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1
Reddy, V. Sandeep Kumar, Saravanan T., N. T. Velusudha, and T. Sunder Selwyn. "Smart Grid Management System Based on Machine Learning Algorithms for Efficient Energy Distribution." E3S Web of Conferences 387 (2023): 02005. http://dx.doi.org/10.1051/e3sconf/202338702005.
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This abstract describes the smart grid management system is an emerging technology that utilizes machine learning algorithms for efficient energy distribution. The paper presents an overview of the architecture, benefits, and challenges of smart grid management systems. The paper also discusses various machine learning algorithms used in smart grid management systems such as neural networks, decision trees, and Support Vector Machines (SVM). The advantages of using machine learning algorithms in smart grid management systems include increased energy efficiency, reduced energy wastage, improved reliability, and reduced costs. The challenges in implementing machine learning algorithms in smart grid management systems include data security, privacy, and scalability. The paper concludes by discussing future research directions in smart grid management systems based on machine learning algorithms.
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Husainy, Avesahemad S. N., Sairam A. Patil, Atharva S. Sinfal, Vasim M. Mujawar, and Chandrashekhar S. Sinfal. "Parameter Optimization of Refrigeration Chiller by Machine Learning." Asian Journal of Electrical Sciences 12, no. 1 (June 22, 2023): 39–45. http://dx.doi.org/10.51983/ajes-2023.12.1.3684.
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The implementation of machine learning in a chiller system provides several benefits. It can improve energy efficiency by optimizing chiller operation based on predicted load requirements. It can enhance system reliability and reduce maintenance costs by detecting and diagnosing faults in advance. Furthermore, it can enable data-driven decision-making, enabling operators to make informed choices based on accurate predictions and insights. This implementation aims to leverage machine learning techniques to optimize the performance and energy efficiency of a chiller system. Chiller systems are widely used in various industries for cooling purposes, and their efficient operation is critical to reducing energy consumption and operational costs. By employing machine learning algorithms, this implementation aims to analyze historical data, understand patterns, and develop predictive models to optimize chiller system performance. The implementation process involves several steps. First, historical data from the chiller system, including sensor measurements, operating parameters and energy consumption, is collected and preprocessed. The data is then split into training and testing sets. Next, suitable machine learning algorithms, such as regression, classification, or time-series forecasting models, are selected based on the specific goals and requirements of the chiller system. Overall, this implementation demonstrates the potential of machine learning to optimize chiller system performance, reduce energy consumption, and improve operational efficiency. By leveraging historical data and advanced analytics, machine learning can play a crucial role in transforming traditional chiller systems into intelligent, adaptive, and energy-efficient cooling solutions.
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Wu, Qingying, Benjamin K. Ng, and Chan-Tong Lam. "Energy-Efficient Cooperative Spectrum Sensing Using Machine Learning Algorithm." Sensors 22, no. 21 (October 27, 2022): 8230. http://dx.doi.org/10.3390/s22218230.
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Cognitive Radio (CR) is a practical technique for overcoming spectrum inefficiencies by sensing and utilizing spectrum holes over a wide spectrum. In particular, cooperative spectrum sensing (CSS) determines the state of primary users (PUs) by cooperating with multiple secondary users (SUs) distributed around a Cognitive Radio Network (CRN), further overcoming various noise and fading issues in the radio environment. But it’s still challenging to balance energy efficiency and good sensing performances in the existing CSS system, especially when the CRN consists of battery-limited sensors. This article investigates the application of machine learning technologies for cooperative spectrum sensing, especially through solving a multi-dimensional optimization that cannot be readily addressed by traditional approaches. Specifically, we develop a neural network, which involves parameters that are integral to the CSS performance, including a device sleeping rate for each sensor and thresholds used in the energy detection method, and a customized loss function based on the energy consumption of the CSS system and multiple penalty terms reflecting the system requirements. Using this formulation, energy consumption is to be minimized with the guarantee of reaching a certain probability of false alarm and detection in the CSS system. With the proposed method, comparison studies under different hard fusion rules (‘OR’ and ‘AND’) demonstrate its effectiveness in improving the CSS system performances, as well as its robustness in the face of changing global requirements. This paper also suggests the combination of the traditional and the proposed scheme to circumvent the respective inherent pitfalls of neural networks and the traditional semi-analytic methods.
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Zhang, Huanhuan, Jigeng Li, and Mengna Hong. "Machine Learning-Based Energy System Model for Tissue Paper Machines." Processes 9, no. 4 (April 9, 2021): 655. http://dx.doi.org/10.3390/pr9040655.
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With the global energy crisis and environmental pollution intensifying, tissue papermaking enterprises urgently need to save energy. The energy consumption model is essential for the energy saving of tissue paper machines. The energy consumption of tissue paper machine is very complicated, and the workload and difficulty of using the mechanism model to establish the energy consumption model of tissue paper machine are very large. Therefore, this article aims to build an empirical energy consumption model for tissue paper machines. The energy consumption of this model includes electricity consumption and steam consumption. Since the process parameters have a great influence on the energy consumption of the tissue paper machines, this study uses three methods: linear regression, artificial neural network and extreme gradient boosting tree to establish the relationship between process parameters and power consumption, and process parameters and steam consumption. Then, the best power consumption model and the best steam consumption model are selected from the models established by linear regression, artificial neural network and the extreme gradient boosting tree. Further, they are combined into the energy consumption model of the tissue paper machine. Finally, the models established by the three methods are evaluated. The experimental results show that using the empirical model for tissue paper machine energy consumption modeling is feasible. The result also indicates that the power consumption model and steam consumption model established by the extreme gradient boosting tree are better than the models established by linear regression and artificial neural network. The experimental results show that the power consumption model and steam consumption model established by the extreme gradient boosting tree are better than the models established by linear regression and artificial neural network. The mean absolute percentage error of the electricity consumption model and the steam consumption model built by the extreme gradient boosting tree is approximately 2.72 and 1.87, respectively. The root mean square errors of these two models are about 4.74 and 0.03, respectively. The result also indicates that using the empirical model for tissue paper machine energy consumption modeling is feasible, and the extreme gradient boosting tree is an efficient method for modeling energy consumption of tissue paper machines.
