Energy Optimization Process Research Articles

Mathematical Modeling and Statistical Analysis of Efficient Cluster Head Selection Based LEACH Protocol for Wireless Sensor Networks

The collection of data from the physical environment, such as temperature and humidity readings, is an essential function of Wireless Sensor Networks (WSNs). Because of its ability to communicate data wirelessly, WSNs are crucial for connecting the digital and physical worlds. Nevertheless, WSN energy efficiency optimization has become a more complicated challenge because of their battery dependency and deployment in adverse locations. The emphasis of this research is the Improved Low-Energy Adaptive Clustering Hierarchy (ILEACH) protocol, which is used as an example of a mathematics-driven strategy to improve WSN energy efficiency. To tackle important problems with energy consumption and network lifetime, mathematical modelling and equation-based analysis are used to study and enhance ILEACH's performance. Particularly in faraway places with few resources, energy efficiency is of the utmost importance when discussing WSNs. This study's mathematical models explore the equations and probability distributions that control the lifetime, energy consumption, and behaviour of sensor nodes. Our goal is to shed light on the energy optimization processes of ILEACH by means of thorough mathematical analysis and derivations. Our method incorporates both theoretical modelling and empirical assessments of ILEACH's cluster head formation, throughput, and network performance as a whole. Our comparison of the mathematical formulations of ILEACH and LEACH shows that our Improved LEACH is better in terms of data throughput, network lifetime, and energy efficiency. Using a figure of merits criterion, we compare LEACH, ILEACH, and the proposed Improved LEACH in detail to help with decision-making and protocol selection. With its solid mathematical groundwork, this study is a great tool for improving the efficiency of WSNs' energy-saving algorithms, which in turn can help networks last longer and run better.

First principle insight on optoelectronic and transport characteristics of lead-free inorganic double perovskites Rb2KTlX6 (X = Cl, Br) for solar cell applications

The toxicity and instability of lead-based perovskites have been replaced with newly investigated stable lead-free inorganic perovskites. The current study included the comprehensive analysis of electronic behavior, elastic, optical, and transport characteristics of lead-free inorganic double perovskites (DPs) Rb2KTlX6 (X = Cl, Br) halides for possible uses. These physical characteristics were revealed using density functional theory (DFT) based Wien2k code. Using the energy optimization process, we have computed structural parameters comparable to existing data using generalized gradient approximation (PBEsol-GGA). In addition, the elastic parameters, formation energy (−1.94 eV and −1.39 eV), and tolerance factor (0.98 and 0.97) endorsed the cubic stability of both halides. Further, we employed modified Becke-Johnson (mBJ) potential to an accurate value of direct bandgaps of both halides reveal that changing the anions from Cl to Br leads to significant adjustment in the band gap, transitioning from the visible to the IR spectrum, with energy levels ranging from 2.08 to 1.2 eV. Our calculated results of optical parameters indicate that both halides with remarkable optoelectronic working due to low reflectivity, high optical absorption, and conductivity. The study reveals that electronic transport factors are associated with temperature due to small bandgap of the materials. The observed figure of merit near unity indicates the semiconducting behavior of these materials that can be useful in thermoelectric tools.

