Problem Of Energy Consumption Research Articles

High performance integrated photonic circuit based on inverse design method

The basic indexes of all-optical integrated photonic circuits include high-density integration, ultrafast response and ultra-low energy consumption. Traditional methods mainly adopt conventional micro/nano-structures. The overall size of the circuit is large, usually reaches hundreds of microns. Besides, it is difficult to balance the ultrafast response and ultra-low energy consumption problem, and the crosstalk between two traditional devices is difficult to overcome. Here, we propose and experimentally demonstrate an approach based on inverse design method to realize a high-density, ultrafast and ultra-low energy consumption integrated photonic circuit with two all-optical switches controlling the input states of an all-optical XOR logic gate. The feature size of the whole circuit is only 2.5 μm × 7 μm, and that of a single device is 2 μm × 2 μm. The distance between two adjacent devices is as small as 1.5 μm, within wavelength magnitude scale. Theoretical response time of the circuit is 150 fs, and the threshold energy is within 10 fJ/bit. We have also considered the crosstalk problem. The circuit also realizes a function of identifying two-digit logic signal results. Our work provides a new idea for the design of ultrafast, ultra-low energy consumption all-optical devices and the implementation of high-density photonic integrated circuits.

Optimal Energy Consumption Optimization in a Smart House by Considering Electric Vehicles and Demand Response via a Hybrid Gravitational Search and Particle Swarm Optimization Algorithm

Buildings are the main energy consumers across the world, especially in urban communities. Building smartization, or the smartification of housing, therefore, is a major step towards energy grid smartization too. By controlling the energy consumption of lighting, heating, and cooling systems, energy consumption can be optimized. All or some part of the energy consumed in future smart buildings must be supplied by renewable energy sources (RES), which mitigates environmental impacts and reduces peak demand for electrical energy. In this paper, a new optimization algorithm is applied to solve the optimal energy consumption problem by considering the electric vehicles and demand response in smart homes. In this way, large power stations that work with fossil fuels will no longer be developed. The current study modeled and evaluated the performance of a smart house in the presence of electric vehicles (EVs) with bidirectional power exchangeability with the power grid, an energy storage system (ESS), and solar panels. Additionally, the solar RES and ESS for predicting solar-generated power prediction uncertainty have been considered in this work. Different case studies, including the sales of electrical energy resulting from PV panels’ generated power to the power grid, time-variable loads such as washing machines, and different demand response (DR) strategies based on energy price variations were taken into account to assess the economic and technical effects of EVs, BESS, and solar panels. The proposed model was simulated in MATLAB. A hybrid particle swarm optimization (PSO) and gravitational search (GS) algorithm were utilized for optimization. Scenario generation and reduction were performed via LHS and backward methods, respectively. Obtained results demonstrate that the proposed model minimizes the energy supply cost by considering the stochastic time of use (STOU) loads, EV, ESS, and PV system. Based on the results, the proposed model markedly reduced the electricity costs of the smart house.

Distributed Nash equilibrium seeking design for energy consumption games of HVAC systems over digraphs

The energy consumption problem of heating, ventilation, and air conditioning systems over general directed graphs is investigated. The considered problem is firstly reformulated as a Nash equilibrium seeking problem, and a distributed consensus-based algorithm is then proposed to solve it. To address the challenge arising from general directed graphs, a distributed estimation algorithm is embedded such that the explicit dependence on the left eigenvector associated with the eigenvalue zero of the Laplacian matrix can be avoided. Then, the exponential convergence of the proposed distributed Nash equilibrium seeking algorithm is established under a standing assumption. A numerical example is finally provided to verify the effectiveness of the proposed algorithm.

Machine Learning Techniques Focusing on the Energy Performance of Buildings: A Dimensions and Methods Analysis

The problem of energy consumption and the importance of improving existing buildings’ energy performance are notorious. This work aims to contribute to this improvement by identifying the latest and most appropriate machine learning or statistical techniques, which analyze this problem by looking at large quantities of building energy performance certification data and other data sources. PRISMA, a well-established systematic literature review and meta-analysis method, was used to detect specific factors that influence the energy performance of buildings, resulting in an analysis of 35 papers published between 2016 and April 2021, creating a baseline for further inquiry. Through this systematic literature review and bibliometric analysis, machine learning and statistical approaches primarily based on building energy certification data were identified and analyzed in two groups: (1) automatic evaluation of buildings’ energy performance and, (2) prediction of energy-efficient retrofit measures. The main contribution of our study is a conceptual and theoretical framework applicable in the analysis of the energy performance of buildings with intelligent computational methods. With our framework, the reader can understand which approaches are most used and more appropriate for analyzing the energy performance of different types of buildings, discussing the dimensions that are better used in such approaches.

