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 new optimized demand management system for smart grid-based residential buildings adopting renewable and storage energies

Demand Side Management (DSM) implies intelligently managing load appliances in a Smart Grid (SG). DSM programs help customers save money by reducing their electricity bills, minimizing the utility’s peak demand, and improving load factor. To achieve these goals, this paper proposes a new load shifting-based optimal DSM model for scheduling residential users’ appliances. The proposed system effectively handles the challenges raised in the literature regarding the absence of using recent, easy, and more robust optimization techniques, a comparison procedure with well-established ones, using Renewable Energy Resources (RERs), Renewable Energy Storage (RES), and adopting consumer comfort. This system uses recent algorithms called Virulence Optimization Algorithm (VOA) and Earth Worm Optimization Algorithm (EWOA) for optimally shifting the time slots of shiftable appliances. The system adopts RERs, RES, as well as utility grid energy for supplying load appliances. This system takes into account user preferences, timing factors for each appliance, and a pricing signal for relocating shiftable appliances to flatten the energy demand profile. In order to figure out how much electricity users will have to pay, a Time Of Use (TOU) dynamic pricing scheme has been used. Using MATLAB simulation environment, we have made effectiveness-based comparisons of the adopted optimization algorithms with the well-established meta-heuristics and evolutionary algorithms (Genetic Algorithm (GA), Cuckoo Search Optimization (CSO), and Binary Particle Swarm Optimization (BPSO) in order to determine the most efficient one. Without adopting RES, the results indicate that VOA outperforms the other algorithms. The VOA enables 59% minimization in Peak-to-Average Ratio (PAR) of consumption energy and is more robust than other competitors. By incorporating RES, the EWOA, alongside the VOA, provides less deviation and a lower PAR. The VOA saves 76.19% of PAR, and the EWOA saves 73.8%, followed by the BPSO, GA, and CSO, respectively. The electricity consumption using VOA and EWOA-based DSM cost 217 and 210 USD cents, respectively, whereas non-scheduled consumption costs 273 USD cents and scheduling based on BPSO, GA, and CSO costs 219, 220, and 222 USD cents.publishedVersio

Residential Demand Side Management model, optimization and future perspective: A review

The residential load sector plays a vital role in terms of its impact on overall power balance, stability, and efficient power management. However, the load dynamics of the energy demand of residential users are always nonlinear, uncontrollable, and inelastic concerning power grid regulation and management. The integration of distributed generations (DGs) and advancement of information and communication technology (ICT) even though handles the related issues and challenges up to some extent, till the flexibility, energy management and scheduling with better planning are necessary for the residential sector to achieve better grid stability and efficiency. To address these issues, it is indispensable to analyze the demand-side management (DSM) for the complex residential sector considering various operational constraints, objectives, identifying various factors that affect better planning, scheduling, and management, to project the key features of various approaches and possible future research directions. This review has been done based on the related literature to focus on modeling, optimization methods, major objectives, system operation constraints, dominating factors impacting overall system operation, and possible solutions enhancing residential DSM operation. Gaps in future research and possible prospects have been discussed briefly to give a proper insight into the current implementation of DSM. This extensive review of residential DSM will help all the researchers in this area to innovate better energy management strategies and reduce the effect of system uncertainties, variations, and constraints

Zero Order Degradation Rate of Vitamin C in Fresh Orange and Strawberry Juices without Any Preservatives during Storage

Fresh orange and strawberry are rich in vitamin C, which is an essensial nutrient to promote a healthy imunne system. Vitamin C is easily degraded through oxidation process into dehydroascorbic acid during storage. Therefore, it is important to study the factors influencing the degradation rate of vitamin C in fruit juices without any preservatives. The aims of this research were to study the kinetic degradation of vitamin C in fresh orange (concentrated and nonconcentrated) and strawberry juices during storage and how the temperature affects these kinetics. Fresh juices were stored at room, refrigerated and frozen temperature. The content of vitamin C in fresh juices were analyzed using direct iodometric titration. Kinetic study of vitamin C degradation for these juices were carried out under isothermal condition. The degradation rates of vitamin C in fresh fruit juices based on zero kinetic model were compared. At room temperature the degradation rates of vitamin C in strawberry juice were about ten times faster than those in the orange juice. At refrigerated temperature the degradation rates of vitamin C in orange and strawberry juices with sugar were twice lower than theirs without sugar addition

