the stress put on the environment by building sector has reached dangerous levels therefore urgent measures are required to approach buildings and to minimize their negative impacts. We can design energy-efficient buildings only when we know where and why energy is needed and how it is used. Most of the energy consumed in buildings is used for heating, cooling, ventilating and lighting the indoor spaces, for sanitary water heating purposes and powering plug-in appliances required for daily life activities. Moreover, on-site renewable energy generation supports building energy efficiency by providing sustainable energy sources for the building energy needs. The production and consumption of energy carriers in buildings occur through the network of interconnected building sub-systems. A change in one energy process affects other energy processes. Thus, the overall building energy efficiency depends on the combined impact of the building with its systems interacting dynamically all among themselves, with building occupants and with outdoor conditions. Therefore, designing buildings for energy efficiency requires paying attention to complex interactions between the exterior environment and the internal conditions separated by building envelope complemented by building systems. In addition to building energy and CO2 emission performance, there are also other criteria for designers to consider for a comprehensive building design. For instance, building energy cost is one of the major cost types during building life span. Therefore, improving building efficiency not only addresses the challenges of global climate change but also high operational costs and consequent economic resource dependency. However, investments in energy efficiency measures can be costly, too. As a result, the economic viability of design options should be analysed carefully during decision-making process and cost-effective design choices needs to be identified. Furthermore, while applying measures to improve building performance, comfort conditions of occupants should not be neglected, as well. Advances in science and technologies introduced many approaches and technological products that can be benefitted in building design. However, it could be rather difficult to select what design strategies to follow and which technologies to implement among many for cost-effective energy efficiency while satisfying equally valued and beneficial objectives including comfort and environmental issues. Even using the state-of-the-art energy technologies can only have limited impact on the overall building performance if the building and system integration is not well explored. Conventional design methods, which are linear and sequential, are inadequate to address the inter-depended nature of buildings. There is a strong need today for new methods that can evaluate the overall building performance from different aspects while treating the building, its systems and surrounding as a whole and provide quantitative insight information for the designers. Therefore, in the current study, we purpose a simulation-based optimization methodology where improving building performance is taken integrally as one-problem and the interactions between building structure, HVAC equipment and building-integrated renewable energy production are simultaneously and dynamically solved through mathematical optimization techniques while looking for a balanced combination of several design options and design objectives for real-life design challenges. The objective of the methodology is to explore cost-effective energy saving options among a considered list of energy efficiency measures, which can provide comfort while limiting harmful environmental impacts in the long term therefore financial, environmental and comfort benefits are considered and assessed together. During the optimization-based search, building architectural features, building envelope features, size and type of HVAC equipment that belong to a pre-designed HVAC system and size and type of considered renewable system alternatives are explored simultaneously together for an optimal combination under given constraints. The developed optimization framework consists of three main modules: the optimizer, the simulator, and a user-created energy efficiency measures database. The responsibility of the optimizer is to control the entire process by implementing the optimization algorithm, to trigger simulation for performance calculation, to assign new values to variables, to calculate objective function, to impose constraints, and to check stopping criteria. The optimizer module is based on GenOpt optimization environment. However, a sub-module was designed, developed and added to optimization structure to enable Genopt to communicate with the user-created database module. Therefore, every time the value of a variable is updated, the technical and financial information of a matching product or system equipment is read from the database, written into simulation model, and fed to the objective formula. The simulator evaluates energy-related performance metrics and functional constraints through dynamic simulation techniques provided by EnergyPlus simulation tool. The database defines and organizes design variables and stores user-collected cost related, technical and non-technical data about the building energy efficiency measures to be tested during the optimization. An updated version of Particle Swarm Optimization with constriction coefficient is used as the optimization algorithm. The study covers multi-dimensional building design aims through a single-objective optimization approach where multi objectives are represented in a ε-Constraint penalty approach. The primary objective is taken as minimization of building global costs due to changes in design variables therefore it includes minimization of costs occur due to operational energy and water consumption together with ownership costs of building materials and building systems. Moreover, a set of penalty functions including equipment capacity, user comfort, CO2 emissions and renewable system payback period are added to the main objective function in the form of constraints to restrict the solution region to user-set design target. Consequently, multi-objective design aims are translated into a single-objective where the penalty functions acts as secondary objectives. The performance of the proposed optimization methodology was evaluated through a case study implementation where different design scenarios were created, optimized and analysed. A hypothetical base-case office building was defined. Three cities located in Turkey namely Istanbul, Ankara and Antalya were selected as building locations. Therefore, the performance of the methodology in different climatic conditions was investigated. An equipment database consists of actual building materials and system equipment commonly used in Turkish construction sector was prepared. In addition, technical and financial data necessary for objective function calculation were collected from the market. The results of the case studies showed that application of the proposed methodology achieved giving climate-appropriate design recommendations, which resulted in major cost reductions and energy savings. One of the most important contributing factors of this thesis is introducing an integrative method where building architectural elements, HVAC system equipment and renewable systems are simultaneously investigated and optimized while interactions between building and systems are being dynamically captured. Moreover, this research is distinctive from previous studies because it makes possible investigating actual market products as energy efficiency design options through its proposed database application and a sub-program that connect optimization engine with the data library. Therefore, application of the methodology can provide support on real-world building design projects and can prevent a mismatch between the optimization recommendations and the available market solutions. Furthermore, another contributing merit of this research is that it achieves formulating competing building design aims in a single objective function, which can still capture multi-dimensions of building design challenge. Global costs are minimized while energy savings are achieved, CO2-equivalent emission is reduced, right-sized equipment are selected, thermal comfort is provided to users and target payback periods of investments are assured. To conclude, the proposed methodology links building energy performance requirements to financial and environmental targets and it provides a promising structure for addressing real life building design challenges through fast and efficient optimization techniques