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Nour, Samar, Shahira Habashy, and Sameh Salem. "Energy-Efficient Cache Partitioning Using Machine Learning for Embedded Systems." Jordan Journal of Electrical Engineering 9, no. 3 (2023): 285. http://dx.doi.org/10.5455/jjee.204-1669909560.
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Nowadays, embedded device applications have become partially correlated and can share platform resources. Cross-execution and sharing resources can cause memory access conflicts, especially in the Last Level Cache (LLC). LLC is a promising candidate for improving system performance on multicore embedded systems. It leads to a reduction in the number of high-latency main memory accesses. Currently, commercial devices can use cache partitioning. The software could better utilize the LLC and conserve energy by caching. This paper proposes a new energy-optimization model for embedded multicore systems based on a reconfigurable artificial neural network LLC architecture. The proposed model uses a machine-learning approach to express the reconfiguration of LLC, and can predict each task’s next interval LLC partitioning factor at runtime. The obtained experimental results reveal that the proposed model - compared to other algorithms - improves energy consumption by 28%, and gives 33% reduction in the LLC miss rate.
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Ismail, Mahmoud M. "A Machine Learning Approach for Energy-Efficient IoT Systems." Journal of Intelligent Systems and Internet of Things 1, no. 1 (2020): 61–69. http://dx.doi.org/10.54216/jisiot.010105.
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The energy challenge in IoT refers to the significant energy consumption of IoT devices, which can lead to sustainability issues, shorter battery life, and increased operating costs. IoT devices are known for their high energy consumption, and optimizing their energy usage can have a significant impact on sustainability and cost. Machine learning (ML) can learn from data and patterns to predict and control energy consumption in IoT systems, making them more energy efficient. The main contribution of this paper is the establishment of a novel deep learning framework for enhanced predictive modeling of energy consumption in IoT networks to help realize Energy-efficient IoT systems. our framework applies recurrent processing to capture long-term relations in the energy consumption of IoT appliances. Then, the self-attention mechanism is devised to help the model to focus on important predictive features. Simulation experiments against the competing ML baselines demonstrate the predictive capability of our framework.
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Waqas Khan, Prince, Yung-Cheol Byun, Sang-Joon Lee, and Namje Park. "Machine Learning Based Hybrid System for Imputation and Efficient Energy Demand Forecasting." Energies 13, no. 11 (May 26, 2020): 2681. http://dx.doi.org/10.3390/en13112681.
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The ongoing upsurge of deep learning and artificial intelligence methodologies manifest incredible accomplishment in a broad scope of assessing issues in different industries, including the energy sector. In this article, we have presented a hybrid energy forecasting model based on machine learning techniques. It is based on the three machine learning algorithms: extreme gradient boosting, categorical boosting, and random forest method. Usually, machine learning algorithms focus on fine-tuning the hyperparameters, but our proposed hybrid algorithm focuses on the preprocessing using feature engineering to improve forecasting. We also focus on the way to impute a significant data gap and its effect on predicting. The forecasting exactness of the proposed model is evaluated using the regression score, and it depicts that the proposed model, with an R-squared of 0.9212, is more accurate than existing models. For the testing purpose of the proposed energy consumption forecasting model, we have used the actual dataset of South Korea’s hourly energy consumption. The proposed model can be used for any other dataset as well. This research result will provide a scientific premise for the strategy modification of energy supply and demand.
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Dixit, Abhishek, and Santosh Kumar. "Machine Learning Based Efficient Protection Scheme for AC Microgrid." INTERNATIONAL RESEARCH JOURNAL OF ENGINEERING & APPLIED SCIENCES 10, no. 4 (December 31, 2022): 18–23. http://dx.doi.org/10.55083/irjeas.2022.v10i04009.
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Micro grids have become popular as a way to reduce carbon emissions and use nonrenewable energy sources to produce power. Microgrids allow users to generate and regulate energy as needed, reducing their reliance on the utility grid. They may also sell excess electricity to the grid and make money. Due to its simple design, fast installation, and easy maintenance, photovoltaic systems are a vital microgrid resource. Microgrids threaten the reliability and optimum functioning of major power grids. It's crucial to discover defects early and fix them before catastrophic system breakdown. This research proposes a unique method based on Discrete wavelet transform and ensemble of Decision tree classifier for detecting and classifying microgrid faults. Once the particular fault type is recognised and categorised, a suitable protective strategy may be used to address it early, enhancing the system's overall safety.
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Khan, Murad, Junho Seo, and Dongkyun Kim. "Towards Energy Efficient Home Automation: A Deep Learning Approach." Sensors 20, no. 24 (December 15, 2020): 7187. http://dx.doi.org/10.3390/s20247187.
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Home Automation Systems (HAS) attracted much attention during the last decade due to the developments in new wireless technologies, such as Bluetooth 4.0, 5G, WiFi 6, etc. In order to enable automation as a service in smart homes, a number of challenges must be addressed, such as fulfilling the electrical energy demands, scheduling the operational time of appliances, applying machine learning models in real-time, optimal human appliances interaction, etc. In order to address the aforementioned challenges and control the wastage of energy due to the lifestyle of the home users, we propose a system for automatically controlling the energy consumption by employing machine and deep learning techniques to smart home networks. The proposed system works in three phases, (1) feature extraction and classification based on 1-dimensional Deep Convolutional Neural Network (1D-DCNN) which extract important energy patterns from the historic energy data, (2) a load forecasting system based on Long-short Term Memory (LSTM) is proposed to forecast the load based on the extracted features in phase 1 and (3) a scheduling algorithm based on the forecasted data obtained from phase 2 is designed to schedule the operational time of smart home appliances. The proposed scheme efficiently automates the smart home appliances to consume less energy while adapting to the lifestyle of smart home users. The validation of the proposed scheme is tested with a number of simulation scenarios incorporating datasets from authentic data sources. The simulation results show that the proposed smart home automation system can be a game-changer in fulfilling the energy demands of the home users without installing renewable and other energy sources in the future.
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Lee, Jin-Hyun, Hye-In Lee, Kyoung-Hwan Ji, and Young-Hum Cho. "Optimal Economizer Control of VAV System using Machine Learning." E3S Web of Conferences 396 (2023): 03034. http://dx.doi.org/10.1051/e3sconf/202339603034.
