Effective Energy Usage Research Articles

Comparative Investigation of Thermal Properties Improvement of Nano-Enhanced Organic Phase Change Materials

Thermal energy storage (TES) using phase change materials (PCMs) is one of the potential solutions for stockpiling thermal energy and utilizing it for different applications, which results in effective energy usage. The main drawback of organic PCMs in practical applications is poor heat transfer due to low thermal conductivity (TC). Therefore, investigations into nano-enhanced PCMs are being explored to improve their thermophysical properties. In this work, the various thermophysical characteristics of nano-enhanced lauryl alcohol as a PCM were investigated using carbon-based and metallic nanoparticles. The results indicated that the addition of nanoparticles improved its thermal properties and affected other physical properties, such as viscosity. The latent heat was degraded with the addition of nanoparticles. The results revealed that by adding MWCNTs and CuO nanoparticles, a maximum of 82.6% and 49.6% improvement in TC was achieved, respectively. The maximum drop in latent heat during melting and freezing for the PCM with MWCNTs was about 10.1% and 9.3%, respectively, whereas for the PCM with CuO, they were about 11% and 10.3%, respectively. The lowest supercooling for the PCM with MWCNTs and CuO nanoparticles was 8.6 and 8.3 °C, respectively. The present work confirms that nano-enhanced PCMs can be a potential material for storing thermal energy for various applications.

Secure and scalable data aggregation techniques for healthcare monitoring in WSN

Wireless Sensor Networks (WSN) are crucial in the healthcare field as they allow for the immediate monitoring and collection of data from medical sensors. The growing incorporation of WSN in healthcare requires the creation of authentication protocols that are both strong and energy-efficient. This study presents Lightweight Authentication Protocol for Cloud-based Health-care Systems (LAPCHS) using BLAKE2 designed specifically for WSN in medical settings for secure and scalable data aggregation. The proposed method tackles the difficulties presented by limited resources in sensor nodes, with the goal of achieving a careful equilibrium between strong security and effective energy usage. LAPCHS utilizes innovative techniques to improve the energy efficiency in WSN. The system employs the BLAKE2 cryptographic hash algorithm to establish robust mutual authentication and key agreement between the tag and the cloud server. The protocol is characterized by its low weight and high efficiency, rendering it appropriate for deployment in medical sensor networks with limited resources, which minimizes the need for global key distribution and decreases communication overhead. The proposed method is useful to meet the distinct needs of medical sensor networks, where energy preservation is of utmost importance to ensure extended device scalability. The findings demonstrate that LAPCHS surpasses current approaches in terms of energy efficiency and latency, while simultaneously upholding a robust level of security. The protocol’s novel strategy for managing encryption keys and selecting cryptographic algorithms makes it a promising solution for securing medical sensor networks while maintaining energy sustainability. This study makes a valuable contribution to the developing field of WSN security by proposing a practical solution for ensuring strong and energy-efficient authentication in healthcare applications.

Comparative analysis of solar heaters and heat exchangers in residential water heating

This research shows the comparative analysis between the solar heater and the heat exchanger efficiency. This is used for water heating in this project to show the use of this for residential purposes. The efficiency of these two is calculated in this project which helps to know the efficiency of each model in the water heating purpose. In this research process, a suitable set of comparisons has been drawn between the heat exchanger and water heater that have been worked on based on the effective solar energy. Effective energy usage provides a suitable beneficial impact in Favor of using this residential water heating system of heat exchangers. Detailed information has been represented in the background sections along with rationale aspects where the issues along with their remedies have been depicted.