Hybrid firely and particle swarm optimisation algorithm for optimal dimming level and energy saving in lecturer’s room

The lighting system is one of the main systems in buildings and contributes to the usage of a considerable amount of energy. Therefore, the daylight harvesting system (DHS) is vital in lighting systems to control and minimise energy consumption from artificial lighting. The main factor for employee comfort in workplaces is the lighting system, where it can provide productivity and reduce stress when employees are working if they are getting enough lighting. There are many types of lighting systems and these systems have different performances in terms of energy efficiency, radiated heat, power consumption and lifetime. The optimisation of artificial lighting is required in order to achieve the optimal dimming level for minimising the energy consumption in buildings. To achieve the optimisation, a good, efficient and fast simulation algorithm is required. Thus, this research proposed a hybrid firefly and particle swarm optimisation (HFPSO) algorithm to solve the energy consumption and visual comfort problem by dimming-level minimisation. In order to identify the optimal dimming level, energy consumption, simulation time and luminaire performance, this research work presents the comparison between light-emitting diode (LED) and fluorescent luminaires using the HFPSO algorithm and using the particle swarm optimisation (PSO) algorithm and the firefly algorithm (FA). The comparison results showed that the superior performance of the HFPSO algorithm for the LED luminaire compared with that for existing luminaires, with significant energy savings of up to 64.2% and fully satisfying the average illuminance according to the Malaysian Standard MS-1525. The average simulation time for the LED luminaire was faster by up to 4.9% compared with the fluorescent luminaire. The average simulation time using the proposed HFPSO algorithm for the LED luminaire was faster by up to 7.6% than that for the fluorescent luminaire, compared with 4% and 3.6% faster using the PSO and FA methods, respectively. Therefore, the LED luminaire had the lowest dimming level and better performance in energy savings compared with those of the fluorescent luminaire by using the proposed method

Optimization of Passive Solar Design and Integration of Building Integrated Photovoltaic/Thermal (BIPV/T) System in Northern Housing