MOF-derived CoN/CoFe/NC bifunctional electrocatalysts for zinc-air batteries

Zinc air battery (ZAB) is one of promising devices to solve the problems of fossil energy consumption and environmental pollution, but the development of low-cost non-precious metal dual-functional catalysts for ZAB still remains a great challenge. Here, we synthesize a bifunctional catalyst (Co5.47N/Co3Fe7/NC) consisting of two kinds of active sites. The Co5.47N/Co3Fe7/NC catalyst shows excellent electrocatalytic activities with a half-wave potential of 0.89 V (versus RHE) for ORR and a low potential of 1.609 V at 10 mA cm−2 for OER in alkaline condition. When the catalyst was used in ZAB, the ZAB provides a maximal power density of 264 mW cm−2 and maintains a stable voltage gap over 180 h. The results show that Co5.47N plays a very important role in both ORR and OER processes, because it effectively improves the conductivity of catalysts. Moreover, the two-phase interface produced by the two crystal structures will produce an electronic coupling effect, forming a large number of active sites. This catalyst provides a reliable alternative to air cathode catalysts for rechargeable zinc air batteries.

Dynamic and highly energy-efficient virtual network embedding method based on elastic optical networks

Network virtualization has been considered one of the promising paradigms for future network architecture, which can satisfy the diversified and personalized requirements of different applications by sharing physical network infrastructure. One of the major challenges in virtualization is how to map virtual networks efficiently onto the substrate network considering different constraints. We aim at solving the problem of high energy consumption of virtual network embedding in respect of the orthogonal frequency division multiplexing using elastic optical networks (EONs). First, we comprehensively consider and design an energy consumption model of network components, which denotes the key part of virtual optical network embedding (VONE). Then, a dynamic heuristic energy-efficient virtual optical network embedding (DEE-VONE) algorithm is proposed to achieve high energy efficiency and resource scalability. Simulation experiments have proven that, compared with the two benchmark algorithms, namely, the FFK-VONE and LRCK-VONE, the proposed method can maximize the network energy consumption saving at the cost of increasing the blocking probability. As the traffic load increases, the DEE-VONE method achieves higher energy-saving efficiency because optical switching energy saving plays an essential role.

Packet loss rate monitoring model of IoT based on differential evolution algorithm

In order to overcome the problems of high energy consumption and low execution efficiency of traditional Internet of things (IoT) packet loss rate monitoring model, a new packet loss rate monitoring model based on differential evolution algorithm is proposed. The similarity between each data point in the data space of the Internet of things is set as the data gravity. On the basis of the data gravity, combined with the law of gravity in the data space, the gravity of different data is calculated. At the same time, the size of the data gravity is compared, and the data are classified. Through the classification results, the packet loss rate monitoring model of the Internet of things is established. Differential evolution algorithm is used to solve the model to obtain the best monitoring scheme to ensure the security of network data transmission. The experimental results show that the proposed model can effectively reduce the data acquisition overhead and energy consumption, and improve the execution efficiency of the model. The maximum monitoring efficiency is 99.74%.

A Novel Approach for Energy-Efficient of WSN Using PEGASIS Protocol

Wireless Sensor Networks (WSNs) have hardware and software limitations and are deployed in hostile environments. The problem of energy consumption in WSNs has become a very important axis of research. To obtain good performance in terms of the lifetime of the WSN, several routing protocols have been proposed. We present in this article a new approach for the energy efficiency of WSN by using the PEGASIS (Power Efficient Gathering In Sensor Information Systems) routing protocol in order to reduce sensor energy consumption and to achieve good performance in terms of the lifetime of the network in WSN. The simulation results have shown that our proposed protocol excels regarding energy consumption in wireless sensor networks, resulting in an extension of a lifetime for the network. We simulated the proposed technique compared with traditional LEACH (Low-Energy Adaptive Clustering Hierarchy) protocol using MATLAB environment.

