Optimize Energy Usage Research Articles

Exploratory Data Analysis for Building Energy Meters Using Machine Learning

The purpose of this research was to apply exploratory data analysis techniques to building energy meters, such as electricity, cold, heat, and steam meters. A thorough understanding of energy usage patterns becomes increasingly vital in an era of growing awareness of energy management and sustainability. Trends, patterns, and anomalies can be identified in building energy meter data using meticulous data exploration approaches, which can give significant insights for increasing energy efficiency. Exploratory data analysis combined with machine learning approaches may be was used to reveal hidden patterns of energy usage and examine the links between relevant factors. The findings of this exploratory data analysis gave vital insights into building energy use trends. Some significant and hidden information that was crucial for understanding energy usage within a certain time frame in each building was discovered via the investigation of the data used in this study. Steam had the highest use, whereas electricity had the lowest. Utilities were more popular before 5 a.m., followed by healthcare, with daytime use hours beginning around 10 a.m., depending on the area. During the working day, the industry needs more energy. Places of worships use more energy on weekends. There was a significant relation between the number of floors and spaces per level of a building and the height meter reading between May and October. There is a significant association between the kind of buildings used for schools, workplaces and high energy use. This study significantly contributed to the management of the energy and sustainability domains. Using exploratory data analysis and machine learning approaches to building energy meters could optimize energy usage, minimize running costs, and enhance overall energy efficiency. This research is still very open to be continued using other methods, to obtain other hidden information.

Fuzzy‐based techniques for clustering in wireless sensor networks (WSNs): Recent advances, challenges, and future directions

SummaryA wireless sensor network (WSN) is a network of tiny sensors deployed to collect data. These sensors are powered with batteries that have limited power. Recharging and/or replacement of these batteries, however, are not always feasible. Over the past few years, WSN applications are being deployed in diverse fields such as military, manufacturing, healthcare, agriculture, and so on. With the ever‐increasing applications of WSNs, improving the energy efficiency of the WSNs still remains to be a challenge. Applying fuzzy logic to the problem of clustering exploits the uncertainty associated with the factors that affect the lifetime of these sensors and enables the development of models that would improve their performance in real‐world applications. We present a comprehensive review of various fuzzy‐based techniques for clustering in WSNs whose main goal is to optimize energy usage in WSNs while simultaneously improving their overall performance.

Development of Far-UVC-Based Surface Disinfection Prototype for Public Buses

This paper presents a prototype of a disinfection system for public transport specifically aiming to disinfect surfaces contaminated with the SARS-CoV-2 virus on buses using 222 nm wavelength far-ultraviolet light (far-UVC). Our study involved testing the developed technical system installed in a 12 m long M3 category urban bus, an investigation of optimal far-UVC light angles, and the determination of disinfection parameters for bus seat disinfection. The study identified the ideal positioning of a light source for effective disinfection and analyzed three disinfection scenarios, considering zone coverage, disinfection time, and energy demand. A subsystem employing real-time occupancy monitoring enhances the disinfection process in crowded areas of buses. An energy efficiency assessment model is proposed for optimizing energy consumption. Furthermore, the energy consumption analyses in different disinfection scenarios provide valuable insights for optimizing energy usage in public transport disinfection.

Using the evolutionary mating algorithm for optimizing the user comfort and energy consumption in smart building

This paper presents a simulation study focused on optimizing user comfort and energy consumption in smart buildings. Managing energy efficiently in smart buildings poses a significant challenge. The aim of this research is to achieve a high level of occupant comfort while minimizing energy usage. The study considers three fundamental parameters for measuring user comfort: thermal comfort, visual comfort, and indoor air quality (IAQ). Data from temperature, illumination, and CO2 sensors are collected to assess the indoor environment. Based on this information, smart building systems can dynamically adjust heating, cooling, lighting, and ventilation to optimize energy usage and ensure occupant comfort. To address the optimization problem, the Evolutionary Mating Algorithm (EMA) is proposed. EMA belongs to the evolutionary computation group of nature-inspired metaheuristic algorithms and offers a promising solution. A comparative analysis is conducted with other well-known algorithms such as Particle Swarm Optimization (PSO), Differential Evolution (DE), Ant Colony Optimization (ACO), Biogeography-Based Optimization (BBO), Teaching-Learning Based Optimization (TLBO), and Beluga Whale Optimization (BWO). The findings demonstrate the effectiveness of EMA in achieving optimum comfort with minimal energy consumption in smart building systems.