With the growing concerns about climate change, Northern Canada is aiming to adopt a “net-zero ready” model building code by 2030. Northern Canada has its unique environmental loads and challenges – namely, the extremely cold climate and the high expense, as the north imports most of its materials and fuel from the south. Significant energy savings could be achieved by optimizing the design of building envelopes and integrating solar design strategies, with a little added construction cost. This thesis focuses on optimizing the passive solar design parameters and the use of thermal and electrical energy from building integrated photovoltaic/thermal (BIPV/T) system as moves toward net-zero energy housing in northern Canada. A reference house representing the typical residential building in northern regions is modeled in EnergyPlus software to study the building design parameters, including thermal resistance of the building envelope, thermal mass, window-wall ratio, shading schedule and ventilation rate. The optimization is carried out by coupling EnergyPlus and Matlab using a multi-objective genetic algorithm. The optimal house under a 25-year life cycle in Yellowknife, NWT, could achieve a 46% energy savings with an initial construction cost increase of 10%. Based on this optimized house, the optimal use of thermal energy produced by a BIPV/T system is evaluated by integration with the space heating system, heat recovery ventilation (HRV) and air-source heat pump (ASHP). Simulation results show that a BIPV/T system with a space heating system can reduce total energy consumption by 15.5%, a BIPV/T system with a HRV could decrease the frost risk time by 8.8% and defrost time by 7.8%, and a BIPV/T system with an ASHP could extend the ASHP working period by 128 hours and increase the annual COP about 2.5%

Multi-objective optimization of building energy design to reconcile collective and private perspectives: CO2-eq vs. Discounted payback time

Building energy design is a multi-objective optimization problem where collective and private perspectives conflict each other. For instance, whereas the collectivity pursues the minimization of environmental impact, the private pursues the maximization of financial viability. Solving such trade-off design problems usually involves a big computational cost for exploring a huge solution domain including a large number of design options. To reduce that computational cost, a bi-objective simulation-based optimization algorithm, developed in a previous study, is applied in the present investigation. The algorithm is implemented for minimizing the CO2-eq emissions and the discounted payback time (DPB) of a single-family house in cold climate, where 13,456 design solutions including building envelope and heating system options are explored and compared to a predefined reference case. The whole building life is considered by assuming a calculation period of 30 years. The results show that the type of heating system significantly affects energy performance; notably, the ground source heat pump leads to the highest reduction in CO2-eq emissions, around 1300 kgCO2-eq/m2, with 17 year DPB; the oil fire boiler can provide the lowest DPB, equal to 8.5 years, with 850 kgCO2-eq/m2 reduction. In addition, it is shown that using too high levels of thermal insulation is not an effective solution as it causes unacceptable levels of summertime overheating. Finally a multi-objective decision making approach is proposed in order to enable the stakeholders to choice among the optimal solutions according to the weight given to each objective, and thus to each perspectiv

The road to developing economically feasible plans for green, comfortable and energy efficient buildings

Owing to the current challenges in energy and environmental crises, improving buildings, as one of the biggest concerns and contributors to these issues, is increasingly receiving attention from the world. Due to a variety of choices and situations for improving buildings, it is important to review the building performance optimization studies to find the proper solution. In this paper, these studies are reviewed by analyzing all the different key parameters involved in the optimization process, including the considered decision variables, objective functions, constraints, and case studies, along with the software programs and optimization algorithms employed. As the core literature, 44 investigations recently published are considered and compared. The current investigation provides sufficient information for all the experts in the building sector, such as architects and mechanical engineers. It is noticed that EnergyPlus and MATLAB have been employed more than other software for building simulation and optimization, respectively. In addition, among the nine different aspects that have been optimized in the literature, energy consumption, thermal comfort, and economic benefits are the first, second, and third most optimized, having shares of 38.6%, 22.7%, and 17%, respectively