Efficient Energy Management Research Articles

ENERGY EFFICIENCY MANAGEMENT TOWARDS SUSTAINABLE DEVELOPMENT: THE NEED FOR AN ENERGY EFFICIENCY LEGAL FRAMEWORK

Issues on the ever-growing demand for energy, limited fossil fuels, difficulties in obtaining viable, sustainable energy for sustainable development, and adverse impacts on global climate change have driven the international community towards the use of cleaner and more sustainable energy sources. In addition to the sustained availability and utilisation of renewable energy sources, many countries have developed and implemented energy policies to address the above issues as well as to conserve energy in line with Goal 7 of the Sustainable Development Goals (SDGs). This doctrinal analysis is performed to identify the extent to which energy efficiency and conservation imperatives in Malaysia have been implemented and explore the need to enact a legal framework for energy efficiency in this country. The aim of this paper is to detail the prospects of energy efficiency in Malaysia and discuss efforts made to enable energy efficiency practices with a view toward the sustainability of energy supply for future use. The outcomes of this study demonstrate that the implementation of energy efficiency in the country is consistent with one of the core agenda items for the economies to progress as recommended by the United Nations General Assembly through SDG 7. Nevertheless, without specific legislation for the implementation of energy efficiency, efforts to strengthen the management and implementation of energy efficiency in Malaysia will not be possible.

DEPLOYMENT OF SENSOR NETWORKS FOR EFFICIENT POWER CONSUMPTION MONITORING APPLICATION

This paper presents a wireless monitoring system implemented using a wireless sensor network and the XCTU software. The existing monitoring systems for tracking alternating current utilization in various devices face limitations in terms of real-time data acquisition and seamless wireless transmission to a central computer for comprehensive analysis. This hinders the ability to accurately measure electric current consumption, impeding the development of efficient energy management strategies. Therefore, the primary focus of this monitoring system is to wirelessly track the utilization of alternating current in various devices. The wireless monitoring system relies on the integration of the XBEE device and the SCT 013 current sensor. The SCT 013 current sensor accurately measures electric current consumption, exhibiting a mere 0.03 variation, while the XBEE device is employed to transmit this data wirelessly to a computer for subsequent data analysis. In this configuration, the XBEE operates in AT mode for seamless data transfer to the computer.

Multivariate solar power time series forecasting using multilevel data fusion and deep neural networks

Accurate forecasting of regional solar photovoltaic power (SPVP) generation is essential for efficient energy management and planning. Existing approaches have shown the effectiveness of decomposing the time series to model the stochastic variability in SPVP data. However, these approaches have limitations in extracting and exploiting both spatial and temporal information from complex and high-dimensional data from multiple sources with intricate relationships, which can impact the accuracy of predictions. In this paper, we propose a novel approach called multilevel data fusion and neural basis expansion analysis (MF-NBEA) for forecasting aggregated regional-level SPVP generation. MF-NBEA integrates exogenous data at multiple levels, uses supervised and unsupervised encoders to provide compact data representation, and enhances model learning from complex data by incorporating spatial information. It also includes a sequence analyser module based on a neural network decomposition mechanism to learn the variability in data and incorporates a residuals learner module to improve overall predictions. We evaluate MF-NBEA using two real-world datasets and find that it outperforms state-of-the-art deep learning methods in terms of forecast accuracy. Furthermore, MF-NBEA facilitates information fusion and knowledge extraction to provide interpretable predictions regarding trend, seasonality, and residual components. The insights gained from our approach inform decision-making for energy management and planning, and can lead to more efficient and sustainable resource utilisation.

DSPM: Dual sequence prediction model for efficient energy management in micro-grid

