Importance Of Energy Management Research Articles

Barriers to the adoption of energy management systems in residential buildings

PurposeThis study aims to examine the challenges in the implementation of energy management systems in residential buildings to lower the running cost and achieve a better energy-efficient building.Design/methodology/approachThis study adopted a mixed research method. Quantitative data was gathered by issuing a research questionnaire to 20 Delphi experts, while qualitative data was acquired through a Systematic Literature Review. Data received was analyzed using the descriptive analysis method.FindingsThe findings revealed that the main barriers to incorporating energy management systems (EMSs) in residential buildings consist of a lack of awareness of energy management systems, lack of management commitment to energy management, lack of knowledge about energy management systems, lack of funds for energy management systems, resistance to energy management technology by the property owners and property managers, distrust and resistance to energy management technology by the property owners, high initial cost of energy management technologies, shortage of technicians for energy management technologies, the nonexistence of local manufacturers of energy management equipment, lack of incentives for efficient energy management and high repair costs of energy management technologies.Research limitations/implicationsThe specific focus on residential buildings may limit the applicability of findings to commercial or industrial sectors. Further research is warranted to accommodate other energy-consuming sectors.Practical implicationsPeople’s perceptions, either wrong or correct, affect their ability to make an informed decision to adopt energy management systems, denying them the opportunity to reap the associated benefits. Therefore, there is an urgent need for the residential industry stakeholders and the government to increase educational opportunities for property owners, managers and property tenants on the importance of energy management systems.Originality/valueThis research presents the potential obstacles and problematic areas that residents may encounter while using these energy management systems. Consequently, they will be able to make a well-informed choice when installing energy management systems. Moreover, the research elucidates the identification of novel perspectives and also unexamined obstacles that impede the widespread use of energy management systems in residential buildings.

Short-term forecasting of rooftop retrofitted photovoltaic power generation using machine learning

This paper explores short-term forecasting of rooftop retrofitted photovoltaic (PV) power generation using a Neural Networks (NN) model, highlighting its importance for energy management and grid integration. The study used data from the Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) of the Faculty of Electrical & Electronics Engineering Technology (FTKEE), capturing a 570.4 kWp rooftop PV system. The data, collected at 15-min intervals from January 29 to February 4, 2024, included thirty-three input features such as ambient temperature, horizontal irradiation, AC voltages, AC currents, and Maximum Power Point Trackers (MPPTs). The target was the total active power in kilowatts. The methodology involved partitioning the data into a training set covering the first five days and a testing set for the last two days. The NN model was compared with other machine learning approaches, including Long Short-Term Memory Networks (LSTM), Gated Recurrent Units (GRU), Random Forest (RF), and k-Nearest Neighbors (k-NN). Performance metrics: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Maximum Error, and Standard Deviation were used to evaluate the models. The results showed that the NN model outperformed all other models, achieving an RMSE of 0.7098, an MAE of 0.3629, a Maximum Error of 3.901, and a Standard Deviation of 0.7076. These findings suggest that NN effectively capture complex patterns in rooftop PV system data, contributing to enhanced reliability and efficiency in short-term solar power forecasting. The study's implications extend to improved grid management and energy efficiency, underlining the significance of advanced machine learning techniques in renewable energy forecasting.

인공지능을 활용한 디지털 트윈 기반 ESS 최적 운영 계획 모델 수립에 관한 연구

Recently, due to the digitalization of power systems, the convergence technology of power systems and ICT has become important, and with the development of operational technologies, the importance of intelligent energy management is increasing. The importance of energy management through intelligent operating technology is emerging. In this paper, a digital twin model simulated by empirical simulation was constructed using the empirical ESS measurement data information collected from the power system. An optimal operation plan model for ESS was established by simulating the state and environment of the system in the virtual space of the digital twin similar to the actual system. It aims to improve the stable operation and efficiency of ESS through rapid situational awareness and correct decision-making by applying a data-based machine learning model rather than an existing optimization-based operation model.

Optimization Techniques for Improving the Performance of Solar Photovoltaic System in Conjunction with Health Informatics and the Hospital Energy Management System