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Energy efficiency of the HVAC system can be improved through system renovation and operating method improvement. Economizer control, one of the energy efficient measures through improvement of operating method, introduces outdoor air when outdoor air is sufficient for cooling. There are high/low limit that determine the range of economizer control and mixed air temperature as control set-points. Economizer is controlled with the user's or manager's experience, and the set-point is operated fixed. This causes problems energy waste because it does not consider indoor and outdoor environments. Therefore, the purpose of this study is to develop an optimal economizer control of VAV system that resetting the set-point considering the indoor and outdoor environments. To this, a machine learning model was used to develop a model that predicts the future state based on the current state. Based on the developed prediction model, the optimal economizer control of VAV system that resets the mixed air temperature set-point in real time was developed and the control method was evaluated through simulation. As a result, it was confirmed that the mixed air temperature set-point changed in real time, and that about 20% of energy consumption was saved compared to the existing dry- bulb temperature control.
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Lokesh, Nitish, and Dr Pawan Kumar. "Billing System using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 1420–26. http://dx.doi.org/10.22214/ijraset.2022.41546.
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Abstract: The system of using pre-made bar codes to identify a product during its billing process is time-consuming and labour intensive. The relatively unique barcode needs to be first produced, then it must be manually attached to the product. This requires a lot of pre-processing work on the products to make them ready for identification and classification. This paper presents an alternate system that works on the principle of using the products’ natural characteristics like its discrete and distinguishable looks to identify and classify them during the billing process. It’s mimicking the human way of identifying and distinguishing the products. To implement this system, we need to move away from conventional methods of programming and use a different paradigm for designing software systems based on an artificial intelligence concept i.e., machine learning. We use machine learning techniques to design the working philosophy of this system. The algorithms in Deep Neural Networks which is a part of Artificial Neural Networks, help in creating a model to base our software system’s operational logic. Especially the models based on Convolutional Neural Networks have been proven to be efficient in providing a model for image classification. This paper discusses the abstracted software system from the base billing process without worrying about the hardware environment. We choose Python and its web framework Django to design the UI to implement this system over a distributed network within any establishment that needs to incorporate this process so that each node that has to process billing need not have to adhere to the hardware requirements imposed on them to run the various CNN models which are reliant on the GPU-based tensor architecture of TensorFlow. The system also provides mechanisms for inventory management over distributed networks and simple data analytics based on local sales.
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Kang, Minseon, Yongseok Lee, and Moonju Park. "Energy Efficiency of Machine Learning in Embedded Systems Using Neuromorphic Hardware." Electronics 9, no. 7 (June 30, 2020): 1069. http://dx.doi.org/10.3390/electronics9071069.
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Recently, the application of machine learning on embedded systems has drawn interest in both the research community and industry because embedded systems located at the edge can produce a faster response and reduce network load. However, software implementation of neural networks on Central Processing Units (CPUs) is considered infeasible in embedded systems due to limited power supply. To accelerate AI processing, the many-core Graphics Processing Unit (GPU) has been a preferred device to the CPU. However, its energy efficiency is not still considered to be good enough for embedded systems. Among other approaches for machine learning on embedded systems, neuromorphic processing chips are expected to be less power-consuming and overcome the memory bottleneck. In this work, we implemented a pedestrian image detection system on an embedded device using a commercially available neuromorphic chip, NM500, which is based on NeuroMem technology. The NM500 processing time and the power consumption were measured as the number of chips was increased from one to seven, and they were compared to those of a multicore CPU system and a GPU-accelerated embedded system. The results show that NM500 is more efficient in terms of energy required to process data for both learning and classification than the GPU-accelerated system or the multicore CPU system. Additionally, limits and possible improvement of the current NM500 are identified based on the experimental results.
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Sankar, Sasirekha, S. Amudha, P. Madhavan, and Dev Krishna Lamba. "Energy Efficient Medium-Term Wind Speed Prediction System using Machine Learning Models." IOP Conference Series: Materials Science and Engineering 1130, no. 1 (April 1, 2021): 012085. http://dx.doi.org/10.1088/1757-899x/1130/1/012085.
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Trivedi, Vijay K. "Energy Aware Routing Protocol with Data Fusion and Machine Learning." International Journal of Wireless and Ad Hoc Communication 5, no. 1 (2022): 22–35. http://dx.doi.org/10.54216/ijwac.050102.
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The current wireless and communication system may be attributed to the contributions made by the Sensor Network in a significant measure. During the last decade, several efforts have been performed to examine and propose answers to challenges about the energy efficiency of wireless sensor network communications. Several different researchers has done these efforts. The challenge of constructing economical energy-use paths has not yet been overcome. Because sensors have limited computational capabilities, which are frequently coupled with energy limitations, it is rather difficult to guarantee that a sensor’s lifespan will be longer. This is because of the energy constraints often associated with these limitations. The results of this research have led to the development of a one-of-a-kind communication system for sensor networks that is not only environmentally friendly but also supported by three distinct revolutionary frameworks. The framework that has been recommended, which goes by the name Potential Energy Efficient Data Fusion (PEE-DF), is the one that is in charge of the optimization of energy. It achieves this with the aid of probabilistic approaches and clustering. The K-SOM (Korhonen self-organizing map) framework was designed using a globular topology, which aids load balancing during data fusion. K-SOM stands for Korhonen self-organizing map. This was done to ensure we got the most out of our resources. A novel method to routing is presented by the technique, which has the potential to be used to assist in the operation of energy-efficient routing in large-scale wireless sensor networks. The framework for the Tree-Based Fusion Technique (TBFT), which has been offered, comes up with a new way for dynamic reconfiguration. This is accomplished via the introduction of the concept of routing agents. The strategy enables the system to recognise which sensor has a greater energy dissipation rate and then instantly moves data fusion work to a more energy-efficient node. This allows the system to save energy. This approach, based on thresholds, enable a sensor to act as a cluster head up until it reaches its threshold remnant energy and then as a member node once it exceeds threshold residual energy. In other words, it may play both roles simultaneously. It is possible to fulfil both of these responsibilities at the same time. The findings have been mathematically modelled using a standard radio-energy model, which has enhanced the robustness of the findings, which is highly positive. The results were encouraging because of the increased robustness of the findings. Compared to the benchmark previously established for energy-efficient strategies, the proposed system demonstrates higher performance in terms of its ability to communicate while using less energy. In contrast to LEACH, the recommended system's findings reveal an almost fifty percent decrease in energy consumption, and at the same time, a reduction in the amount of time required to carry out the operation..