Hybrid Models for Indoor Temperature Prediction Using Long Short Term Memory Networks—Case Study Energy Center

In the European Union States, household energy usage accounts on average for 40% of overall energy consumption and is responsible for a considerable amount of carbon dioxide emissions. The urgent need to take concrete action to identify solutions that can ensure more effective usage of energy in households, both because of environmental and political reasons, has been repeatedly stated by the European Parliament. White box, grey box and black box predictive models were demonstrated to be a feasible approach to predict the indoor temperature to implement an effective energy management strategy. This study has the purpose of illustrating the potentiality of an LSTM Artificial Neural Network in a short and long-term prediction of the indoor temperature in 15 offices distributed on three storeys of an existing building (Energy Center of Turin (Italy)). The indoor temperature was predicted two hours, five hours and one entire day ahead. The performance of these algorithms has been evaluated not only based on two main criteria (i.e., Root Mean Squared Error and Mean Absolute error) but also by considering the adaptability of the model between the three floors and in terms of different years. Moreover, the proposed work explains how parameters affect performances, aiming to properly identify the optimal model structure. Current results indicate that these models can provide accurate predictions for all the proposed time scales and could all potentially be used for predictive control purposes to optimise the energy demand. The novelty of this study is to show that these models can only be trained on data for a limited period and a specific plane, and then be reliable in predicting indoor temperature, both for different planes and for random periods, taking into account temperature and relative humidity. Furthermore, input parameters are limited to indoor HVAC variables, to ensure acceptable predictions regardless of outdoor parameters availability. The only exception is the outdoor temperature, because of its undeniable and proven importance, it was retained as the only exogenous input variable. Based on current literature and temperature perception capabilities, the results were considered acceptable if the RMSE was less than 0.15 or better yet 0.10, which is equivalent to an inaccuracy between the predicted and actual indoor temperature of 0.15 °C/0.10 °C. On average, the models trained on the Energy Center database achieved an error of 0.1 °C in terms of RMSE.

Challenges for real-world applications of nonintrusive load monitoring and opportunities for machine learning approaches

The rapid increase of power consumption calls for efficient and effective energy usage and conservation strategies in buildings. One of the requirements of achieving such a goal is load monitoring of residential appliances. Among the available load monitoring frameworks, nonintrusive load monitoring (NILM) which is used to estimate the appliance-level power usage from the aggregated signals from smart meters, has the potential to be widely deployed. This paper presents an up-to-date review of NILM methods and the challenges existing in each step of NILM. Then this paper reviews two state-of-the-art machine learning based NILM methods including Hidden Markov Model and Deep Learning techniques. Finally, this paper discusses areas for future research and development of NILM in real-world applications, where machine learning approaches can play a more significant and even decisive role.

Effective energy usage and data compression approach using data mining algorithms for IoT data

AbstractThe emergence of technology and communication system paved way for the development of the internet of things (IoT). The IoT system generates diversified data and the quantity of the data is also huge. The IoT systems are developed to adhere to the situation and to make intelligent decisions in a specified time. Hence, the IoT system necessitates high processing and storage environment, which makes the effective response in a short‐duration. The data transmission across the mobile nodes and cloud service has made huge utilization of energy. The storage and energy consumption are considered as major issues in the IoT system whereas these issues will reflect in the performance of the IoT system. Initiation of edge computing into the IoT system permits the workload to be offloaded from the cloud providers, which is attained from the closer location of the source of data. This improves privacy, minimizes the saving time, and traffic. In this article, an Effective Energy usage and Data Compression Approach using Data Mining proposed for IoT data. The proposed approach is investigated by considering the driving behaviour and it achieves effective compression without influencing the quality of the data. The stress level of the driver is also identified with high accuracy.

Important extrema points extraction-based data aggregation approach for elongating the WSN lifetime

Energy conservation is one of the most basic problems of wireless sensor networks. Energy of sensor nodes is limited, so effective energy usage is important. Data aggregation helps to minimise the volume of data communicated across the network while preserving information quality and decreasing energy waste, thereby enhancing the lifetime of network. In this paper, we propose a data aggregation approach based on the important extrema points extraction for elongating the WSN lifetime (IEEDA). Rather than transmitting all the set of collected measures at the end of every time period, we propose transmitting the extracted important extrema measures of sensor node. Using real-world data sets with radically different properties, we tested our method against two protocols ATP and PFF. The proposed method resulted in a reduction in the amount of the following: data remaining up to 95%, data sent up to 80%, and energy consumed up to 77%.