Power generation and consumption predictions are fundamental for smart grid operations, addressing challenges posed by renewable energy variability and irregular consumer demand. This study introduces the Dual Sequence Predictive Model (DSPM) based on Spatiotemporal CNN (STCNN) architecture, offering a holistic solution for forecasting Electricity Generation (EG) and Electricity Consumption (EC) collectively. Leveraging STCNN, the DSPM efficiently extracts spatial and temporal information, improving prediction accuracy and processing speed. A 1D spatial attention module is added to capture crucial spatial dependencies in historical data. Incorporating shared historical weather information streamlines learning process and reduces model complexity. Extensive benchmark evaluations demonstrate the DSPM superior performance, achieving the lowest error rates compared to baseline models. The DSPM also employs the Kullback–Leibler Divergence (KLD) algorithm for power generation and consumption matching, ensuring efficient power distribution within smart and microgrids. Furthermore, the DSPM is evaluated alongside Single Sequence Prediction (SSP) models, providing a comprehensive analysis of its capabilities and comparisons with state-of-the-art models. The SSP model achieved an average RMSE reduction of 17.3% for solar EG data and 15.86% for IHEPC residential EC data, while DSPM reduced RMSE by 7.57% over PowerGrid dataset compared to baseline models.

ENHANCING ENERGY EFFICIENCY WITH AI: A REVIEW OF MACHINE LEARNING MODELS IN ELECTRICITY DEMAND FORECASTING

This study presents a comprehensive review of the impact of artificial intelligence (AI) and machine learning (ML) on enhancing energy efficiency, particularly in the context of electricity demand forecasting. The review systematically explores the paradigm shift brought about by the emergence of AI in energy efficiency, focusing on the role of AI in electricity demand forecasting and the historical evolution of forecasting techniques. A critical analysis of various ML models is conducted, examining their theoretical underpinnings, selection criteria, and performance in diverse scenarios. Key insights reveal that ML models, especially those incorporating deep learning and big data analytics, significantly outperform traditional forecasting methods in accuracy and adaptability. These models are adept at handling complex, nonlinear relationships and large datasets, making them particularly effective in the dynamic and increasingly renewable-focused energy markets. The review also highlights the importance of selecting appropriate ML models based on criteria such as accuracy, adaptability to forecasting periods, data handling capabilities, and environmental impact considerations. The study further delves into the technological, economic, and environmental impacts of ML in energy efficiency. It underscores the potential of ML to drive innov4eations in energy forecasting, contributing to more sustainable and efficient energy management. However, challenges such as data privacy, cybersecurity, and the need for skilled professionals are identified as critical areas requiring attention. Strategic recommendations are provided for practitioners and policymakers, emphasizing the need for investment in AI and ML training, development of supportive regulatory frameworks, and fostering collaborations across sectors. The review concludes with a future outlook, suggesting directions for future research in AI and energy efficiency, particularly in developing robust and scalable ML models that can integrate with renewable energy sources and smart grid technologies. This study serves as a valuable resource for researchers, practitioners, and policymakers engaged in the field of energy efficiency and AI-driven forecasting.
 Keywords: Machine Learning, Energy Efficiency, Demand Forecasting, Artificial Intelligence.

Energy Consumption Prediction by using Machine Learning

Energy consumption prediction is a critical task in today's world, where sustainable energy management and resource optimization are of paramount importance. This abstract presents a machine learning-based approach for accurately predicting energy consumption. By leveraging historical data and various predictive features, our model aims to provide accurate forecasts, enabling better energy resource allocation and efficient energy management.

Membrane and desublimation integrated hydrogen separation followed by liquefaction process: An energy, exergy, and economic evaluation

H2 has gained global attention owing to its clean energy characteristics. As an energy vector, long-term storage and transportation of H2 in liquid form is essential which requires effective pretreatment. Standalone technologies, such as pressure swing adsorption, membrane, and cryogenic, cannot produce highly pure H2 with high recovery in an energy-efficient manner. The integration of technologies is a promising alternative to overcome these limitations. In this regard, membrane and cryogenic (desublimation) techniques are integrated to separate H2 with high purity and recovery. Purified H2 is liquefied to produce 100% saturated liquid H2 (LH2). The proposed process is designed in Aspen Hysys v11 and evaluated with respect to energy, exergy, and economic considerations. The results have shown that the proposed process resulted in H2 separation with 99.99% purity and 95.87% recovery. The total net energy consumption of the proposed integrated process was 11.53 kWh/kg. Exergy analysis revealed that the liquefaction section had the highest specific exergy destruction (∼92%). Moreover, the detailed economic calculations showed that the total annualized process cost was 671.6 m$/y. In this study, the potential of membrane–cryogenic integration with LH2 production is evaluated and shown to be fundamental in the development of a sustainable H2 economy.