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Talei, Hanaa, Driss Benhaddou, Carlos Gamarra, Houda Benbrahim, and Mohamed Essaaidi. "Smart Building Energy Inefficiencies Detection through Time Series Analysis and Unsupervised Machine Learning." Energies 14, no. 19 (September 23, 2021): 6042. http://dx.doi.org/10.3390/en14196042.
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The climate of Houston, classified as a humid subtropical climate with tropical influences, makes the heating, ventilation, and air conditioning (HVAC) systems the largest electricity consumers in buildings. HVAC systems in commercial buildings are usually operated by a centralized control system and/or an energy management system based on a fixed schedule and scheduled control of a zone setpoint, which is not appropriate for many buildings with changing occupancy rates. Lately, as part of energy efficiency analysis, attention has focused on collecting and analyzing smart meters and building-related data, as well as applying supervised learning techniques, to propose new strategies to operate HVAC systems and reduce energy consumption. On the other hand, unsupervised learning techniques have been used to study the consumption information and profile characterization of different buildings after cluster analysis is performed. This paper adopts a different approach by revealing the power of unsupervised learning to cluster data and unveiling hidden patterns. In this study, we also identify energy inefficiencies after exploring the cluster results of a single building’s HVAC consumption data and building usage data as part of the energy efficiency analysis. Time series analysis and the K-means clustering algorithm are successfully applied to identify new energy-saving opportunities in a highly efficient office building located in the Houston area (TX, USA). The paper uses 1-year data from a highly efficient Leadership in Energy and Environment Design (LEED)-, Energy Star-, and Net Zero-certified building, showing a potential energy savings of 6% using the K-means algorithm. The results show that clustering is instrumental in helping building managers identify potential additional energy savings.
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Darus, Muhamad Firdaus, Fakrulradzi Idris, and Norlezah Hashim. "Energy-efficient non-orthogonal multiple access for wireless communication system." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 2 (April 1, 2023): 1654. http://dx.doi.org/10.11591/ijece.v13i2.pp1654-1668.
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<p>Non-orthogonal multiple access (NOMA) has been recognized as a potential solution for enhancing the throughput of next-generation wireless communications. NOMA is a potential option for 5G networks due to its superiority in providing better spectrum efficiency (SE) compared to orthogonal multiple access (OMA). From the perspective of green communication, energy efficiency (EE) has become a new performance indicator. A systematic literature review is conducted to investigate the available energy efficient approach researchers have employed in NOMA. We identified 19 subcategories related to EE in NOMA out of 108 publications where 92 publications are from the IEEE website. To help the reader comprehend, a summary for each category is explained and elaborated in detail. From the literature review, it had been observed that NOMA can enhance the EE of wireless communication systems. At the end of this survey, future research particularly in machine learning algorithms such as reinforcement learning (RL) and deep reinforcement learning (DRL) for NOMA are also discussed.</p>
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K., Babu, Sivasubramanian S., Nivetha C.S., Senthil Kumar R., and Mohana Soundari. "Intelligent Energy Management System for Smart Grids Using Machine Learning Algorithms." E3S Web of Conferences 387 (2023): 05004. http://dx.doi.org/10.1051/e3sconf/202338705004.
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Smart grid technology is rapidly advancing and providing various opportunities for efficient energy management. To achieve the full potential of smart grids, intelligent energy management systems (IEMS) are required that can optimally manage and control the distributed energy resources (DERs). In this paper, proposed an IEMS using the Deep Reinforcement Learning (DRL) algorithm to manage the energy consumption and production in a smart grid. The proposed methodology aims to minimize the energy cost while maintaining the stability and reliability of the grid. The performance of the proposed IEMS is evaluated on a simulated smart grid, and the results show that it can effectively manage the energy resources while minimizing the energy cost.
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Caliskan, Abdullah, Conor O’Brien, Krishna Panduru, Joseph Walsh, and Daniel Riordan. "An Efficient Siamese Network and Transfer Learning-Based Predictive Maintenance System for More Sustainable Manufacturing." Sustainability 15, no. 12 (June 8, 2023): 9272. http://dx.doi.org/10.3390/su15129272.
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Legacy machinery poses a specific challenge when integrated into modern manufacturing lines. While modern machinery provides swift methods of integration and inbuilt predictive maintenance (PdM), older machines, while physically fully functional, are less attractive to reuse, a specific reason being their lack of ready-to-implement PdM hardware and models. More sustainable manufacturing operations can be achieved if the useable lifespan of functional older machinery can be extended through retrofittable PdM and modern industrial communication systems. While PdM models can be developed for a class (make/model) of machine with retrofitted sensing, it is often found that legacy machines will deviate greatly from their original form, through nonstandard maintenance and component replacement actions during their lengthy lifespan. This would mean that each legacy machine would require a custom PdM model, a cost often leading to the removal or nonusage of legacy machines. This paper proposes a framework designed for the generation of an efficient PdM algorithm which would allow for the reuse of legacy machines retrofitted with low-cost sensing in modern manufacturing for increased sustainability. Given a limited number of data samples collected from a machine to be maintained, we aim to predict a failure or/and maintenance time by making use of the difference between the characteristics of the variation of the healthy and unhealthy data collected from the machine. We measure the healthiness of the machine by using a Siamese network trained with a public dataset and fine-tuned with data samples obtained from machines with similar characteristics. Although we use different training and testing datasets coming from completely different sources, we obtain reasonable results thanks to the proposed technique. The results of simulations and the statistical analysis enable us to devise a transfer learning technique and a Siamese network employed for failure detection in the machine. The proposed system will allow for the continued use of older machines in modern facilities, enabling more sustainable manufacturing models.
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M.Prakash, U., Priyanshu Madan, K. R. GokulAnand, and S. Prabhakaran. "Intelligent Lighting System and Garbage Monitoring System." International Journal of Engineering & Technology 7, no. 3.12 (July 20, 2018): 876. http://dx.doi.org/10.14419/ijet.v7i3.12.16554.