Integration of photovoltaic modules to optimize energy usage in residential buildings

Integration of photovoltaic modules to optimize energy usage in residential buildings

A Hybrid Deep Learning Model Using CNN and K-Mean Clustering for Energy Efficient Modelling in Mobile EdgeIoT

In mobile edge computing (MEC), it is difficult to recognise an optimum solution that can perform in limited energy by selecting the best communication path and components. This research proposed a hybrid model for energy-efficient cluster formation and a head selection (E-CFSA) algorithm based on convolutional neural networks (CNNs) and a modified k-mean clustering (MKM) method for MEC. We utilised a CNN to determine the best-transferring strategy and the most efficient partitioning of a specific task. The MKM method has more than one cluster head in each cluster to lead. It also reduces the number of reclustering cycles, which helps to overcome the energy consumption and delay during the reclustering process. The proposed model determines a training dataset by covering all the aspects of cost function calculation. This training dataset helps to train the model, which allows for efficient decision-making in optimum energy usage. In MEC, clusters have a dynamic nature and frequently change their location. Sometimes, this creates hurdles for the clusters to form a cluster head and, finally, abandons the cluster. The selected cluster heads must be recognised correctly and applied to maintain and supervise the clusters. The proposed pairing of the modified k-means method with a CNN fulfils this objective. The proposed method, existing weighted clustering algorithm (WCA), and agent-based secure enhanced performance approach (AB-SEP) are tested over the network dataset. The findings of our experiment demonstrate that the proposed hybrid model is promising in aspects of CD energy consumption, overhead, packet loss rate, packet delivery ratio, and throughput compared to existing approaches.

Competitors-Aware Stochastic Lap Strategy Optimisation for Race Hybrid Vehicles

World Endurance Championship (WEC) racing events are characterised by a relevant performance gap among competitors. The fastest vehicles category, consisting in hybrid vehicles, has to respect energy usage constraints set by the technical regulation. Considering absence of competitors, i.e. traffic conditions, the optimal energy usage strategy for lap time minimisation is typically computed through a constrained optimisation problem. To the best of our knowledge, the majority of state-of-the-art works neglects competitors. This leads to a mismatch with the real world, where traffic generates considerable time losses. To bridge this gap, we propose a new framework to offline compute optimal strategies for the powertrain energy management considering competitors. Through analysis of the available data from previous events, statistics on the sector times and overtaking probabilities are extracted to encode the competitors' behaviour. Adopting a multi-agent model, the statistics are then used to generate realistic Monte Carlo (MC) simulation of their position along the track. The simulator is then adopted to identify the optimal strategy as follows. We develop a longitudinal vehicle model for the ego-vehicle and implement an optimisation problem for lap time minimisation in absence of traffic, based on Genetic Algorithms. Solving the optimisation problem for a variety of constraints generates a set of candidate optimal strategies. Stochastic Dynamic Programming is finally implemented to choose the best strategy considering competitors, whose motion is generated by the MC simulator. Our approach, validated on data from a real stint of race, allows to significantly reduce the lap time.

Improving Energy Performance in Flexographic Printing Process through Lean and AI Techniques: A Case Study

Flexographic printing is a highly sought-after technique within the realm of packaging and labeling due to its versatility, cost-effectiveness, high speed, high-quality images, and environmentally friendly nature. A major challenge in flexographic printing is the need to optimize energy usage, which requires diligent attention to resolve. This research combines lean principles and machine learning to improve energy efficiency in selected flexographic printing machines; i.e., Miraflex and F&K. By implementing the 5Why root cause analysis and Kaizen, the study found that the idle time was reduced by 30% for the Miraflex machine and the F&K machine, resulting in energy savings of 34.198% and 38.635% per meter, respectively. Additionally, a multi-linear regression model was developed using machine learning and a range of input parameters, such as machine speed, production meter, substrate density, machine idle time, machine working time, and total machine run time, to predict energy consumption and optimize job scheduling. The results of the research exhibit that the model was efficient and accurate, leading to a reduction in energy consumption and costs while maintaining or even improving the quality of the printed output. This approach can also add to reducing the carbon footprint of the manufacturing process and help companies meet sustainability goals.