ENHANCEMENT OF ENERGY EFFICIENCY AND BIOSPHORE COMPATIBILITY OF BUILDINGS AND STRUCTURES IN UKRAINE

The use of alternative energy sources is one of the most important areas of modern energy policy, aimed both at improving the environment and saving traditional fuel and energy resources. Adopted legislative and regulatory acts contributed to the rapid growth of "green" energy in Ukraine. The meaning of the ecological and energy optimization process is not to replace one energy source with another, but economic and industrial transformation, decarbonization and decentralization. 
 A technical solution to the problem regarding the source of alternative energy due to the use of heat in the surface layers of the earth's crust is proposed. At the depth of placement of probes-heat exchangers, which are structurally part of heat pump installations, the soil temperature is practically unchanged throughout the year and is positive. Non-freezing glycol solutions are used to increase the operational reliability of the installations. 
 To study the process of thermal interaction in the zone of placement of heat exchanger probes, mathematical modeling using the finite element method was applied. As a result, a significant array of data was obtained, which reflects the dynamics of temperature fields in different modes of operation of ground-source heat pump installations with vertical probes. 
 Of particular interest is the analysis of temperature fields along the depth of the well. After the seasonal selection of heat - the end of the heating period - the minimum temperature of the soil in the boundary zone of the geothermal probe is not lower than 0 0С. That is, freezing of soil rocks does not occur. 
 In the warm period of the year, due to, for example, the working air conditioning system of the building, the energy source of which is the heat pump installation, regeneration of the primary circuit takes place. The average temperature of the rocks between the probes at the beginning of August is, according to calculations, 6.5 0С. 
 The obtained results of analytical studies were used in the design of a ground heat pump installation for the energy supply needs of a public building with an estimated thermal capacity of 200 kW.

Regression Models and Shape Descriptors for Building Energy Demand and Comfort Estimation

Optimal building design in terms of comfort and energy performance means designing and constructing a building that requires the minimum energy demand under the given conditions while also providing a good level of human comfort. This paper focuses on replacing the complex energy and comfort simulation procedure with fast regression model-based processes that encounter the building shape as input. Numerous building shape descriptors were applied as inputs to several regression models. After evaluating the results, it can be stated that, with careful selection of building geometry describing design input variables, complex energy and comfort simulations can be approximated. Six different models with five different building shape descriptors were tested. The worst results were around R2 = 0.75, and the generic results were around R2 = 0.92. The most accurate prediction models, with the highest level of accuracy (R2 > 0.97), were linear regressions using 3rd power and dense neural networks using 1st power of inputs; furthermore, averages of mean absolute percentage errors are 1% in the case of dense neural networks. For the best performance, the building configuration was described by a discrete functional point cloud. The proposed method can effectively aid future building energy and comfort optimization processes.

POSSIBLE WAYS OF TRANSFORMATION IN ENERGY UKRAINE ON THE GROUNDS OF BIOSPHORE COMPATIBILITY

The ways of further transformation of the existing energy market in Ukraine under the conditions of biosphere compatibility are analyzed. The use of renewable energy sources is one of the most important directions of modern energy policy, aimed both at improving the state of the environment and at saving traditional fuel and energy resources. Adopted legislative and regulatory acts contributed to the rapid growth of "green" energy in Ukraine. Currently, the uncontrolled construction and commissioning of exclusively solar power plants at rates that are ahead of planned indicators have caused a threat to the energy security of our state. At the same time, other types of renewable sources, which in the vast majority can be used to balance the energy market, are underdeveloped. Waste from agricultural production, solid household waste landfills under certain conditions can be transformed from sources of environmental pollution into renewable energy sources with biogas generation. Its main components are methane and carbon dioxide. One of the promising ways to increase the efficiency of fuel use can be the synthesis of methane from hydrogen, which is obtained with the use of electricity from alternative sources, and carbon dioxide, which is formed during the production of biomethane. In this way, a complex problem is solved - the simultaneous loading of nuclear power plant units, the use of excess electricity from wind and solar power plants with a decrease in the balancing capacities of thermal generation. The meaning of the ecological and energy optimization process is not to replace one energy source with another, but economic and industrial transformation, decarbonization and decentralization. To solve these problems, measures are proposed to improve the requirements of the regulatory and legislative acts in force in Ukraine on the regulation of the state's energy sector by introducing greater competition, European technical standards and transparent regulatory rules, a better investment climate in the domestic market.

Experimental and Molecular Simulation Studies of Huadian Oil Shale Kerogen.