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This paper aims at designing a system that focusses on two major societal issues in India i.e. improper garbage management and loss of energy from existing street lighting systems. We designed an intelligent lighting system Using embedded systems and machine learning algorithm we predict the environmental lighting conditions and accordingly change the behavior of the street lights. The present street lighting systems use timer or manual interaction to turn on and off the lights. But these methods are not energy efficient. In our model, we used Light Dependent Resistor LDR and algorithm to eliminate those drawbacks. In Garbage monitoring system, alerting the concerned authorities about the level of garbage collected was the most important aspect. In this system, an Ultrasonic sensor along with machine learning algorithm was used to solve the above situation.
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A., Nagesh*. "Energy Audit System for Households using Machine Learning." Regular issue 10, no. 7 (May 30, 2021): 33–36. http://dx.doi.org/10.35940/ijitee.g8895.0510721.
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the growth in population and economics the global demand for energy is increased considerably. The large amount of energy demand comes from houses. Because of this the energy efficiency in houses in considered most important aspect towards the global sustainability. The machine learning algorithms contributed heavily in predicting the amount of energy consumed in household level. In this paper, a energy audit system using machine learning are developed to estimate the amount of energy consumed at household level in order to identify probable areas to plug wastage of energy in household. Each energy audit system is trained using one machine leaning algorithm with previous power consumption history of training data. By converting this data into knowledge, gratification of analysis of energy consumption is attained. The performance of energy audit Linear Regression system is 82%, Decision Tree system is 86% and Random Forest 91% are predicted energy consumption and the performance of learning methods were evaluated based on the heir predictive accuracy, ease of learning and user friendly characteristics. The Random Forest energy audit system is superior when compare to other energy audit system.
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Khirirat, Sarit, Sindri Magnússon, Arda Aytekin, and Mikael Johansson. "A Flexible Framework for Communication-Efficient Machine Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 9 (May 18, 2021): 8101–9. http://dx.doi.org/10.1609/aaai.v35i9.16987.
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With the increasing scale of machine learning tasks, it has become essential to reduce the communication between computing nodes. Early work on gradient compression focused on the bottleneck between CPUs and GPUs, but communication-efficiency is now needed in a variety of different system architectures, from high-performance clusters to energy-constrained IoT devices. In the current practice, compression levels are typically chosen before training and settings that work well for one task may be vastly suboptimal for another dataset on another architecture. In this paper, we propose a flexible framework which adapts the compression level to the true gradient at each iteration, maximizing the improvement in the objective function that is achieved per communicated bit. Our framework is easy to adapt from one technology to the next by modeling how the communication cost depends on the compression level for the specific technology. Theoretical results and practical experiments indicate that the automatic tuning strategies significantly increase communication efficiency on several state-of-the-art compression schemes.
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Abebe, Misganaw, Yongwoo Shin, Yoojeong Noh, Sangbong Lee, and Inwon Lee. "Machine Learning Approaches for Ship Speed Prediction towards Energy Efficient Shipping." Applied Sciences 10, no. 7 (March 28, 2020): 2325. http://dx.doi.org/10.3390/app10072325.
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As oil prices continue to rise internationally, shipping costs are also increasing rapidly. In order to reduce fuel costs, an economical shipping route must be determined by accurately predicting the estimated arrival time of ships. A common method in the evaluation of ship speed involves computing the total resistance of a ship using theoretical analysis; however, using theoretical equations cannot be applied for most ships under various operating conditions. In this study, a machine learning approach was proposed to predict ship speed over the ground using the automatic identification system (AIS) and noon-report maritime weather data. To train and validate the developed model, the AIS and marine weather data of the seventy-six vessels for a period one year were used. The model accuracy result shows that the proposed data-driven model has a satisfactory capability to predict the ship speed based on the chosen features.
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Leonowicz, Zbigniew, and Michal Jasinski. "Machine Learning and Data Mining Applications in Power Systems." Energies 15, no. 5 (February 24, 2022): 1676. http://dx.doi.org/10.3390/en15051676.
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This Special Issue was intended as a forum to advance research and apply machine-learning and data-mining methods in order to facilitate the development of modern electric power systems, grids and devices, smart grids and protection devices, as well as to develop tools for more accurate and efficient power system analysis [...]
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Liu, Junxia, and Wen Liu. "CA Energy Saving Joint Resource Optimization Scheme Based on 5G Channel Information Prediction of Machine Learning." Sustainability 14, no. 24 (December 19, 2022): 17012. http://dx.doi.org/10.3390/su142417012.
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Carrier aggregation (CA) is considered as a key enabling technology to provide higher rates for users in LTE/5G networks. However, the increased transmission rate is accompanied by higher energy consumption. The existing CA energy efficiency resource optimization allocation scheme in 5G does not fully consider the following two issues, namely, the impact of delayed channel state information feedback on the rationality of resource allocation and the increasing in energy consumption caused by frequent switching of component carriers (CCs) by narrowband users; this paper proposed a CA energy-efficient joint resource optimization allocation (PEJA) scheme based on channel information prediction. The proposed scheme (PEJA) fully considers the above two issues. Firstly, the algorithm of random forest predicting channel state information is designed. On the basis, the CA energy-efficient joint resource optimization allocation (PEJA) scheme based on channel information prediction is proposed. The simulation results show that the proposed algorithm PEJA has a higher energy efficiency and throughput than the comparison algorithm under different numbers of users and different transmission powers. The PEJA algorithm is more energy efficient than the PEJA-NC algorithm, which does not consider the CC handover of narrowband users. To sum up, the proposed PEJA energy-efficient resource allocation scheme maximizes system energy efficiency and achieves a higher throughput.
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Baidoo, Charity Yaa Mansa, Winfred Yaokumah, and Ebenezer Owusu. "Estimating Overhead Performance of Supervised Machine Learning Algorithms for Intrusion Detection." International Journal of Information Technologies and Systems Approach 16, no. 1 (February 3, 2023): 1–19. http://dx.doi.org/10.4018/ijitsa.316889.