Towards energy- and interference-aware health monitoring by using WBANs in medicine services

We study the problems of energy consumption and interference for Wireless Body Area Networks (WBANs). Our model relies on a real medical monitoring, where the patient’s vital signals such as EEG, ECG, and EMG are regularly sampled at equal intervals to send to a medical center. For this purpose, an efficient scheme called Energy- and Interference-aware Routing and Scheduling (EIRS) is presented, which is based on three phases. EIRS first uses an energy-aware scheme to develop a data aggregation routing tree for each WBAN. In phase 2, it finds a set of appropriate channels for developed tree in terms of interference. Phase 3 employs a greedy algorithm to assign interfering transmissions to distinct time slots, while scheduling non-interfering transmissions at the same time slot. To realize energy efficient and reliable communication, EIRS jointly employs Euclidean distance, residual energy, channel diversity, multi-radio technology, and spatial reuse. As another advantage, the proposed scheme reduces inter-WBAN interference as well as intra-WBAN interference. Numerical results confirm the superiority of EIRS in terms of network lifetime, consumption of radio resources, interference, and delay.

Improving Degradation Efficiency of Organic Pollutants through a Self-Powered Alternating Current Electrocoagulation System.

Although electrocoagulation technology has been widely researched in wastewater treatment, high energy consumption and electrode passivation are still the main challenges for its widespread applications. Here, we propose a self-powered electrocoagulation system based on a triboelectric nanogenerator (TENG) with alternating current (AC) outputs to solve these two issues, and thus enhance the removal efficiency of organic pollutants. Compared with the direct current source, the AC power source can reduce the electrode passivation, produce more aluminum hydroxide compounds after consuming an equal amount of charges, and thus improve the degradation efficiency. Moreover, the removal efficiency can be further enhanced by decreasing the frequency AC, in which a 5.7-fold improvement was achieved at 0.2 Hz compared to DC at 1.8 Hz. Inspired by the low frequency of ocean wave water, we developed a self-powered AC-electrocoagulation system to directly drive the electrocoagulation reaction by harvesting water wave energy, which can effectively remove 94.8% of xylenol orange and 98.8% of water-oil emulsion, and thus completely address the problem of energy consumption. This study further promotes the application of self-powered electrochemical systems in treating environmental pollution.

Optimal Placement and Sizing of Distributed Generators Based on Multiobjective Particle Swarm Optimization

To solve the problems of environmental pollution and energy consumption, the development of renewable energy sources becomes the top priority of current energy transformation. Therefore, distributed power generation has received extensive attention from engineers and researchers. However, the output of distributed generation (DG) is generally random and intermittent, which will cause various degrees of impact on the safe and stable operation of power system when connected to different locations, different capacities, and different types of power grids. Thus, the impact of sizing, type, and location needs to be carefully considered when choosing the optimal DG connection scheme to ensure the overall operation safety, stability, reliability, and efficiency of power grid. This work proposes a distinctive objective function that comprehensively considers power loss, voltage profile, pollution emissions, and DG costs, which is then solved by the multiobjective particle swarm optimization (MOPSO). Finally, the effectiveness and feasibility of the proposed algorithm are verified based on the IEEE 33-bus and 69-bus distribution network.

Development of an Electrical Energy Consumption Model for Malaysian Households, Based on Techno-Socioeconomic Determinant Factors

Energy-saving strategies are required to address the increasing global CO2 and electrical energy consumption problems. Therefore, the determinant factors of electrical energy consumption consist of socio-demographic changes, occupant behavior, house and appliance characteristics, or so-called techno-socioeconomic factors, which all need to be assessed. Statistics models, such as the artificial neural network (ANN), can investigate the relationship among those factors. However, the previous ANN model only used limited factors and was conducted in the developed countries of subtropical regions with different determinant factors than those in the developing countries of tropical regions. Furthermore, the previous studies did not investigate the various impacts of techno-socioeconomic factors concerning the performance of the ANN model in estimating monthly electrical energy consumption. The current study develops a model with a more-in depth architecture by examining the effect of additional factors such as socio-demographics, house characteristics, occupant behavior, and appliance characteristics that have not been investigated concerning the model performance. Thus, a questionnaire survey was conducted from November 2017 to January 2018 with 214 university students. The best combination factors in explaining the monthly electrical energy consumption were developed from occupant behavior, with 81% of the variance and a mean absolute percentage error (MAPE) of 20.6%, which can be classified as a reasonably accurate model. The current study’s findings could be used as additional information for occupants or for companies who want to install photovoltaic or wind energy systems.