Information Technology and Digital Sufficiency for Building the Sustainable Circular Economy

Information technologies possess the significant potential to improve the efficiency of resources and optimize energy usage, as well as make a significant contribution to the sustainable circular economy (CE). The concept of digital sufficiency provides a framework for understanding how information technology can be part of significant achievements in the circular economy, especially when embraced by business companies. Moreover, the possibility of the implementation of closed-loop resources has become possible with the development of digital manufacturing technologies. However, the research of establishing the CE in SMEs, especially in fossil-energy-abundant countries, such as the Russian Federation, is quite limited. Our paper fills in this gap by studying the adoption of CE practices as well as the investments for promoting CE in Russian SMEs through such factors as the existence of R&D, bank loans, and access to grants at the national and international level. It achieves this based on the data sample of 314 managers of Russian SMEs. Our results demonstrate that the investment or existence of R&D in SMEs and knowledge of CE as well the governmental funding and access to wider markets all together tend to have a significant and positive effect on implementing and investing into CE in SMEs, while the administrative barriers yield a small but negative effect. These results might be helpful for the relevant stakeholders in order to identify factors catalyzing attention from both the SMEs engaged in CE transitions, as well as help the decision makers wishing to foster the transformation of the SMEs to a circular economy. We can conclude that supporting SMEs (both financially and via increasing their public awareness) to make their own transitions towards CE has a societal effect that can speed up a greener transition and significantly contribute to increasing energy efficiency.

A meta-heuristic-based energy efficient route modeling for EVs integrating start/stop and recapturing energy effect

The research interest in electric vehicles (EVs) has recently sparked exponentially on account of curbing carbon emissions and paucity of fuel resources. No doubt, these vehicles are ecologically sustainable and move smoothly at a moderate speed that is economical on fuel, but their limited range due to the battery reserve demands an efficient use of energy resources. An immediate solution to overcome this problem lies in regulating the EV route, with optimum energy usage. Thus, this study presents a mathematical model that accounts for vehicle energy consumption with the vehicle’s start/stop energy expenditure. To achieve the objective of maximizing the energy-efficiency, it also introduces Amplified-ACO (A2CO), a routing algorithm based on Ant Colony Optimization (ACO) principles that make use of the probabilistic selection model (DS2 relies on Distance, Speed, and State-of-Charge). This study simulates the proposed model over the real-time map of Chandigarh (India) and provides a comparative study against some of the nature-inspired meta-heuristic approaches such as classical ACO and Particle Swarm Optimization (PSO). The proposed model justifies its significance under various parameters, viz. energy consumption, travel time, total travel distance, and remaining SoC.

Regenerative Manufacturing: Crafting a Sustainable Future through Design and Production

In an era characterised by mounting environmental concerns and a growing awareness of the critical need for sustainability, the manufacturing industry stands at a crossroads. “Regenerative Manufacturing” emerges as a visionary strategy that not only tries to lower the ecological footprint of production but also seeks to restore and rejuvenate ecosystems, communities, and economies. This abstract provides a look into the profound potential of regenerative manufacturing, showcasing its main principles, processes, and its transformational impact on the future of design and production. Regenerative manufacturing signifies a fundamental transformation in the conceptualization, production, and use of items. The manufacturing process incorporates sustainability, circularity, and resilience throughout all its stages, encompassing material selection, design, production, distribution, and end-of-life concerns. The holistic approach discussed here places significant emphasis on the reduction of waste, optimisation of energy usage, and the utilisation of regenerative resources. This strategy aims to establish a regenerative cycle that actively supports the nourishment of the environment, rather than causing its depletion By employing novel methodologies such as biomimicry and generative design, this approach effectively harnesses the knowledge inherent in nature to stimulate the development of sustainable solutions. The regenerative manufacturing paradigm places significant emphasis on the core principles of collaboration and inclusivity. The recognition of the interconnection of all stakeholders is evident, encompassing producers, designers, customers, and local communities. By promoting openness and upholding ethical standards, this approach facilitates socially responsible production techniques that enhance the agency of local economies, safeguard cultural heritage, and prioritise the welfare of employees. The revolutionary capacity of regenerative manufacturing extends beyond the scope of specific goods and sectors. The power of this phenomenon lies in its ability to transform economic systems, facilitating a shift away from a linear model characterised by the processes of extraction, production, and disposal, towards a regenerative and circular economy. This transition offers not alone ecological advantages, but also financial robustness and enduring success.

Optimizing Energy Efficiency in Electrical Power Systems

Energy efficiency in electrical power systems has emerge a paramount issue in latest years due to the increasing worldwide demand for electrical power and the developing environmental awareness. This journal explores the multifaceted demanding situations and possibilities in optimizing energy efficiency in electrical power systems. It delves into the crucial areas of generation, transmission, distribution, and consumption, providing a comprehensive evaluation of the different factors and technology which can contribute to improved energy efficiency. The journal discusses cutting-edge strategies, including advanced grid management techniques, renewable energy integration, and demand-side management, to enhance the sustainability and resilience of power systems. It also highlights the position of emerging technologies such as smart grids, energy storage systems, and data analytics in optimizing energy usage and reducing losses. Furthermore, this journal provides insights into the economic, environmental, and social implications of energy efficiency initiatives within the power sector. It explores policy frameworks, regulatory measures, and enterprise collaborations geared toward fostering a transition in the direction of more energy-efficient electrical power systems.