Optimization of energy consumption and environmental impacts of chickpea production using data envelopment analysis (DEA) and multi objective genetic algorithm (MOGA) approaches

AbstractEnergy consumption in agricultural products and its environmental damages has increased in recent centuries. Life cycle assessment (LCA) has been introduced as a suitable tool for evaluation environmental impacts related to a product over its life cycle.In this study, optimization of energy consumption and environmental impacts of chickpea production was conducted using data envelopment analysis (DEA) and multi objective genetic algorithm (MOGA) techniques. Data were collected from 110 chickpea production enterprises using a face to face questionnaire in the cropping season of 2014–2015. The results of optimization revealed that, when applying MOGA, optimum energy requirement for chickpea production was significantly lower compared to application of DEA technique; so that, total energy requirement in optimum situation was found to be 31511.72 and 27570.61MJha−1 by using DEA and MOGA techniques, respectively; showing a reduction by 5.11% and 17% relative to current situation of energy consumption. Optimization of environmental impacts by application of MOGA resulted in reduction of acidification potential (ACP), eutrophication potential (EUP), global warming potential (GWP), human toxicity potential (HTP) and terrestrial ecotoxicity potential (TEP) by 29%, 23%, 10%, 6% and 36%, respectively. MOGA was capable of reducing the energy consumption from machinery, farmyard manure (FYM) diesel fuel and nitrogen fertilizer (the mostly contributed inputs to the environmental emissions) by 59%, 28.5%, 24.58% and 11.24%, respectively. Overall, the MOGA technique showed a superior performance relative to DEA approach for optimizing energy inputs and reducing environmental impacts of chickpea production system

Hybrid tabu search – strawberry algorithm for multidimensional knapsack problem

Multidimensional Knapsack Problem (MKP) has been widely used to model real-life combinatorial problems. It is also used extensively in experiments to test the performances of metaheuristic algorithms and their hybrids. For example, Tabu Search (TS) has been successfully hybridized with other techniques, including particle swarm optimization (PSO) algorithm and the two-stage TS algorithm to solve MKP. In 2011, a new metaheuristic known as Strawberry algorithm (SBA) was initiated. Since then, it has been vastly applied to solve engineering problems. However, SBA has never been deployed to solve MKP. Therefore, a new hybrid of TS-SBA is proposed in this study to solve MKP with the objective of maximizing the total profit. The Greedy heuristics by ratio was employed to construct an initial solution. Next, the solution was enhanced by using the hybrid TS-SBA. The parameters setting to run the hybrid TS-SBA was determined by using a combination of Factorial Design of Experiments and Decision Tree Data Mining methods. Finally, the hybrid TS-SBA was evaluated using an MKP benchmark problem. It consisted of 270 test problems with different sizes of constraints and decision variables. The findings revealed that on average the hybrid TS-SBA was able to increase 1.97% profit of the initial solution. However, the best-known solution from past studies seemed to outperform the hybrid TS-SBA with an average difference of 3.69%. Notably, the novel hybrid TS-SBA proposed in this study may facilitate decisionmakers to solve real applications of MKP. It may also be applied to solve other variants of knapsack problems (KPs) with minor modifications