Energy savings, clean energy, savings in utility and energy governance tools are buzzwords in the healthcare industry. Healthcare sectors become largest consumer of energy in the modern world. Based on data of American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE), a moderate hospital consume 2.5 times higher energy than commercial buildings. As a result, increased worries about energy costs and environmental issues, as well as the anticipation of rising energy prices in the future and the need to enhance the dependability of healthcare facilities, have led to a focus on in-house power generation systems and the importance of energy management in hospitals and their health care facilities. Solar power systems that are clean and ecologically friendly have grown in popularity as distributed power generation (DPG) systems in recent years. In this work, a Grid-tied Solar PV system incorporated with Battery energy storage technology is considered in conjunction with health informatics and the hospital Energy Management System reduces energy consumption cost and improves the reliability of the power supply to run all clinical equipment available in the hospital’s Intensive Care Unit (ICU) and other premises. In this context, the Energy management controller utilised in the hospital Energy Management System will effectively use the electricity supplied by the Solar PV system while minimising grid demand and stabilising the voltage in the DC bus, which must be inverted into AC using an inverter to feed clinical loads. Furthermore the maximum power point tracking method is adopted, which enhances the quality of DC voltage generated by solar PV panels and feed to the DC bus. Sliding mode controller (SMC) is adopted in the inverter side and the quality of the inverted voltage is optimized using artificial bee colony (ABC) method. The proposed solar PV system in conjunction with health informatics and the hospital Energy Management System is developed and simulated in the MATLAB Simulink. The response of the suggested SMC and ABC techniques are compared and their outcomes are shown to confirm the performance of hospital energy management system.

Considering Environmental Health and Energy Resources to Design Transformers

Background: Environmental health has become a worldwide debating issue among researchers, scientists, and governments. Although fossil fuels have been the greatest energy resources for the human, their consumption leads to injecting greenhouse gases into the air, which can affect the existence of all living species dramatically. In fact, fossil fuels consumption pollutes the environment because of the injection of greenhouse gases which result in global warming. On the other hand, reductions in fossil fuel and drinking water resources have highlighted the importance of energy management and loss reduction in brand-new management strategies and manufacturing methods. Distribution transformers are one of the most used devices in power distribution networks. Hence, it seems to be logical to consider transformer losses as a definitive factor in design and construction procedures. Furthermore, such design procedures require powerful tools to solve complex non-linear equations and find the best solutions in the shortest time. Researchers and scientists have always had a great challenge regarding finding the best solutions for their analysis. In actuality, it is hard to solve complex non-linear problems by means of traditional calculation methods even if a researcher employs a powerful computer. For this reason, metaheuristic optimization algorithms have found great popularity among scientists. These algorithms seek the best solution through a solution pool at an appropriate pace. Therefore, a researcher can save more time and energy in solving an intricate problem. Objective: In this paper, the authors aim to modify the conventional Teaching-Learning Based Optimization (TLBO) algorithm to design an optimum distribution transformer by consideration of the transformer Total Owning Cost (TOC). The TOC consists of operational and initial manufacturing costs of a distribution transformer. The used raw materials affect the manufacturing cost and it would be decreased if the copper and iron volumes are reduced in the construction instruction. However, the operational cost that includes iron and copper losses must not be forgotten in design analysis. Indeed, loss consideration is of great significance to keep energy from frittering away and as a result, protects environmental resources. Materials and Methods: A novel approach based on an optimization technique for distribution transformer design problems is presented in this paper. The entire expenses of a transformer consist of transformer construction and operating costs. In other words, by reducing copper and iron volumes, the initial cost of a transformer will be decreased. Due to the electromagnetic and electrical losses in transformers, the initial cost of a transformer is not its entire design problem and the operating cost must be considered in the design algorithm. Appropriate limits on efficiency, voltage regulation, temperature rise, no-load current and winding fill factor are the constraints on the transformer design problems in the international standards. With respect to these constraints, the transformer designer can minimize the volume of the core and windings. In this paper, the Conditional Teaching-Learning Based Optimization (CTLBO) algorithm determines the appropriate transformer properties, while transformer construction cost and its operating cost are selected as an objective function for optimization method. In order to attain a suitable voltage regulation, transformer impedance is chosen as an optimization constraint. Results: The result of the paper demonstrates the proposed algorithm ability in reducing the Total Owning Cost (TOC) during transformer lifetime, which could be useful for energy distribution companies. In addition, the result analysis proves that the total losses of the transformer are reduced by the proposed approach in comparison to conventional design techniques. Then, more energy will be saved in the power grid when the proposed transformer is utilized in the power network. Conclusion: In this paper, a suitable method to design an optimum distribution transformer is proposed which enables manufacturers to construct their product based on the proposed method. In this way, it can be claimed that not only more money will be saved during the transformer operation, but also the energy consumption will be decreased drastically. Therefore, world resources will remain for future generations.