MPSA: A real‐time collaborative scheduling algorithm for wireless rechargeable sensor networks

SummaryIn this paper, we mainly focus on the influence of network partitioning on the network and develop a new Multi‐chargers region Partition charging Scheduling Algorithm (MPSA) based on network information. By studying the influence of the different number of partitions and partition mode on the whole sensor networks, and simultaneously comparing different MCs scheduling algorithms from multiple perspectives, such as the effective energy usage efficiency and the survival rate of sensor nodes in the whole network, the superiority of Multi‐chargers region Partition charging Scheduling Algorithm (MPSA) scheduling strategy and the rationality and necessity of layered mechanism are proven. Simulations are conducted to verify the advantages of our scheme, revealing that our scheme outperforms in terms of dead node number, energy usage efficiency, and so on.

Early prediction of remaining useful life for Lithium-ion batteries based on a hybrid machine learning method

Early prediction of battery remaining useful life (RUL) is critical to ensure a steady energy supply and the effective usage of energy. To reduce the amount of degradation data required for accurate RUL prediction, this study proposed a deep-learning-based prediction algorithm, namely ADLSTM-MC, which combines the features of adaptive dropout long short-term memory (ADLSTM) and Monte Carlo (MC) simulation. The adaptive dropout method was optimized by the Bayesian optimization. MC simulation is combined to describe the uncertainties of prediction results. To evaluate the performance of the proposed method, we developed two data sets including LiFePO4/graphite and LiNi0.8Co0.15Al0.05O2/graphite batteries with different charge/discharge rates and discharge cut-off voltages. The ADLSTM-MC method is shown to precisely achieve early prediction with only 25% of the entire degradation data, while existing models typically require 40%–70% of the degradation data. The R2 of the proposed capacity prediction model is up to 0.957–0.982, and the prediction errors including root mean square error (RMSE) and mean absolute error (MAE) are less than 0.033 and 0.027, respectively. The results show that the proposed method outperforms the mainstream regression algorithms and several recent published hybrid methods in terms of data requirement and prediction accuracy.

Oxygen Pressure Influence on Properties of Nanocrystalline LiNbO3 Films Grown by Laser Ablation.

Energy conversion devices draw much attention due to their effective usage of energy and resulting decrease in CO2 emissions, which slows down the global warming processes. Fabrication of energy conversion devices based on ferroelectric and piezoelectric lead-free films is complicated due to the difficulties associated with insufficient elaboration of growth methods. Most ferroelectric and piezoelectric materials (LiNbO3, BaTiO3, etc.) are multi-component oxides, which significantly complicates their integration with micro- and nanoelectronic technology. This paper reports the effect of the oxygen pressure on the properties of nanocrystalline lithium niobate (LiNbO3) films grown by pulsed laser deposition on SiO2/Si structures. We theoretically investigated the mechanisms of LiNbO3 dissociation at various oxygen pressures. The results of x-ray photoelectron spectroscopy study have shown that conditions for the formation of LiNbO3 films are created only at an oxygen pressure of 1 × 10−2 Torr. At low residual pressure (1 × 10−5 Torr), a lack of oxygen in the formed films leads to the formation of niobium oxide (Nb2O5) clusters. The presented theoretical and experimental results provide an enhanced understanding of the nanocrystalline LiNbO3 films growth with target parameters using pulsed laser deposition for the implementation of piezoelectric and photoelectric energy converters.

THE INFLUENCE OF BOARD OF DIRECTORS’ CHARACTERISTICS ON THE ENVIRONMENTAL DISCLOSURE AMONG MALAYSIAN COMPANIES

The environmental activities conducted by companies have been scrutinised by the public due to the huge environmental problems faced by many people across the world. These activities are corporate social activities included in the corporate social responsibility (CSR) framework by Bursa Malaysia that informs the public regarding the social and environmental activities implemented by Malaysian companies. Data from 353 companies of various types in Malaysia revealed that the information about effective usage of energy and resources is the most disclosed item by Malaysian companies, followed by the waste management and disposal, and reusing and recycling information. The least disclosed item by Malaysian companies is prevention and reparation program. In addition, the regression results showed that the higher proportion of non-executive directors could increase the disclosure of environmental information. The results prove that the non-executive directors have played their role to increase the companies’ legitimacy by encouraging the management of companies to provide more environmental information. Hence, this study provides significant contribution for the supervisory bodies and policy makers in developing guidelines for Malaysian companies concerning environmental issues through environmental disclosures in their annual reports.