Desempeño energético de un sistema de bomba de calor geotérmico acoplado a un invernadero

INTRODUCTION. The research focuses on designing and implementing a geothermal heat pump adapted to a 470 m2 greenhouse in the Andean region. OBJECTIVE. The goal is to maintain a temperature of 15 °C at night and 30 °C during the day, replacing conventional energy sources such as LPG and contributing to the sustainability and efficiency of food production in challenging climatic conditions. METHOD. We estimated the energy potential of solar and geothermal sources through precise measurements, which were compared to theoretical models. Subsequently, we calculated the appropriate size for the geothermal heat pump using the vapor compression cycle from the greenhouse's energy balance. Finally, we considered critical variables such as temperature and relative humidity to assess the energy efficiency of a geothermal heat pump. Prediction intervals were determined for the relevant variables to investigate values outside statistical control, and the construction of a heat map revealed heat transfer and thermal behavior in the greenhouse. RESULTS. The results demonstrated the impact of climatic conditions on the performance of the geothermal heat pump. Out of the 3169 analyzed data points, 7% fell outside the prediction intervals. CONCLUSIONS. This analysis underscores the critical influence of the environment on the energy efficiency of greenhouse cooling systems in the Andean region, suggesting the need for more efficient energy management strategies based on environmental conditions.

Adaptive eco-cruising control for connected electric vehicles considering a dynamic preceding vehicle

Energy consumption and driving safety of a vehicle are greatly influenced by the driving behaviors of the vehicle in front (also termed the preceding vehicle). Inappropriate responses to unanticipated changes in the preceding vehicle can lead to decreased energy efficiency and an increased risk of rear-end collisions. To address this issue, this study proposes an innovative Adaptive Eco-cruising Control Strategy (AECS) for connected electric vehicles (CEVs) considering the dynamic behavior prediction of the preceding vehicle. The AECS, which is designed with a two-stage receding horizon control framework, can adapt to scenarios where the preceding vehicle cuts in or moves out in a safer and energy-efficient manner compared to traditional eco-cruising strategies, which merely focus on a constant preceding vehicle. In the first stage, a prediction model for characterizing the dynamic behavior of preceding vehicles is developed using the Bayesian network. This model is trained using real-world vehicle driving data, allowing it to anticipate the driving trajectories of vehicles changing lanes in front. In the second stage, an energy-saving, safety, and driving comfort-oriented optimization problem is formulated as a quadratic programming form. The eco-cruising speed is then optimized to adapt to the dynamic traffic environment, especially when the preceding vehicle changes over time. Finally, several simulations are conducted to validate the AECS. The results demonstrate that the AECS can improve the energy efficiency of CEVs by up to 11.80% and 19.53% on average compared to the existing cruise control strategies and ensure vehicle driving safety and comfort, without compromising travel time. Additionally, the vehicle cut-in position, the cut-in vehicle speed, and the ego vehicle speed affect the energy efficiency improvement performance of the AECS.

A trifecta membrane modified by multifunctional superhydrophilic coating for Oil/Water separation and simultaneous absorption of dyes and heavy metal

Adsorption and separation are the most important chemical engineering processes for wastewater treatment. Exerting microfiltration membranes as the central separation technology and decorating them with absorption property, forming absorptive membranes, result in the separation and purification of diverse wastewaters in an energy-efficient manner. Herein, a versatile polyanionic coating with superhydrophilicity/underwater superoleophobicity, superior anti-oil-fouling, and efficient charges is acquired through mussel-inspired co-deposition, followed by b-PEI functionalization, N-alkylation, and anionization. Thanks to the superhydrophilicity/underwater superoleophobicity, the as-prepared microfiltration membrane achieves selective separation of oil/water emulsions. Meanwhile, the functional membranes exhibited excellent selective and efficient adsorption capacity for organic dyes and heavy metal ions due to the abundant surface charges. Interestingly, benefiting from superhydrophilicity and considerable surface charges, the as-designed microfiltration membrane can realize selective and efficient simultaneous adsorption and separation of wastewaters with complicated components, including oil/water emulsions, organic dyes, and heavy metal ions by the concept of “achieving three things at one stroke”. Such a versatile and feasible strategy could pave a new path for constructing multifunctional membranes for complex wastewater treatment and purification.