Deep learning for load forecasting with smart meter data: Online Adaptive Recurrent Neural Network

Electricity load forecasting has been attracting research and industry attention because of its importance for energy management, infrastructure planning, and budgeting. In recent years, the proliferation of smart meters and other sensors has created new opportunities for sensor-based load forecasting on the building and even individual household level. Machine learning approaches such as Recurrent Neural Networks (RNNs) have shown great successes in load forecasting, but these approaches employ offline learning: they are trained once and miss on the opportunity to learn from newly arriving data. Moreover, they are not well suited for handling the concept drift; for example, their predictive performance will degrade if the load changes due to the installation of new equipment. Consequently, this paper proposes Online Adaptive RNN, an approach for load forecasting capable of continuously learning from newly arriving data and adapting to new patterns. RNN is employed to capture time dependencies while the online aspect is achieved by updating the RNN weights according to new data. The performance is monitored; if it degrades, online tuning is activated to adapt the RNN hyperparameters to changes in data. The proposed approach was evaluated with data from five individual homes: the results show that the proposed approach achieves higher accuracy than the standalone offline long short term memory network and five other online algorithms. Moreover, the time to learn from new samples is only a fraction of the time needed to re-train the offline model.

Multi-objective Optimization to Increase Nusselt Number and Reduce Friction Coefficient of Water/Carbon Nanotubes via NSGA II using Response Surface Methodology

Heat transfer science is one of the most important and most applied engineering sciences, with the importance of energy management and energy conservation being doubled. Because of their properties, nanofluids have been widely used in various industries, making them particularly important to study. In this paper, the Nusselt number and coefficient of friction with volume fraction ranging from 0 to 0.1 at approximately Reynolds numbers of 200 to 5000 are studied experimentally. Higher thermal conductivity, better stability, lower pressure drop was observed using nanoparticles of solid particles. NSGA II algorithm was used to maximize Nusselt number and minimum friction coefficient by changing temperature and volume fraction of nanoparticles. To obtain Nusselt number and friction coefficient based on the temperature and volume fraction of the nanoparticles, the experimental data response surface methodology was used and with increasing Reynolds number, the Nusselt number increased and the friction coefficient decreased. In order to evaluate the objective functions in the optimization, the response surface methodology is attached to the optimization algorithm. At the end, the Pareto Front and its corresponding optimal points are presented.

A Widespread Review of Smart Grids Towards Smart Cities

Nowadays, the importance of energy management and optimization by means of smart devices has arisen as an important issue. On the other hand, the intelligent application of smart devices stands as a key element in establishing smart cities, which have been suggested as the solution to complicated future urbanization difficulties in coming years. Considering the scarcity of traditional fossil fuels in the near future, besides their ecological problems the new smart grids have demonstrated the potential to merge the non-renewable and renewable energy resources into each other leading to the reduction of environmental problems and optimizing operating costs. The current paper clarifies the importance of smart grids in launching smart cities by reviewing the advancement of micro/nano grids, applications of renewable energies, energy-storage technologies, smart water grids in smart cities. Additionally a review of the major European smart city projects has been carried out. These will offer a wider vision for researchers in the operation, monitoring, control and audit of smart-grid systems.

Millisecond methane steam reforming for hydrogen production: A computational fluid dynamics study

The potential of methane steam reforming to produce hydrogen in thermally integrated micro-chemical systems at short contact times was theoretically explored. Methane steam reforming coupled with methane catalytic combustion in microchannel reactors for hydrogen production was studied numerically. A two-dimensional computational fluid dynamics model with detailed chemistry and transport was developed. To provide guidelines for optimal design, reactor behavior was studied, and the effect of design parameters such as catalyst loading, channel height, and flow arrangement was evaluated. To understand how steam reforming can happen at millisecond contact times, the relevant process time scales were analyzed, and a heat and mass transfer analysis was performed. The importance of energy management was also discussed in order to obtain a better understanding of the mechanism responsible for efficient heat exchange between highly exothermic and endothermic reactions. The results demonstrated the feasibility of the design of millisecond reforming systems, but only under certain conditions. To achieve this goal, process intensification through miniaturization and the improvement in catalyst performance is very important, but not sufficient; very careful design and implementation of the system is also necessary to enable high thermal integration. The channel height plays an important role in determining the efficiency of heat exchange. A proper balance of the flow rates of the combustible and reforming streams is an important design criterion. Reactor performance is significantly affected by flow arrangement, and co-current operation is recommended to achieve a good energy balance within the system. The catalyst loading must be carefully designed to avoid insufficient reactant conversion or hot spots. Finally, operating windows were identified, and engineering maps for designing devices with desired power were constructed.