Advancements and Challenges in Energy-efficient 6G Mobile Communication Network

The arrival of 6G mobile communication networks is anticipated to revolutionize the technological landscape, bringing about profound innovations. This research paper explores the various technological advancements that will pave the way for the advent of 6G networks, with a particular focus on addressing energy consumption. It is widely recognized that energy efficiency plays a crucial role in the evolution of 6G networks. To enhance network performance, user experience, and resource management, the integration of Artificial Intelligence (AI) is expected to be a pivotal technology. AI-based solutions can effectively optimize energy usage and contribute to the overall efficiency of 6G networks. Furthermore, the incorporation of wireless communication systems, telecommunication, and the Internet of Things (IoT) will be integral to the infrastructure of 6G networks. The need for significant enhancements in 6G networks is also examined in this study. Ensuring the safety and protection of 6G networks from cyber threats becomes increasingly important due to the growing reliance on networked communication and the sensitive nature of transmitted information. Cutting-edge security methods such as homomorphic encryption and blockchain technology may be essential in this regard. Moreover, this research paper explores the impact of 6G networks on various domains and discusses the challenges that must be overcome to unlock the technology’s full potential. To ensure responsible adoption and usage of 6G networks, the development of new business models and regulatory frameworks may be necessary to support their implementation while addressing energy consumption concerns.

Modelling and Use of Adaptive Control Using Petri Nets

The issue of modeling holds significant importance for the applicability of control theory, particularly at higher control levels where the verbal description of control objects becomes increasingly crucial. However, a notable disadvantage of such verbal descriptions is the inherent ambiguity and vagueness of the obtained results. This article aims to address this challenge by proposing an approach that utilizes Petri nets (PN) to model and control a specific component within an intelligent home. Additionally, the Simulink modeling tool and Petri net analysis tools are introduced as aids in the modeling process. The article focuses on creating a model that accurately represents the temperature regulation system in the home, considering the constant rise in energy consumption and costs. Furthermore, the transfer of the model to the Simulink environment using IF THEN rules is demonstrated, allowing for an evaluation of its performance. Through the comprehensive application of this approach, the aim is to optimize energy usage and reduce costs within household environments.

Collaborative analysis for energy usage monitoring and management on a large university campus

This describes a successful ongoing collaboration between the Department of Statistics and the Facilities Operations at the University of Connecticut (UConn). The collaboration stemmed from facilities reaching out to the productive Statistical Consulting Services (SCS) in the Department of Statistics for help in understanding and optimizing energy usage across the large university campus. Starting on a small scale with one faculty leader and two graduate assistants in 2016, the continued high level of deliverables resulted in the project growing in personnel and scope to involve four statistics faculty collaborators, four engineers and operations staff from the facilities unit, one staff from Office of Budget and Planning, one postdoc, ten PhD graduate assistants, four MS graduate students, five UConn undergraduate students, and two international visiting undergraduate researchers. This paper highlights this journey and explains how the concerted effort and team work continues to pave the way for useful implementation, many‐tiered mentoring, interesting research projects, and team satisfaction of a job well done.

A conceptual framework for waste heat recovery from compression ignition engines: Technologies, working fluids & heat exchangers

• The current article reviews the potentials of waste heat recovery systems in diesel engines. • The study highlights the importance of working fluids and their performance in waste heat recovery systems. • This research work reviews the importance of heat exchanger design and the choice of waste heat recovery systems. In recent years, the awareness about sustainable energy production using renewable energy sources has increased drastically due to the depletion of fossil fuels and the implementation of environmental protection measures. The effective energy conversion rate of conventional combustion engines stands at 35% whereas the rest of the energy gets dissipated as waste heat to cool the engine. Several Waste Heat Recovery (WHR) methods have been developed so far such as the Organic Rankine Cycle (ORC), thermoelectric generator (TEG), Stirling engine (SE) and the Electric turbo-compounding (ETC). Various research works have been conducted so far to enhance the efficiency of combustion engines through innovative heat recovery systems and by reducing the energy losses, especially in automotive applications. The novelty of the current review article lies in analyzing different aspects of the WHR technologies in terms of increasing the fuel economy and environmental compliance in diesel engines from the perspectives of technology and application feasibility. Further, the current study is also an initiative to present a comprehensive review of different criteria regarding working fluid selection, heat exchanger parameters and their optimization. The results of this article infer that waste heat can be used in an attractive way to produce additional power efficiently, which in turn can result in maximum overall efficiency and optimal usage of the waste energy through the implementation of an exhaust heat recovery system. The current study also recommends that the fuel economy of compression ignition engines can be significantly improved by paying special attention to innovative heat exchanger designs, different parameters of the heat exchangers and working fluid selection.