Whale optimization algorithm and internet of things for horizontal axis solar tracker-basedload optimization

<p>Renewable solar energy is the future of all other resources because of its reliability and availability all over the earth. Optimization of the energy consumption and utilization of internet of things (IoT) devices deployed in such systems poses significant challenges. Axis tracker panel is the scope for the next decade toincrease the performance of the existing panels. This research focuses on the development of intelligent energy optimization algorithms for IoT devices. The integration of renewable energy sources and IoT devices in solar-microgrid energy systems offers promising solutions for sustainable and efficient energy management. The proposed whale optimization algorithm (WOA) takes into account dynamic factors, including varying energy availability and fluctuating demand patterns, to optimize the overall performance. Leveraging real-time data from IoT sensors and smart meters, the algorithms balance energy generation and consumption, prioritize critical loads, and incorporate energy forecasting techniques to handle fluctuations in renewable energy production. Moreover, they integrate demand response mechanisms and dynamic pricing models to encourage flexible energy consumption patterns and minimize operational costs. The results of this study demonstrate the significant potential of the WOA algorithm in enhancing the sustainability of microgrid energy systems, paving the way for a greener and more reliable energy future.</p>

Strategies for Low CO2-emission Schools in Norway

Regional and national strategies targeting climate change are a driver for reducing greenhouse gas emissions. Municipalities play an important role towards a sustainable society and in restructuring needed to reach the 2030 European Climate targets. They often require that new buildings shall be zero emission and that the building’s carbon footprint shall be reduced compared to a reference building, starting with public buildings. To reach low or zero emission buildings, the production of renewable energy must equal or outweigh the CO2-emissions associated with the building. The energy consumption of the building should be decreased before designing on-site renewable energy generation systems. This is ensured by following a holistic energy design approach: energy conservation, reducing electrical energy, efficient energy management and producing renewable energy. Although Norway has had a focus on low and zero emission buildings, there is a limited number of low CO2-emission schools. These are designed and constructed by various actors. Therefore, the practical experience and knowledge in the industry is scattered and still in development. This paper aims to summarize practical strategies for low CO2-emission schools in Norway. Several case studies were analyzed to identify common traits and interviews were conducted to identify opportunities, challenges and strategies.

Preface

This volume of Journal of Physics: Conference Series is dedicated to 2023 3rd International Conference on Power System and Energy Internet (PoSEI 2023) supported by Murdoch University, Australia, held during 28th–30th July 2023 in Qingdao, China (virtual conference). The Conference, focused exclusively on recent trends of research in power system and energy Internet, attracted about 80 participants around the world.The immediate purpose of this Conference was to bring together experienced as well as young scholars who are interested in working actively on various aspects of power system and energy Internet, in order to share and discuss recent related researches. And the ultimate plan is to make it a regular event every year based on the very positive response we received which was beyond our expectation.The keynote speeches addressed major theoretical issues, current and forthcoming observational data as well as upcoming ideas in both theoretical and observational sectors. Keeping in mind the principle academic exchange first, the lectures were arranged in such a way that the young researchers had ample scope to interact with the stalwarts who are internationally leading experts in their respective fields of research.Besides the keynote speeches, a good proportion of the participants also presented their work through contributory talks and posters on this big platform. This was particularly encouraging and of benefit to the young participants, given that there were a good number of scientists of international repute among the participants, the feedback from whom could guide them in the right direction. All the contributions were refereed by experts rigorously.The major topics covered in the Conference are: Cogeneration and Distributed Generation, Power Quality and Electromagnetic Compatibility, Energy Efficiency and Energy Management, Information and Communication for Energy Internet, Sensor and Micro-machines, etc.We are indebted to the Murdoch University, Australia for providing us with generous support to organize such a huge conference. We thank all the members of the Conference Committee who contributed their hard labour to make the Conference a great success. We gratefully acknowledge the experts and reviewers for their valuable suggestions on the review process of the papers submitted.We sincerely thank the staff of Journal of Physics: Conference Series for their efforts in the publication of this issue. Last but not the least, we thank all the speakers and participants without whom the Program would not have been such a success. We hope we will have your active participation in the future conference versions of PoSEI as well.The Committee of PoSEI 2023List of Committee Member is available in this Pdf.