Performance analysis of grid-connected micro-cogeneration system based on the internal combustion engine

The effective usage of energy is an important factor in our country and world. The cogeneration system is one of the methods for the effective usage of energy. The cogeneration system in recent technology is about 30% more effective in reducing fuel consumption and carbon footprint compared to the power and heat production efficiency for traditional systems feeding by fossil fuels. Over the last decade, extensive research has been undertaken on the optimization and use of low-capacity internal combustion gas engines in micro-combined heat-power systems. Problems such as the technology used in the micro-cogeneration system, fuel type and performance, efficiency of the system, utilization of different fuel types, noise, emission, maintenance problems have not been solved yet. In recent years, intensive studies have been carried out on the fuel cells that are being used for the cogeneration system, but the cost and the low life span of fuel cell cogeneration systems are preventing it from becoming widespread. With the proposed system, it will be easily applied in small scale places such as houses, offices, campuses, hospitals, shopping malls, and electricity and heat production efficiency will be used effectively for low CO2 emission. In this paper, the integration of an internal combustion engine which has 9.8kW and 1500 revolutions per minute (rpm) properties, a synchronous generator and grid connection have been successfully achieved and the performance results of the proposed system have been obtained by using MATLAB/Simulink.

Enhancement in lifetime of sensor node using Data Reduction Technique in Wireless Sensor Network

In this paper data compression is becoming more popular, because it helps to reduce the size.A number of clustering protocols have been explored in order to obtain the effective energy usage in WSNs. It is based on randomized rotation of the CHs to distribute the energy load among the sensor nodes evenly in the entire network. Each node elects itself as a CH based on a probabilistic scheme and broadcasts its availability to all the sensor nodes present in the area. The received signal strength is the prime parameter for determining the communication distance between the nodes. The CH performs aggregation of the packets received from all the nodes present in their cluster. Also, all the nodes get a chance to become the CH to balance the overall energy consumption across the network. The aim ofClustering is one of the important methods for prolonging the network lifetime in wireless sensor networks (WSNs). It involves grouping of sensor nodes into clusters and electing cluster heads (CHs) for all the clusters. CHs collect the data from respective cluster‟s nodes and forward the aggregated data to base station.by using Huffman encoding techniques [1].

The effect of thermal camera and pyrometer usage in industrial tempering furnaces on optimum combustion performance

The decrease of energy sources, the increase of energy need and energy costs, competitive conditions in industry and environmental sensitivity require the effective usage of energy sources. In this study, optimum combustion study by using thermal camera and pyrometer in the correct heat furnace of an industry plant for the effective usage of energy in industry. For this reason, in tempering furnace of a milling roll workshop, in order to control the efficiency of tempering, detect the flame length and color and control the homogeneous tempering, 4 thermal cameras and 4 pyrometers have been assembled to the appropriate locations of the furnace. In the study, the tempering furnace has been observed and the results of observations have been evaluated. The contribution of the changes made to the production has been calculated by the information obtained from thermal camera and pyrometer and has been examined with the date of reimbursement. Possible spare part and production saving has been established as 364,460 $ annually and back-payment time as 3 months. Accident risk that is caused by the hot air and sometimes occurs during the controls has also been reduced so that the workers have been forced to work more safely according to WHS (workers’ health and security).

Pareto-optimal clustering scheme using data aggregation for wireless sensor networks

The presence of cluster heads (CHs) in a clustered wireless sensor network (WSN) leads to improved data aggregation and enhanced network lifetime. Thus, the selection of appropriate CHs in WSNs is a challenging task, which needs to be addressed. A multicriterion decision-making approach for the selection of CHs is presented using Pareto-optimal theory and technique for order preference by similarity to ideal solution (TOPSIS) methods. CHs are selected using three criteria including energy, cluster density and distance from the sink. The overall network lifetime in this method with 50% data aggregation after simulations is 81% higher than that of distributed hierarchical agglomerative clustering in similar environment and with same set of parameters. Optimum number of clusters is estimated using TOPSIS technique and found to be 9–11 for effective energy usage in WSNs.