A review of the Digital Twin technology for fault detection in buildings

This study aims to evaluate the utilization of technology known as Digital Twin for fault detection in buildings. The strategy consisted of studying existing applications, difficulties, and possibilities that come with it. The Digital Twin technology is one of the most intriguing newly discovered technologies rapidly evolving; however, some problems still need to be addressed. First, using Digital Twins to detect building faults to prevent future failures and cutting overall costs by improving building maintenance is still ambiguous. Second, how Digital Twin technology may be applied to discover inefficiencies inside the building to optimize energy usage is not well defined. To address these issues, we reviewed 326 documents related to Digital Twin, BIM, and fault detection in civil engineering. Then out of the 326 documents, we reviewed 115 documents related to Digital Twin for fault detection in detail. This study used a qualitative assessment to uncover Digital Twin technology’s full fault detection capabilities. Our research concludes that Digital Twins need more development in areas such as scanner hardware and software, detection and prediction algorithms, modeling, and twinning programs before they will be convincing enough for fault detection and prediction. In addition, more building owners, architects, and engineers need substantial financial incentives to invest in condition monitoring before many of the strategies discussed in the reviewed papers will be used in the construction industry. For future investigation, more research needs to be devoted to exploring how machine learning may be integrated with other Digital Twin components to develop new fault detection methods.

Evaluating Sustainability Aspects of Housing in UNESCO world heritage city of Yazd Case Study: Lariha House

Nowadays, reducing energy consumption and coexistence with natural and climatic conditions in the region has become one of the most prominent architectural goals. As a result of the growing population and crisis created by rising energy demand, managing energy consumption patterns has become an essential component of sustainability. By recognizing Iran’s culture, customs, traditions, and climates, traditional Iranian architectures designed and built the constructions with the least use of non-renewable energy. Besides, simple and logical methods had provided the most use of renewable energy. As a result, this study explores the optimization of energy consumption in the housing sector through current native pattern designing in hot and dry regions, with a specific emphasis on the relevance of energy savings. The case study of this research is Lariha house. Its Building design style will examine such as wall thickness, placement, the height of internal structures, etc, and the elements used plants, porches, wind flow, etc., which directly influences optimizing energy usage.

Optimizing of Consumption Energy in Smart Building

Intelligent technologies have advanced significantly over the past two decades and have integrated with cities to enhance citizen lives. Additionally, the amount of energy consumed varies depending on the weather, the number of occupants, and the type of building—commercial, residential, or administrative. In contrast, the citizen must make a trade-off between the building's environmental impact, comfort levels, and energy use. In this essay, we'll suggest a smart model that enables management, control, and regulation of energy usage in accordance with a set of standards. As a result, this approach enables real-time calculation, regulation, and optimization of energy usage as well as comfort for the occupants. As a result, the person can learn about their energy consumption without having to read electricity measurements or wait for a billing cycle. Additionally, this method enables energy resource conservation and increases system output even during periods of high demand.

Incorporation of Energy Efficient Computational Strategies for Clustering and Routing in Heterogeneous Networks of Smart City

For decision-making and governance, smart cities depend on tracking data collected via a substantial percentage of wireless sensing nodes. However, several limitations affect Wireless Sensor Network (WSN)-based Internet of Things (IoT) services, such as low battery life, recurrent connectivity problems due to multi-hop connections, and a limited channel capacity. Furthermore, in many systems, clustering and routing are handled independently, which prevents the adaptation of effective strategies for optimal energy usage and prolonged network lifespan. This research gathers data from heterogeneous IoT nodes linked via WSN and distributed across a smart infrastructure. There are two interrelated problems to be addressed with respect to energy efficiency computations: clustering and routing. We provide a new clustering strategy through which efficient routing of critical and regular data is handled. As a result, both clustering and routing have been significantly strengthened, which balances the communication load across different sectors of the smart infrastructure network. Minkowski distance and ranking strategy are used for routing and selecting cluster heads, respectively. Deterministic distributed–time division multiple access (DD-TDMA) scheduling is employed to balance the communication load across the network. The experimental results show that the proposed work outperforms some of the popular cluster-based routing strategies.