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Estimating the energy and memory consumption of machine learning(ML) models for intrusion detection ensures efficient allocation of system resources. This study investigates the impact of supervised ML algorithms on the energy and memory consumption of intrusion detection systems. Experiments are conducted with seven ML algorithms and a proposed ensemble model, utilizing two intrusion detection datasets. Pearson correlation coefficient(PCC) and Spearman correlation coefficient are employed for the selection of optimum features. Regarding energy consumption, the findings reveal that the PCC with the UNSW-NB15 dataset uses the least amount of DRAM and CPU power. For ML methods, SVM utilizes the highest energy for both feature selection methods and datasets. Concerning memory consumption, the results show that decision tree uses the most current memory with PCC on the UNSW-NB15. The proposed ensemble model demonstrates the highest performance. These findings offer practical guidelines to ML experts when choosing the optimum model with the most efficient utilization of energy and memory.
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N, Miss Swapna, and Mrs Renuka Malge. "Classification and Detection of Bone Fracture Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (July 31, 2022): 1636–40. http://dx.doi.org/10.22214/ijraset.2022.45523.
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Abstract: Technologies that are rapidly growing are appearing every day in a variety of disciplines, particularly the medical one. However, there are still certain outdated methods that are still widely used, effective, and efficient. One of these methods is the use of X-rays to identify damaged bones . However, sometimes the number of fractures is insignificant and difficult to see. Systems should be created that are efficient and intelligent. In this study, an artificial classification system that can recognise and categorise bone fractures is being developed. There are two main steps in the system that has been designed. The photos of the fractures are processed in the first stage using various image processing techniques to identify their position and shapes. The classification phase follows, in which a back propagation neural network training on the processed images before being put to the test. The system was put to the test experimentally on various photographs of bone fractures, and the results indicate high performance as well as a classification rate.
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Huang, Xiaoyan, Ke Zhang, Fan Wu, and Supeng Leng. "Collaborative Machine Learning for Energy-Efficient Edge Networks in 6G." IEEE Network 35, no. 6 (November 2021): 12–19. http://dx.doi.org/10.1109/mnet.100.2100313.
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Alhmiedat, Tareq. "Fingerprint-Based Localization Approach for WSN Using Machine Learning Models." Applied Sciences 13, no. 5 (February 27, 2023): 3037. http://dx.doi.org/10.3390/app13053037.
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The area of localization in wireless sensor networks (WSNs) has received considerable attention recently, driven by the need to develop an accurate localization system with the minimum cost and energy consumption possible. On the other hand, machine learning (ML) algorithms have been employed widely in several WSN-based applications (data gathering, clustering, energy-harvesting, and node localization) and showed an enhancement in the obtained results. In this paper, an efficient WSN-based fingerprinting localization system for indoor environments based on a low-cost sensor architecture, through establishing an indoor fingerprinting dataset and adopting four tailored ML models, is presented. The proposed system was validated by real experiments conducted in complex indoor environments with several obstacles and walls and achieves an efficient localization accuracy with an average of 1.4 m. In addition, through real experiments, we analyze and discuss the impact of reference point density on localization accuracy.
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Cardoso, Daniel, Daniel Nunes, João Faria, Paulo Fael, and Pedro D. Gaspar. "Intelligent Micro-Cogeneration Systems for Residential Grids: A Sustainable Solution for Efficient Energy Management." Energies 16, no. 13 (July 6, 2023): 5215. http://dx.doi.org/10.3390/en16135215.
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This paper presents an optimization approach for Micro-cogeneration systems with internal combustion engines integrated into residential grids, addressing power demand failures caused by intermittent renewable energy sources. The proposed method leverages machine learning techniques, control strategies, and grid data to improve system flexibility and efficiency in meeting electricity and domestic hot water demands. Historical residential grid data were analysed to develop a machine learning-based demand prediction model for electricity and hot water. Thermal energy storage was integrated into the Micro-cogeneration system to enhance flexibility. An optimization model was created, considering efficiency, emissions, and cost while adapting to real-time demand changes. A control strategy was designed for the flexible operation of the Micro-cogeneration system, addressing excess thermal energy storage and resource allocation. The proposed solution’s effectiveness was validated through simulations, with results demonstrating the Micro-cogeneration system’s ability to efficiently address high electricity and hot water demand periods while mitigating power demand failures from renewable energy sources. The research presents a novel approach with the potential to significantly improve grid resilience, energy efficiency, and renewable energy integration in residential grids, contributing to more sustainable and reliable energy systems.
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Oh, Myeung Suk, Gibum Kim, and Hyuncheol Park. "Machine-Learning-Based Link Adaptation for Energy-Efficient MIMO-OFDM Systems." Journal of Korean Institute of Electromagnetic Engineering and Science 27, no. 5 (June 7, 2016): 407–15. http://dx.doi.org/10.5515/kjkiees.2016.27.5.407.
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Kaushik, Akshay, and Varun Goel. "Building an Efficient Intrusion Detection System using Feature Selection and Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 5314–23. http://dx.doi.org/10.22214/ijraset.2022.43434.
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Abstract: With the increase in internet activity and as the world goes digital as the days go, the risk of exposure to malicious activities also increased rapidly. The intruders/hackers use various methods to gain unauthorized access to one's computer or any other device, Network Intrusion is one of the methods by which intruders attack the network of the user, the user can be an individual or an organization based on the intention/agenda of the attackers. Significant Reasons for intrusion are Hacktivism, Steal Money or Data, and Spying. Due to the internet being a vast place, it is challenging to pinpoint a particular way in which Network Intrusion takes place, therefore a Network Intrusion Detection System needs to be put in place in order to deal with the issues regarding Network Intrusions. There are multiple leaks or data extortion that happened previously and, in this paper, the dataset released based on a leak from KDD99 is used. An improved version of KDD99 (NSL-KDD) is used in this study. NSLKDD datasets have been used for training the Machine Learning Model. Given the number of attributes in the dataset, it was difficult to use all attributes so, feature selection methods were used to get the best attributes to develop an efficient Machine Learning model. In this analysis of Machine Learning algorithms, the algorithms under consideration are Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, and Naive-Bayes. For comparison of the performance of the algorithms metrics like Accuracy Score, Confusion Matrix, and Classification Report were considered to find the best algorithm among them.
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Chatterjee, Rajdeep, Oindrila Das, Ridam Kundu, and Soumik Podder. "Machine Learning Inspired Smart Agriculture System with Crop Prediction." International Journal for Research in Applied Science and Engineering Technology 11, no. 1 (January 31, 2023): 1511–17. http://dx.doi.org/10.22214/ijraset.2023.48841.