Acceptability of Sustainable Green Practices: Perception of Electrical & Electronics Manufacturing SMEs

Business and legal aspects of environmental management have developed greatly and generated huge interest in recent decades. Organisations are rapidly changing their structures, systems, work process and activities. This changing environment calls for Small Medium Enterprises (SMEs) managers to manage and respond to the changes in appropriate manner. Despite of wide-range of studies over the last decade, the question on whether green initiatives complement SMEs sustainable green practices remain inconclusive. Anchoring to the arguments on the tenets of sustainable green practices, the present study leads to determine the pattern and dimension of green initiatives consists of energy management, water conservation and waste management within Electrical and Electronics (E&E) manufacturing SMEs, as well as examining the influences of Green Technology policy awareness in building sustainable green practices. A questionnaire based survey collected data from managers of 195 E&E manufacturing SMEs. Results indicate that E&E manufacturing SMEs managers aware and recognised the importance of energy management, water conservation, waste management towards sustainable green practices. The findings also indicate that E&E manufacturing SMEs in Malaysia are still not aware and not perceived Green Technology policy as an important factor towards sustainable green practices. An environmental management monitoring, education, training systems and policy recommendation was formulated to promote sustainable green practices in E&E manufacturing SMEs and other SMEs to hop on the Go-green bandwagon, particularly those without any green practice in place.

Improved energy efficiency within the Swedish steel industry—the importance of energy management and networking

The iron and steel industry is an energy-intensive industry that consumes a significant portion of fossil fuel and electricity production. Climate change, the threat of an unsecure energy supply and rising energy prices have emphasised the issue of improved energy efficiency in the iron and steel industry. However, an energy-efficiency gap is well recognised, i.e. cost-efficient measures are not implemented in practice. This study will go deeper into why this gap occurs by investigating energy-management practices at 11 iron and steel companies in Sweden. Energy managers at the steel plants were interviewed about how they perceived their own and their companies’ efforts to improve energy efficiency and how networking among energy managers influenced the efforts to improve energy efficiency. Reported barriers to improved energy efficiency were, for example, too long payback period, lack of profitability, lack of personnel, risk of production disruption, lack of time and lack of commitment. Only three out of the eleven companies had assigned a person to work full time with energy management, and some of the energy managers were frustrated with not having enough time to work with energy issues. Generally, the respondents felt that they had support from senior management and that energy issues were prioritised, but only a few of the companies had made great efforts to involve employees in improving energy efficiency. Networking among Swedish steel companies was administered by the Swedish Steel Producers’ Association, and their networking meetings contributed to the exchange of knowledge and ideas. In conclusion, Swedish steel companies regard improved energy efficiency as important but have much work left to do in this area. For example, vast amounts of excess heat are not being recovered and more efforts could be put into engaging employees and introducing a culture of energy efficiency.

Importance of energy management in desiccated coconut industry

Energy flow rates across commonly used desiccators and associated heat transfer equipment are quantified. It is seen that approximately 726 percent excess heat energy over the actual requirement is presently used for drying in desiccators. Approximately 49 percent of heat energy is exhausted to the atmosphere through the chimney and approximately 23 percent along with the desiccator exhaust gases. Approximately 40 percent of energy could be saved by introducing recirculation techniques of heat recovery. This saving represents about 83.2 million kwh of energy for 1981 alone, which is an equivalent of 4.5 percent of the total electrical energy generated in Sri Lanka in 1981. COCOS, (1984) 2 18-28 doi: 10.4038/cocos.v2i0.812

Energy-aware disk scheduling for soft real-time I/O requests

In this work, we develop energy-aware disk scheduling algorithm for soft real-time I/O. Energy consumption is one of the major factors which bar the adoption of hard disk in mobile environment. Heat dissipation of large scale storage system also calls for an energy-aware scheduling technique to further increase the storage density. The basic idea in this work is to properly determine the I/O burst size so that device can be in standby mode between consecutive I/O bursts and that it can satisfy the soft real-time requirement. We develop an elaborate model which incorporates the energy consumption characteristics, overhead of mode transition in determining the appropriate I/O burst size and the respective disk operating schedule. Efficacy of energy-aware disk scheduling algorithm greatly relies on not only disk scheduling algorithm itself but also various operating system and device firmware related concerns. It is crucial that the various operating system level and device level features need to be properly addressed within disk scheduling framework. Our energy-aware disk scheduling algorithm successfully addresses a number of outstanding issues. First, we examine the effect of OS and hard disk firmware level prefetch policy and incorporate its effect in our disk scheduling framework. Second, our energy aware scheduling framework can allocate a certain fraction of disk bandwidth to handle sporadically arriving non real-time I/O's. Third, we examine the relationship between lock granularity of the buffer management and energy consumption. We develop a prototype software with energy-aware scheduling algorithm. In our experiment, proposed algorithm can reduce the energy consumption to one fourth if we use energy-aware disk scheduling algorithm. However, energy-aware disk scheduling algorithm increases buffer requirement significantly, e.g., from 4 to 140 KByte. We carefully argue that the buffer overhead is still justifiable given the cost of DRAM chip and importance of energy management in modern mobile devices. The result of our work not only provides the energy efficient scheduling algorithm but also provides an important guideline in capacity planning of future energy efficient mobile devices.