Microscopic details on the intrinsic chemical reactivity of Huadian oil shale kerogen associated with electron properties of kerogen were investigated by the combination of experimental analyses and molecular simulations. Multimolecular structure models of kerogen with different densities were constructed for examining the accuracy of the proposed kerogen model. Results revealed that the simulated density of the kerogen model is in good agreement with the experimental value. Evaluation of the kerogen model revealed that the energy optimization process is mainly related to the change in the bond angle caused by atom displacement. According to the results from the Hirshfeld analysis of atomic charges, the S atoms in thiophene and S=O structures exhibit positive charges. By contrast, the concentration of electrons on the S atom led to the electronegativity of the sulfhydryl group. To investigate the distribution characteristics of electrons in kerogen, the molecular electrostatic potential (MEP) of a complete kerogen molecule was calculated. Notably, this is the first report of an MEP diagram of the kerogen model that can provide valuable information on the determination of electrophilic or nucleophilic reaction sites for kerogen.

A hybrid Grasshopper Optimization Algorithm and Harris Hawks Optimizer for Combined Heat and Power Economic Dispatch problem

The Combined Heat and Power Economic Dispatch (CHPED) is a real-world optimization problem with several complex constraints that has been a topic of studies around energy systems and optimization processes. This paper attempts to conceptualize a potent algorithm by combining the Modified Grasshopper Optimization Algorithm (MGOA) and the Improved Harris Hawks Optimizer (IHHO) for attaining a better balance between the beginning stages of global search and the latter stages of global convergence. The proposed attempt is abbreviated as MGOA-IHHO. Firstly, the chaotic and Opposition-Based Learning (OBL) methods are invoked to generate the initial population. Second, the mathematical model of the conventional Grasshopper Optimization Algorithm (GOA) is modified using Sine–Cosine Acceleration Coefficients (SCAC) to simulate the global exploration at the initial iterations and graduating to the global convergence at the final stages of optimization. Hence, it is named MGOA. Finally, the adaptive search mechanism integrates the two improved search phases of HHO with a search phase of MGOA to improve the performance of the proposed optimization method. This mechanism investigates the best solution for the aging level of the individual during the optimal evaluation process for choosing an appropriate search phase in MGOA-IHHO. The intended effect of the proposed MGOA-IHHO method is verified with other nature-inspired methods on standard single-objective test functions including 23 benchmark problems, 30 test suits of IEEE Congress on Evolutionary Computation 2017 (CEC2017), and four CHPED problems. The statistical results ascertain that the proposed hybridized MGOA-IHHO is capable of providing promising results when compared with its variants and optimization algorithms introduced in the literature.

Building Retrofitting through Coupling of Building Energy Simulation-Optimization Tool with CFD and Daylight Programs

Simultaneous satisfaction of both thermal and visual comfort in buildings may be a challenging task. Therefore, this paper suggests a comprehensive framework for the building energy optimization process integrating computational fluid dynamics (CFD) daylight simulations. A building energy simulation tool, IDA Indoor Climate and Energy (IDA-ICE), was coupled with three open-source tools including GenOpt, OpenFOAM, and Radiance. In the optimization phase, several design variables i.e., building envelope properties, fenestration parameters, and Heating, Ventilation and Air-Conditioning (HVAC) system set points, were selected to minimize the total building energy use and simultaneously improve thermal and visual comfort. Two different scenarios were investigated for retrofitting of a generic office building located in Oslo, Norway. In the first scenario a constant air volume (CAV) ventilation system with a local radiator in each zone was used, while an all-air system equipped with a demand control ventilation (DCV) was applied in the second scenario. Findings showed that, compared to the reference design, significant reduction of total building energy use, around 77% and 79% in the first and second scenarios, was achieved respectively, and thermal and visual comfort conditions were also improved considerably. However, the overall thermal and visual comfort satisfactions were higher when all-air system was applied.