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Abstract: This paper presents Machine Learning Inspired Smart Agriculture System with Crop Prediction that will assist farmers in getting Live Data (Temperature, Soil Moisture, level of Nitrogen, Phosphorous and Potassium present in the soil, Alkalinity and Acidity in the soil etc. ) for efficient environment monitoring which will enable them to increase their overall yield and quality of products. The Machine Learning Inspired Smart Agriculture System with Crop Prediction is proposed where Machine Learning Technology is hybridized with different Sensors and a Wi-Fi module that will yield live data feed using online software Localhost and PHPmyadmin. The product being proposed is tested on Live Agriculture Fields giving high accuracy over 98% in data feeds
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Noori, Nematullah, Vyenkatesh Bawanthad, Mayur Pakhare, Ramashray Agrawal, and Vinod Kimbahune. "Phishing URL Detection using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 3645–48. http://dx.doi.org/10.22214/ijraset.2023.52342.
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Abstract: Phishing attacks continue to pose a major threat for computer system defenders, often forming the first step in a multistage attack. There have been great strides made in phishing detection; however,some phishing emails appear to pass through filters by making simple structural and semantic changes to the messages. We tackle this problem through the use of a machine learning classifier operating on a large corpus of phishing and legitimate emails. We design a system to extract features, elevating some to higherlevel feature, that are meant to defeat common phishing email detection strategies. This paper presents an approach to detect phishing URLs in an efficient way based on URL features only. For detecting the phishing URLs SVM classifier is used. The performances are evaluated for different size of datasets using different number of features. The results are compared with other machine learning classification techniques. The proposed system is able to detect phishing websites using URL features only.
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Mittal, Mohit, Rocío Pérez de Prado, Yukiko Kawai, Shinsuke Nakajima, and José E. Muñoz-Expósito. "Machine Learning Techniques for Energy Efficiency and Anomaly Detection in Hybrid Wireless Sensor Networks." Energies 14, no. 11 (May 27, 2021): 3125. http://dx.doi.org/10.3390/en14113125.
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Wireless sensor networks (WSNs) are among the most popular wireless technologies for sensor communication purposes nowadays. Usually, WSNs are developed for specific applications, either monitoring purposes or tracking purposes, for indoor or outdoor environments, where limited battery power is a main challenge. To overcome this problem, many routing protocols have been proposed through the last few years. Nevertheless, the extension of the network lifetime in consideration of the sensors capacities remains an open issue. In this paper, to achieve more efficient and reliable protocols according to current application scenarios, two well-known energy efficient protocols, i.e., Low-Energy Adaptive Clustering hierarchy (LEACH) and Energy–Efficient Sensor Routing (EESR), are redesigned considering neural networks. Specifically, to improve results in terms of energy efficiency, a Levenberg–Marquardt neural network (LMNN) is integrated. Furthermore, in order to improve the performance, a sub-cluster LEACH-derived protocol is also proposed. Simulation results show that the Sub-LEACH with LMNN outperformed its competitors in energy efficiency. In addition, the end-to-end delay was evaluated, and Sub-LEACH protocol proved to be the best among existing strategies. Moreover, an intrusion detection system (IDS) has been proposed for anomaly detection based on the support vector machine (SVM) approach for optimal feature selection. Results showed a 96.15% accuracy—again outperforming existing IDS models. Therefore, satisfactory results in terms of energy efficiency, end-to-end delay and anomaly detection analysis were attained.
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R., Udendhran, Sasikala R., Nishanthi R., and Vasanthi J. "Smart Energy Consumption Control in Commercial Buildings Using Machine Learning and IOT." E3S Web of Conferences 387 (2023): 02003. http://dx.doi.org/10.1051/e3sconf/202338702003.
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In recent years, the use of artificial intelligence (AI) techniques such as Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs), in combination with the Internet of Things (IoT), has gained significant attention for optimizing energy consumption in commercial buildings. With the increasing demand for energy and the rising costs of energy, there is a pressing need for efficient methods for energy management in commercial buildings. Smart energy consumption control systems that utilize machine learning algorithms and IoT devices can provide real-time data on energy usage and automate energy usage decisions in commercial buildings. In this paper, we investigate the potential of ANN and SVM-based smart energy consumption control systems in commercial buildings. We aim to analyze the impact of using these algorithms on energy consumption patterns in commercial buildings and evaluate the efficiency and effectiveness of these systems in reducing energy consumption and costs while maintaining the desired level of comfort for the occupants. Our study will focus on comparing the performance of ANN and SVM-based algorithms in terms of energy consumption reduction and cost savings. The results of this study can provide valuable insights into the application of ANN and SVM-based smart energy consumption control systems in commercial buildings and contribute to the development of more sustainable and energy-efficient buildings.
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Reddy, Suraka Maha Lakshmi, and Adusumilli Yagna Gayathri. "Healthcare Monitoring System for Diabetic Patients Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 11 (November 30, 2022): 122–32. http://dx.doi.org/10.22214/ijraset.2022.47262.
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Abstract: Present-day technology provides quite an efficient way of monitoring an individual’s health. Bluetooth Low Energy (BLE)-based sensors can be considered as one of the solutions for checking personal vital signs data such as blood pressure, heart rate, weight, and blood glucose (BG). In this study, we propose a personalized healthcare monitoring system by utilizing a BLE-based sensor device dataset, data processing, and machine learning-based algorithms to help diabetic patients to better self-manage their chronic condition. The proposed real-time data processing system utilizes machine Learning algorithms to train the model. Machine learning–based classification methods were tested on a diabetes dataset in order to show that a Multilayer Perceptron can provide early prediction of diabetes given the user’s sensor data as input. Furthermore, the proposed diabetes classification and prediction might be integrated with individualized diet and physical activity recommendations to improve patients' health quality and avert severe circumstances in the future.
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Khabbouchi, Imed, Dhaou Said, Aziz Oukaira, Idir Mellal, and Lyes Khoukhi. "Machine Learning and Game-Theoretic Model for Advanced Wind Energy Management Protocol (AWEMP)." Energies 16, no. 5 (February 24, 2023): 2179. http://dx.doi.org/10.3390/en16052179.