A Numerical Study on the Optimization of the Material Pressure of the Feeding System

To solve the problem of high energy consumption caused by the large initial material pressure of the feeding system, the macroscopic and mesoscopic laws of the effect of the movement of the feeder on the material pressure were studied, and an optimization method of changing the initial position of the feeder to reduce the initial material pressure is proposed. First, the influence of the movement direction of the feeder on the material pressure was studied based on the discrete element method and verified by experiments. A single-factor experiment was designed to analyze the influence of particle size, material repose angle, and bin slope on material pressure, drawing the applicable conditions of the proposed method. On this basis, the influence of the movement state of the feeder on the change of material pressure during the descent process was studied. Then, the motion parameters of the selected feeder descending process were optimized by the response surface method. Finally, case analyses of the vibrating feeding system and the scraper feeding system were carried out. The Discrete Element Method (DEM) calculation results show that the movement of the feeder will cause different distributions and evolution of the force chain on the mesoscale, which will result in different changes in the macroscopic material pressure. The initial material pressure and the material resistance were reduced in the optimized feeding system.

Partition control model and application to an electrically heated residential house

With the increasing environmental problems, the problem of high heating energy consumption in residential dwellings in winter is urgent to solve. To fulfill the requirements of energy-saving and thermal comfort simultaneously, this paper proposed different partition heating schemes and simulated their energy consumptions with DeST and TRNSYS. The results indicated that the partition-controlled heating program that preheating 1 h in advance was the most energy-saving, with an energy-saving rate of 14.13%.

Fuzzy methods to evaluate the development effects of SAGD technology and its application during the middle and later stages of super-heavy oil reservoirs

The problems of energy consumption and decreasing production are prominent during the middle and later stages of steam-assisted gravity drainage (SAGD) in super-heavy oil reservoirs. Geological parameters and production parameters have an important impact on the development effects. However, there is no quantitative method to evaluate production effects of SAGD during the middle and later stages. In order to fill this gap, this study proposed a new approach to quantitatively evaluate the development effects of SAGD by considering the geological and production parameters for super-heavy reservoirs. Where, the primary factors controlling reservoir’s characteristics and development effects of SAGD were determined by using numerical simulation method. There are 42 well pairs that have already entered the middle and later stages using SAGD in the Xinjiang oil field, China, which were taken as an example. Firstly, a fuzzy cluster analysis model was established to classify reservoir by considering three geological indicators. The results indicated the potential of production for each well pair. Then a fuzzy comprehensive evaluation model was established to evaluate development effects by considering five production indicators. The new standards and ranking of evaluation indicators were quantitatively confirmed by using analytic hierarchy processes. Finally, the approach combined reservoirs classification and development effects evaluation to provide a quantitative and comprehensive evaluation of each well pair. The oil recovery ratio and utilization degree of the horizontal section can clearly reflect the development effects according to comprehensive evaluation results, which means that when the well group meets the recovery factor less than 3.0% and the utilization degree of horizontal section less than 85.0%, the development effects are evaluated as “Category III”. Based on the evaluation results, the corresponding follow-up adjustment measures can be adopted. This study has guidance on the development of technical countermeasures for different types of well pairs.

Routing Optimization of Sensor Nodes in the Internet of Things Based on Genetic Algorithm

As an important part of the perception layer of the Internet of things, wireless sensor networks will provide sensing data for the application of the Internet of things, so it is necessary to optimize the routing path of wireless sensor nodes for the application of the Internet of things. Aiming at the problem of optimal selection of wireless sensor nodes for the Internet of things, this paper describes the mathematical model and abstracts it as a multi-objective optimization problem. Secondly, through the analysis of the deployment optimization process, this paper abstracts the optimization selection method of wireless sensor nodes facing the Internet of things and the guarantee method to avoid coverage holes. Then a node selection method based on a genetic algorithm is proposed to solve the problems of high redundancy and high energy consumption in the internet of things. In the application process of genetic algorithm, aiming at the possible problems of standard genetic algorithm, the similarity judgment is introduced into the crossover operation, and the operation of introducing new individuals is added into the genetic process. Finally, simulation experiments are carried out to verify the feasibility, efficiency, and parameter setting of the algorithm. The simulation results show that the proposed method can guarantee the coverage of the area to be monitored, reduce the network energy consumption and keep the energy consumption balanced.