Промышленный энергоменеджмент: оценка системы принятия решений

Increased concern about the state of the environment and, accordingly, energy problems in the 70s and 80s. last century significantly influenced the development of new policies and strategic decisions to reduce energy losses and its rational and efficient use. Reducing energy consumption through an annual reduction in energy intensity is an indicator of the implementation of energy efficiency policies. The industrial sector, being one of the main consumers of energy, pays great attention to improving its energy performance. Therefore, increasing attention is being paid to understanding the energy status, as well as the barriers and potential opportunities for implementing energy efficiency and energy conservation programs in the enterprise. One of the main roles in increasing the level of energy efficiency and energy saving of industrial enterprises is played by the energy management system, which implements relevant programs and practices. However, due to the lack of information or knowledge, as well as the high degree of uncertainty and risk in energy markets, the problem of decision making is still relevant at the very first stage of implementing an energy management program at an enterprise, so the importance of determining the parameters that should be included in the decision-making process increases. solutions, and developing an energy management strategy in accordance with the complexity and characteristics of each company. Achieving the energy transition will depend not only on the introduction of new and innovative energy-related technologies, but also on significant and in some cases radical changes in the decisions and behavior patterns of decision makers. Given that budget allocation for any investment must have not only financial but also strategic value for the company, this paper aims to identify the parameters that influence decision-making in the energy sector in industry, taking into account the critical role of energy management systems, energy efficiency and energy saving.

Online Rolling Optimization for Energy Efficiency in Smart Homes

To cope with the variability of electricity consumption patterns, this study proposes an online rolling optimization-based energy efficiency management strategy for smart homes, which considers user preferences on energy saving and electricity comfort. A weight parameter is used to balance these two objectives and users can set them according to their own electricity preferences. The strategy employs predictive models based on historical data and future inputs to forecast system outputs, and applies feedback correction to compensate prediction errors. In order to better express the power consumption satisfaction of users, two measures of user satisfaction are introduced: utility comfort and temperature comfort. Finally, the simulation result shows that the proposed method achieves an average energy reduction rate of 13.97%, which demonstrates that our strategy can achieve significant reductions in power consumption while enhancing user satisfaction.

Necessity of artificial intelligence techniques for power quality issues in electric vehicles

<p>World transportation is in an electrified mass transitional phase due to the predominant features such as a greener environment, fewer maintenance requirements, improved reliability and energy management efficiency compared to conventional transportation. Numerous difficulties have emerged since electric vehicle sales hit record highs, including how electrified transportation works with the grid. Emerging innovations and technological advances in the field of artificial intelligence have led to the development of enhanced controllers for electric vehicle penetration with the grid. This work includes information on the integration of artificial intelligence strategies used for the mitigation of power quality issues. By merging artificial intelligence techniques into the grid, users can achieve efficient energy use and beneficial power interactions between the grid and electric vehicles. To assess the network's practicality, this research develops a charging model that uses a Tasmanian Devil optimization (TDO) algorithm based on MATLAB software. According to research, TDO improves the waveform quality by reducing the THD with better performance and achieves efficient power transfer between the grid and EVs.</p>

2D Materials-based Neuromorphic Computing Electronic Device

Nowadays, with the rapid information explosion connected to all devices, there is a huge demand for effectively processing big data. In particular, conventional von Neumann computing system with physically separated processing and memory units face significant problems in dealing with massive unstructured data such as sound, images, and video because of a von Neumann bottleneck. As a key feature of parallel operations, neuromorphic computing systems can analyze massive unstructured data in a time and energy efficient manner. However, critical issues related to reliability and variability of nonlinearity and asymmetric weight update, have been great challenges in the implementation of artificial synaptic device in practical neuromorphic hardware system. Also, hardware systems enabling artificial neural networks in-situ personal data are essential for adaptive wearable neuromorphic edge computing.