Virtual agent organizations for user behaviour pattern extraction in energy optimization processes: A new perspective

The optimization of energy use in family homes and public buildings is an ongoing topic of discussion. State-of-the-art research has almost always focused on reducing the consumption of heating systems, air-conditioning or lighting. Despite their importance, user-related variables, such as comfort, are normally not included in the optimization process. These aspects should be considered to be able to effectively minimize energy consumption. Thus, there is a need for a comprehensive energy optimization approach, one that will consider both climatological factors and user behaviour. Learning about user behaviour is key to effective optimization. In this work, the proposed architecture’s capacity to organize Virtual Agent Organizations (VAO) allows it to adapt to highly variable user behavior and preferences. This agent methodology has the ability to manage Wireless Sensor Networks (WSNs), Artificial Neural Networks (ANN) and Case-Based Reasoning (CBR) to obtain user preferences and predict their behaviour in the home or building. The proposed approach has been tested in two different buildings, a traditional-construction house and a modular home, obtaining savings of 30.16% and 13.43%, respectively. These results validate the proposed mixed approach of temperature adjustment algorithms together with the extraction of user behavior patterns for the establishment of a threshold based on preferences.

АКТУАЛЬНІСТЬ ПАРАДИГМИ СТАЛОГО РОЗВИТКУ ЩОДО ТРАНСФОРМАЦІЇ ЕНЕРГЕТИКИ В УКРАЇНІ

У статті розглянуто процес трансформації енергетичної галузі України на основі принципів сталого розвитку. Сьогодні в державі системи енергопостачання є прикладом неефективного використання паливно-енергетичних ресурсів. Окрім того, соціальна спрямованість державної політики сприяє наростанню кризових явищ в енергетичній сфері. Водночас збільшується забруднення атмосферного повітря викидами парникових газів і забруднювальних речовин. Влаштування альтернативних джерел енергії – сонячних та вітрових електростанцій – вирішує проблему «теплового забруднення» атмосфери, проте суттєво підвищує як закупівельну вартість електроенергії, так і ціну для кінцевих споживачів. Обґрунтовано необхідність внесення змін у структуру та механізми ринку електроенергії. Для вирішення зазначених проблем запропоновано вжити заходів з удосконалення вимог чинних в Україні нормативно-правових і законодавчих актів з регулювання енергетичного сектору держави.

Reinforcement Learning-Based Distributed BESS Management for Mitigating Overvoltage Issues in Systems With High PV Penetration

High levels of penetration of distributed photovoltaic generators can cause serious overvoltage issues, especially during periods of high power generation and light loads. There have been many solutions proposed to mitigate the voltage problems, some of them using battery energy storage systems (BESS) at the PV generation sites. In addition to their ability to absorb extra power during the light load periods, BESS can also supply additional power under high load conditions. However, their capacity may not be sufficient to allow charging every time when power absorption is desired. Therefore, typical PV/BESS may not fully prevent over-voltage problems in power distribution grids. This work develops a cooperative state of charge control scheme to alleviate the BESS capacity problem through Monte Carlo tree search based reinforcement learning (MCTS-RL). The proposed intelligent method coordinates the distributed batteries from other regions to provide voltage regulation in a distribution network. Furthermore, the energy optimization process during the day hours and the simultaneous state of charge control are achieved using model predictive control (MPC). The proposed approach is demonstrated on two test cases, the IEEE 33 bus system and a practical medium size distribution system in Alberta Canada.

Applications of the Poisson and diffusion equations to materials science

The scope of a variety of experiments involving the transfer of heat in engineering applications is evident in research on thermal studies throughout the department of Norte de Santander, Colombia. From the energy optimization processes in the brickworks of the province of Ocaña to the didactic use in the Universidad Francisco Santander, Ocaña, Colombia. In this sense, mathematical models will be a fundamental tool in the understanding of thermal transfer processes. The aim of this work is to establish a mathematical model by numerical methods to determine the physical process of heat transfer in liquid crystal. For the data acquisition, an experiment that measures thermal properties on an aluminum plate was designed. We proceed to propose a mathematical model that uses the Poisson equation. Also, we proceed to calculate the solution of the equation by the finite difference method and then make a comparison with the analytical solution and the experimental data.

Advances in Theoretical and Computational Energy Optimization Processes

Industry, construction and transport are the three sectors that traditionally lead to the highest energy requirements [...]