The model construction in airport renewable energy system scale planning based in Multi-Criteria Decision Making

In order to solve the energy consumption and environmental problems, renewable energy was utilized in many airports. However, due to the periodic and intermittent, a single renewable energy generation is difficult to provide a continuous supply all the time. Hybrid energy system was considered to be the important way to alleviate the intermittent. The study focuses on the optimal design of hybrid renewable energy system planning. A model based on Multi-Criteria Decision Making (MCDM) was constructed. This model could achieve the optimization of scale for renewable energy and energy storage devices in hybrid renewable energy. The model was built in consideration of different power grid operation strategies including “Independence-net operation” and “grid connected operation”. The results showed that the environmental and social benefits could be calculated at the same time, and the optimal renewable energy system scale planning outcome could be obtained.

Research on the Deployment Strategy of Blockchain Nodes in the Agricultural Product Blockchain Traceability System

The agricultural product traceability system based on blockchain can monitor the entire growth cycle of agricultural products, trace it at any time, and cannot tamper with information without authorization. However, the energy consumption of the entire system is relatively high due to the introduction of blockchain technology. In order to alleviate the problem of high overall energy consumption, we are trying to reduce the communication power consumption between terminal sensors and the full nodes of the blockchain. We use the K-means algorithm, the DBSCAN algorithm and the improved DK fusion algorithm we proposed to deploy blockchain full nodes to the agricultural products sensors that have been determined to reduce the communication power consumption of the sensor terminals and improve the coverage of the full nodes to the terminal sensors.

In Situ Electrocatalytic Infrared Spectroscopy for Dynamic Reactions

The modern society is confronted with severe problems of energy consumption, climatic variation, and environmental pollution. A series of electrocatalysis reactions, including oxygen evolution reaction (OER), hydrogen evolution reaction (HER), CO2 reduction reaction (CO2RR), N2 or nitrate reduction reaction (NRR or NOxRR), and methanol or ethanol oxidation reaction (MOR or EOR), have become promising ways to solve these serious issues and realize sustainable development. However, the limited understanding of the dynamic process of electrocatalysis still hinders the rational design of these electrocatalysts. Combining infrared spectroscopy with electrocatalysis, in situ electrochemical infrared spectroscopy (in situ EIRS) is a mighty tool to acquire deep insight of dynamic electrocatalytic reaction process at molecule level. Moreover, the surface sensitive in situ EIRS could acquire information about the adsorbates and the key intermediate, making it broad prospects in the study of electrocatalytic dynamic process. In this short review, we summarize recent progress of in situ EIRS with the focus of probing dynamic reactions. In particular, we first briefly introduce the fundamental and the methodological development of in situ EIRS. Then, the recent advances of in situ EIRS applications in electrocatalytic reaction is demonstrated, including OER, HER, CO2RR, NRR, and MOR. Finally, challenges and perspectives of future research in related fields are discussed.

Wind‐Driven Soft‐Contact Rotary Triboelectric Nanogenerator Based on Rabbit Fur with High Performance and Durability for Smart Farming

AbstractWind energy, as one kind of renewable and sustainable energy sources, is expected to solve the problem of energy consumption and environment deterioration. Here, an improved rotary triboelectric nanogenerator (TENG) using rabbit fur‐based soft‐contact (SCR‐TENG) with segmented structure for harvesting low‐speed wind energy is design and fabricated. The adopted soft raw‐rabbit fur shows excellent performance in triboelectrification and is beneficial to reducing frictional resistance and prolonging the lifetime of device. After 480 000 cycles, no obvious wear is observed on the dielectric film surface, and the transferred charge is not significantly attenuated. The mechanical‐to‐electrical energy conversion efficiency of 15.4% can be achieved. Moreover, self‐powered applications have been successfully demonstrated in night direction indication, insect trapping, soil moisture detection, ambient temperature and humidity detection, and signal transmission, powered by SCR‐TENG, toward smart farming.