Hybrid Multi-Objective-Derived Horse Herd and Dragonfly Algorithm-Based Energy-Efficient Secured Routing in WSN

Energy efficiency and security have become prominent aspects of Wireless Sensor Networks (WSNs) for transmitting data. The major challenge is to increase the “Quality of Service” (QoS) since it is restricted by fewer constrained resources. Although the existing routing protocols acquire efficient transmission, they are still subsisting with the challenges of improving security and energy conservation. It will be helpful in practical implications like the military, weather forecasting and healthcare industry. As the nodes are operated by the energy preservation of the battery, developing an energy-efficient protocol is challenging. Since WSN possesses massive numbers of sensor nodes, it also has to avoid long-term communication, and the clustering mechanism is a prerequisite. Considering the diverse objectives like delay, energy and distance, there are still remarkable challenges to develop the routing protocol. Moreover, security enhancement becomes another challenging issue over the network for data transmission through the sensor nodes. To overcome these issues, a novel energy-efficient routing model is designed in this paper by using a hybrid algorithm and the multi-objective derivatives. Initially, the clustering approach is employed to reduce the complexity and improve the security in an energy-efficient manner. Therefore, the Cluster Head (CH) is selected significantly using the novel Hybrid Horse Herd-Dragonfly Optimisation (HHHDO). The hybrid algorithm is developed by integrating Horse Herd Optimisation (HHO) and Dragonfly Algorithm (DA). Here, the optimal CH selection is achieved by deriving the multi-objective function regarding “distance, delay, energy, load and trust of nodes”. Trust is measured with the Neural Network (NN)-based HHHDO (NN-HHHDO) model. Depending on the trust evaluation and energy efficiency, the proposed model obtains better communication between the nodes. In the end, the experimentation of the recommended model is made using various measures and it’s ensured that the suggested technique enhances trust among the nodes and energy conservation in comparison to the existing algorithms.

Experimental evaluation of thermal adaptation and transient thermal comfort in a tropical mixed-mode ventilation context

Mixed-mode ventilation offers a promising solution for achieving comfortable indoor environments in an energy-efficient manner. However, in the tropics, characterized by a year-round hot and humid climate, opportunities for natural ventilation (NV) are typically limited to specific hours, necessitating operational mode switches on an intraday basis. Much of the thermal comfort research on mixed-mode ventilation relies either on cross-sectional data collected from different subjects or longitudinal data spanning a few seasons. This raises an open question: Can occupants thermally adapt when switching between NV and air-conditioning (AC) modes within a single day? To address the gap, our experimental study engaged 57 tropically acclimatized subjects, exposing them to combinations of five operational modes and four ceiling fan speeds (0.15-1.15 m/s) over an eight-hour timeframe. The results revealed that the subjects preferred a Standard Effective Temperature (SET*) that was 2-3 °C higher when operating in the NV mode compared to the AC modes, indicating thermal adaptation within the same day and in the same space. Specifically, the adaptation process was observed to stabilize between 35 to 45 mins following a mode switch. These findings contribute to the optimization of thermal comfort in mixed-mode controls, considering the dynamic characteristics of thermal adaptation.

Comprehensive energy efficiency optimization algorithm for steel load considering network reconstruction and demand response

Industrial loads are usually energy intensive and inefficient. The optimization of energy efficiency management in steel plants is still in the early stage of development. Considering the topology of power grid, it is an urgent problem to improve the operation economy and load side energy efficiency of steel plants. In this paper, a two-level collaborative optimization method is proposed, which takes into account the dynamic reconstruction cost, transmission loss cost, energy cost and demand response benefit. The upper level objective is the optimization of topology in the grid structure to optimize the power loss and dynamic reconstruction costs of the grid. The lower level is the energy cost considering demand response, real time price and dynamic demand response price. Firstly, the mathematical models of stable load, impact load and the steel production line load are built. The key parameters are identified by the Back Propagation neural network algorithm according to the actual production data. Secondly, considering the constraints of grid structure and load operation capacity, the impact of dynamic grid loss and real-time dynamic electricity price on the energy efficiency of the whole grid are analyzed in depth. The optimal operation model considering the dynamic reconfiguration and grid tramission loss of distribution network is built. Taking a steel plant park in Northeast China as an example, it is proved that the optimization model can improve energy efficiency on the load side by optimizing energy consumption and demand response participation time on load side. The energy cost is reduced by 17.77% on the load side, the network loss is reduced by 1.8%, and the operating cost of the power grid is reduced by 26.2%, which has a positive effect on improving energy utilization efficiency, reducing distribution network loss, and improving overall economic efficiency.