A-EMCS for PHEV based on real-time driving cycle prediction and personalized travel characteristics.

Energy management plays an important role in improving the fuel economy of plug-in hybrid electric vehicles (PHEV). Therefore, this paper proposes an improved adaptive equivalent consumption minimization strategy (A-ECMS) based on long-term target driving cycle recognition and short-term vehicle speed prediction, and adapt it to personalized travel characteristics. Two main contributions have been made to distinguish our work from exiting research. Firstly, online long-term driving cycle recognition and short-term speed prediction are considered simultaneously to adjust the equivalent factor (EF). Secondly, the dynamic programming (DP) algorithm is applied to the offline energy optimization process of A-ECMS based on typical driving cycles constructed according to personalized travel characteristics. The improved A-ECMS can optimize EF based on mileage, SOC, long-term driving cycle and real-time vehicle speed. In the offline part, typical driving cycles of a specific driver is constructed by analyzing personalized travel characteristics in the historical driving data, and optimal SOC consumption under each typical driving cycle is optimized by DP. In the online part, the SOC reference trajectory is obtained by recognizing the target driving cycle from Intelligent Traffic System, and short-term vehicle speed is predicted by Nonlinear Auto-Regressive (NAR) neural network which both adjust EF together. Simulation results show that compared with CD-CS, the fuel consumption of A-ECMS proposed in the paper is reduced by 8.7%.

Study on the energy charging process of a plate-type latent heat thermal energy storage unit and optimization using Taguchi method

To understand the energy charging process of a plate-type thermal energy storage unit using phase change materials (PCMs) under various conditions, an experimental system was fabricated to identify the impact of heat transfer fluid (HTF) temperature, HTF velocity and plate inclination. The energy charging cycles were studied using liquid fraction and accumulated energy storage density with detailed temperature profiles. After the Taguchi optimization, a maximum energy charging rate of 759 W with a 23% increment was obtained with the HTF temperature of 55 °C, the HTF velocity of 5 m/s and the plate inclination of 75°. The melting process was influenced the most by the HTF temperature, followed by the HTF velocity and the plate inclination based on the standardized melting time. However, the influence of inclination angle was larger than the HTF velocity according to the actual melting time. Optimizing inclination angle was an effective way to improve the melting process without consuming additional materials and energy. Because of the PCM natural convection, the melting temperature range varied under different conditions. It was critical to identify the actual melting temperature range to predict the melting performance accurately.

Developing an energy-efficient process planning system for prismatic parts via STEP-NC

Reducing energy consumption for machining operations is a significant research in the manufacturing sector. However, energy-efficient machining oriented to process planning systems is still a challenge. This work uses data structures of Standard for the Exchange of Product model data-Numerical Control (STEP-NC) to develop an energy-efficient process planning system for prismatic parts. A framework of energy-efficient process planning system, consisting of human machine interface, data operation module, and core module, is proposed. The implementation of the system is given, where the core module comprising energy consumption model, ontology, and optimization process is illustrated in details. A test part with typical manufacturing features compliant with STEP-NC is utilized to verify the developed system. The result shows that the process planning system could generate energy-efficient machining scheme with STEP-NC Part21 file or G/M code, which is propitious to the requirement of low energy demand manufacturing.

An Efficient Energy Management in Office Using Bio-Inspired Energy Optimization Algorithms