Privacy-Aware Profiling and Statistical Data Extraction for Smart Sustainable Energy Systems

The growing population and global warming have been calling for more effective energy usage, which have stimulated the emergence of smart sustainable energy technology. The distinct feature of this newly emerging technology is the incorporation of advanced information and communication technologies (ICT), which collects more detailed information on how energy is generated, distributed, and consumed. Various smart metering technologies have also been proposed to support the optimization on sustainable energy usage. Despite the obvious benefits of these technologies, people may still hesitate to adopt them because of possible privacy breach. On the other hand, we observe that the major target information for making the sustainable energy system smart is the aggregated statistics of energy usage, not the full detailed usage profiles which would compromise customers' privacy. Thus, how to design schemes to collect aggregated statistics while preserving customers' privacy becomes an important research problem. In this paper, we propose two schemes to deal with this problem. The first one can support dynamic profiling, which can extract aggregated statistical information without compromising individual privacy. The second one aims to extract correlation information among various factors for the smart system design and can also be used as an underlying tool for baseline inference and association rule mining.

Environmental aspects in manufacturing system modelling and simulation—State of the art and research perspectives

The efficient and effective usage of energy and resources is of rising importance in manufacturing companies. This paper argues that manufacturing system simulation is a promising way to realistically cope with those issues and simultaneously consider them with traditional target dimensions. Against this background, the paper analyses whether commercial simulation tools are already capable to address those aspects. It turns out that environmentally related aspects are currently not sufficiently considered as standard functions. Therefore, based on the analysis of on-going research work, different directions for further development are presented and discussed.

Evaluating energy balance and energy indices of wheat production in rain-fed farming in northern Iran

One way to evaluate the sustainable development of agriculture is the use of energy flow method. This method, in an agricultural production system, refers to the energy consumed during production operations and energy saved when crops have been produced. In this article, an evaluation of energy balance and energy indices in rain-fed cultivation of wheat in northern Iran (Guilan province) was carried out. The data were collected in 2011 from 72 farms in Guilan province by using a face to face questionnaire method. By using consumed data as input and total production as output, their respective equivalent energy, energy balance and energy indices were calculated. Energy efficiency (energy output to input energy ratio) for seed and straw in this study were found to be 1.609 and 1.614 respectively, showing the effective usage of energy in agro ecosystems wheat production. Energy balance efficiency (production energy to consumption energy ratio) for seed and straw in this study were found to be 0.92 and 0.79 respectively, showing the effective usage of energy in agro ecosystems wheat production. Key words: Iran, energy indices, energy balance indices, wheat.

An Effective Heuristic DSR

In this paper, we come up with an optimal DSR based on ant colony algorithm to improve the operational efficiency of wireless sensor network. Firstly we introduce the concepts of virtual grid and effective information propagation radius, then accord to the sensor’s translational speed and the rest energy to estimate the sensors’ persistence of life; besides, depend on the radius of effective information propagation we set the size of virtual grid and use this virtual grid as the unit to divide clusters. Finally, we realize the goal of steady information translation and effective energy usage. Under the same simulation environment, we compared the running results of DSR, LEACH and HDSR, the results illustrate that HDSR is not only able to improve the efficiency of information transportation but also equipoise the energy cost, it effectively increases the robustness of WSN.

Going Green: How the Green Movement Impacts the Bottom Line in the Hospitality Industry

The American Hotel and Lodging Association (AH&LA) has released green guidelines for the hospitality industry. Those guidelines include information on effective energy usage, water conservation and waste management. The purpose of this paper is to analyze the literature to determine if the green movement has impacted the hospitality industry's bottom line. Determining whether or not it is practical for older hotels to refurbish and adhere to the guidelines proposed by the AH&LA's recommendations is something that every property must determine on its own. Properties being built now have the option to create sustainable and efficient properties. However, some choose not to due to the perceived cost increases and the fear that there will be little benefit to their own business in the long run. How do we make being green, greener?