Advanced Energy Modelling and Life Cycle Assessment of Indoor Agriculture

This thesis investigates the agricultural greenhouse sector in a cold climate, which requires a large amount of natural gas for supplying the substantial heating demands. The heating demand of these structures is calculated, and potential sustainable design methods are implemented to reduce the reliance on carbon-based fuels. Assessment of the environmental impacts of a bell pepper greenhouse in Southwestern Ontario, Canada heated by natural gas was studied. A life cycle assessment (LCA) method is employed to scrutinize the bell pepper greenhouse, pinpointing areas that need improvement. It was concluded that Global Warming (GW) is the significant environmental hazard among other environmental categories (3.87e-2 kg ??2-Eq). It should be noted, the main contributor to global warming is the natural gas being used as the heating resource (3.2e-2 kg ??2-Eq). The analysis is extended to explore the deployment of solar energy as an alternative source for heating. Solar energy is found to be a superior alternative in terms of emissions. Furthermore, in order to integrate solar energy into the energy supplying systems of the greenhouses, a hybrid Solar Thermal/Photovoltaic-Battery Energy Storage (ST/PV-BES) system is modeled. Evaluation of the best configuration of photovoltaic (PV) and solar thermal (ST) modules, and battery energy storage (BES) size to have the minimum Levelized Cost of Energy (LCOE) was conducted. It is proved that the system is economically optimized. Moreover, to improve operational efficiency and reduce the energy demand of commercial greenhouses, parametric optimization of major growing environment variables including cladding material and window to wall ratio as well as the characteristics of the solar thermal model elements such as hot water tank capacity and heat exchanger effectiveness was carried out. It is demonstrated that the best greenhouse configuration which is a system with 80% window area and 20% brick wall area in both lower nodes and upper nodes results in heating and cooling demand energy reduction without significantly compromising the solar energy absorption. This scenario leads to increasing system performance from 36% to 39%. It is also concluded that doubling the tank capacity improves system performance from 36% to 43% and changing the heat exchanger effectiveness has minor impacts on the system performance

The Effect of Storage Temperature for the Detection of Silver Nanoparticles via Engineered Biomolecules

Temperature plays an important role in biology as a way to regulate reaction. In this study, we report the effect of storage temperature (4, 25, and 37oC) for the detection of silver nanoparticles via engineered biomolecules by monitoring the fluorescence intensity. We genetically engineered a biomolecule consisting of silver binding peptide that fused with cellulose binding domain and green fluorescent protein (GFP). This modular protein was a genetically designed peptide, possesses unique and specific interaction with cellulose as a matrix immobilized surface and can be able to capture silver nanoparticle from wastewater solution. Samples were instrumentally analysed everyday. We aim to assess the long-term stability of our genetically modular protein. This strategy was demonstrated a rapid and green environmentally monitoring

Combined application of Artificial Neural Networks and life cycle assessment in lentil farming in Iran

AbstractIn this study, an Artificial Neural Network (ANN) was applied to model yield and environmental emissions from lentil cultivation in Esfahan province of Iran. Data was gathered from lentil farmers using face to face questionnaire method during 2014–2015 cropping season. Life cycle assessment (LCA) was applied to investigate the environmental impact categories associated with lentil production. Based on the results, total energy input, energy output to input ratio and energy productivity were determined to be 32,970.10MJha−1, 0.902 and 0.06kgMJ−1, respectively. The greatest amount of energy consumption was attributed to chemical fertilizer (42.76%). Environmental analysis indicated that the acidification potential was higher than other environmental impact categories in lentil production system. Also results showed that the production of agricultural machinery was the main hotspot in abiotic depletion, eutrophication, global warming, human toxicity, fresh water aquatic ecotoxicity, marine aquatic ecotoxicity and terrestrial ecotoxicity impact categories, while direct emissions associated with lentil cultivation was the main hotspot in acidification potential and photochemical oxidation potential. In addition, diesel fuel was the main hotspot only in ozone layer depletion. The ANN model with 9-10-6-11 structure was identified as the most appropriate network for predicting yield and related environmental impact categories of lentil cultivation. Overall, the results of sensitivity analysis revealed that farmyard manure had the greatest effect on the most of the environmental impacts, while machinery was the most affecting parameter on the yield of the crop