Energy is one of the valuable resources in this biosphere. However, with the rapid increase of the population and increasing dependency on the daily use of energy due to smart technologies and the Internet of Things (IoT), the existing resources are becoming scarce. Therefore, to have an optimum usage of the existing energy resources on the consumer side, new techniques and algorithms are being discovered and used in the energy optimization process in the smart grid (SG). In SG, because of the possibility of bi-directional power flow and communication between the utility and consumers, an active and optimized energy scheduling technique is essential, which minimizes the end-user electricity bill, reduces the peak-to-average power ratio (PAR) and reduces the frequency of interruptions. Because of the varying nature of the power consumption patterns of consumers, optimized scheduling of energy consumption is a challenging task. For the maximum benefit of both the utility and consumers, to decide whether to store, buy or sale extra energy, such active environmental features must also be taken into consideration. This paper presents two bio-inspired energy optimization techniques; the grasshopper optimization algorithm (GOA) and bacterial foraging algorithm (BFA), for power scheduling in a single office. It is clear from the simulation results that the consumer electricity bill can be reduced by more than 34.69% and 37.47%, while PAR has a reduction of 56.20% and 20.87% with GOA and BFA scheduling, respectively, as compared to unscheduled energy consumption with the day-ahead pricing (DAP) scheme.

A simulation-based optimization method for designing energy efficient buildings

Buildings have a remarkable impact on the environment, therefore finding efficient design configurations satisfying conflicting criteria such as, economic and environmental performance has become an important task. The aim of this study is to propose a methodology to aid designers in satisfying the requirements of government regulations and green building certification programs, while optimizing the energy consumption and maintaining the thermal comfort. In this context, a software package combining a tailor-made thermal simulation software and Matlab Optimtool is developed to implement proposed methodology. The developed software package offers an effective method to perform large number of simulations to find optimal building configuration. The software package was tested on a reference building that represents a typical residential building in Turkey. The testing process is conducted on a five story building located in Istanbul and building information such as building size, location and occupation schedule were used within the developed software. Based on an extensive market search for building materials, cost functions for each parameter were developed as well as a database required for optimization process. A genetic algorithm optimization technique was utilized to minimize the objective function and find the optimal building configuration for the selected building. Development of a simulation-based optimization method fulfills the need for a tool that assists designers to find better design alternatives at the conceptual design stage. The software package requires least amount of data input for energy simulation process to improve usability. A tailor-made energy simulation module was developed to significantly reduce optimization time period. Besides, instead of coupling two separate software packages, performing the energy simulation and optimization processes on a single platform (MATLAB) reduces the time required to find optimal design and eliminates compatibility issues. Developing simulation-based optimization software on a single platform increased the flexibility and user-friendliness of the software. The effectiveness of the approach for finding optimal building configuration is demonstrated in the presented test cases.

BIM-based process management model for building design and refurbishment

A conceptual model of BIM-based design and refurbishment, based on pre-built indicators and allowing the assessment of the building energy demand and eco-building parameters, is presented. The new approach presented in this model creates a knowledge-based decision-making environment for refurbishment strategies and quality control, in this way creating the preconditions to bridge the gap between expected and actual energy performance. The model with integration of new BIM-based optimization subsystems enables energy management and optimization processes. For a comprehensive evaluation of refurbishment measures, it is suggested to include energy efficiency, eco-efficiency, and economic parameters.

Robust tracking based on H-CNN with low-resource sampling and scaling by frame-wise motion localization

In big data age, learning with deep models has shown its outstanding effectiveness in a variety of vision tasks. Unfortunately, the requirement of enormous training samples and computational cost still limit its practicability in the low resource media computing based applications such online object tracking. More recently, CNN based feature extraction has helped tracking-by-learning strategies make a significant progress, although the coarse resolution outputs from the last layer still substantially limit a further improvement of tracking performance. By exploiting the hierarchies of convolutional layers as an image pyramid representation, earlier convolutional layers of hierarchical CNN have shown a certain enhancement of spatial localization but are less invariant to target appearance changes, which inevitably led to an inaccurate region for sampling when the non-rigid objects have intrinsic motion. To guarantee a qualified sampling for tracking-by-learning with hierarchical CNN, in this paper, we incorporated an inter-frame motion guidance with the intra-frame appearance correlations by formulating different energy optimization process in both spatial and temporal domains. With an optional functionality for the extracted regions combination, the proposed algorithm is able to achieve more precise target localization for qualified sampling. Experiments on challenging non-rigid tracking benchmark dataset have demonstrated a superior performance of the proposed tracking in comparison to the other state-of-art trackers.