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To meet the target of carbon neutrality by the year 2050 and decrease the dependence on fossil fuels, renewable energy sources (RESs), specifically wind power, and Electric Vehicles (EVs) have to be massively deployed. Nevertheless, the integration of a large amount of wind power, with an intermittent nature, into the grid and the variability of the load on the demand side require an efficient and reliable energy management system (EMS) for operation, scheduling, maintenance and energy trading in the modern power system. This article proposes a new Energy Management Protocol (EMP) based on the combination of Machine Learning (ML) and Game-Theoretic (GT) algorithms to manage the operation of the charging/discharging of EVs from an energy storage system (ESS) via EV supply equipment (EVSE) when the main source of energy is wind power. The ESS can be linked to the grid to overcome downtimes of wind power production. Case study results of wind power forecasting using an ML algorithm and 10 min wind measurements, combined with a GT optimization model, showed good performance in the forecasting and management of power dispatching between EVs to ensure the efficient and accurate operation of the power system.
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Khac Le, Hung, and SoYoung Kim. "Machine Learning Based Energy-Efficient Design Approach for Interconnects in Circuits and Systems." Applied Sciences 11, no. 3 (January 20, 2021): 915. http://dx.doi.org/10.3390/app11030915.
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In this paper, we propose an efficient design methodology for energy-efficient off-chip interconnect. This approach leverages an artificial neural network (ANN) as a surrogate model that significantly improves design efficiency in the frequency-domain. This model utilizes design specifications as the constraint functions to guarantee the satisfaction of design requirements. Additionally, a specified objective function to select low-loss and low-noise structure is employed to determine the optimal case from a large design space. The proposed design flow can find the optimum design that gives maximum eye height (EH) with the largest allowable transmitter supply voltage (VTX) reduction for minimum power consumption. The proposed approach is applied to the microstrip line and stripline structures with single-ended and differential signals for general applicability. For the microstrip line, the proposed methodology performs at a performance speed with 42.7 and 0.5 s per structure for the data generation and optimization process, respectively. In addition, the optimal microstrip line design achieves a 25%VTX reduction. In stripline structures, it takes 31.9 s for the data generation and 0.6 s for the optimization process per structure when the power efficiency reaches a maximum 30.7% peak to peak VTX decrease.
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Neelaveni, Dr R., Abhinav ., and Sahas . "Analysis of Efficient Intrusion Detection System using Ensemble Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 1521–30. http://dx.doi.org/10.22214/ijraset.2023.51858.
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Abstract: Our increasingly connected world continues to face an ever-growing amount of network-based attacks. Intrusion detection systems (IDS) are essential security technology for detecting these attacks. Although numerous machine learningbased IDS have been proposed for detecting malicious network traffic, most have difficulty properly detecting and classifying the more uncommon attack types. The research in Cyber Security has raised the need to address the cybercrimes that have caused the requisition of intellectual properties such as the breakdown of computer systems and impairment of important data compromising the confidentiality authenticity and integrity of the user. Considering these scenarios, securing the computer systems and the user using an Intrusion Detection System (IDS) is essential. The performance of IDS was studied by developing an IDS dataset consisting of network traffic features to learn the attack patterns. Intrusion detection is a classification problem wherein various Ensemble Learning (ML) and Data Mining (DM) techniques are applied to classify the network data into normal and attack traffic. Moreover, the types of network attacks changed over the years, so updating the datasets used for evaluating IDS is necessary.
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Forootan, Mohammad Mahdi, Iman Larki, Rahim Zahedi, and Abolfazl Ahmadi. "Machine Learning and Deep Learning in Energy Systems: A Review." Sustainability 14, no. 8 (April 18, 2022): 4832. http://dx.doi.org/10.3390/su14084832.
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With population increases and a vital need for energy, energy systems play an important and decisive role in all of the sectors of society. To accelerate the process and improve the methods of responding to this increase in energy demand, the use of models and algorithms based on artificial intelligence has become common and mandatory. In the present study, a comprehensive and detailed study has been conducted on the methods and applications of Machine Learning (ML) and Deep Learning (DL), which are the newest and most practical models based on Artificial Intelligence (AI) for use in energy systems. It should be noted that due to the development of DL algorithms, which are usually more accurate and less error, the use of these algorithms increases the ability of the model to solve complex problems in this field. In this article, we have tried to examine DL algorithms that are very powerful in problem solving but have received less attention in other studies, such as RNN, ANFIS, RBN, DBN, WNN, and so on. This research uses knowledge discovery in research databases to understand ML and DL applications in energy systems’ current status and future. Subsequently, the critical areas and research gaps are identified. In addition, this study covers the most common and efficient applications used in this field; optimization, forecasting, fault detection, and other applications of energy systems are investigated. Attempts have also been made to cover most of the algorithms and their evaluation metrics, including not only algorithms that are more important, but also newer ones that have received less attention.
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Lokesh, Nitish, and Dr Pawan Kumar. "A Literature Review on Billing System using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 645–48. http://dx.doi.org/10.22214/ijraset.2022.41199.
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Abstract: The process of billing a product in a retail outlet is done using the system of pre-made barcodes or RFIDs. This is done to keep a track of the items and identify them in the billing process, but it is time-consuming and labour-intensive as all items need to be affixed with barcodes before being used. Most of the small to medium scale retail stores also have a large part of their sales with items of a variable quantity, which cannot be affixed with barcodes as their quantity is determined dynamically and according to the customer’s needs. In this paper, we have explored various technologies including but not limited to computer vision, object detection and image recognition, especially due to its latest boom with machine learning and deep learning. We have briefed our findings on technologies that can be used to build efficient and novel systems to solve the issues that arise with using systems that are labour intensive, redundant, time-consuming and explore ways to incorporate items that can be packed with variable quantities. Keywords: Machine Learning, Deep Learning, Object Detection, Billing System, Computer vision, Image recognition
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R, Praveenkumar, Kirthika ., Durai Arumugam, and Dinesh . "Hybridization of Machine Learning Techniques for WSN Optimal Cluster Head Selection." International Journal of Electrical and Electronics Research 11, no. 2 (June 19, 2023): 426–33. http://dx.doi.org/10.37391/ijeer.110224.
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Wireless sensor networks (WSN) keep developing in recent days concerning the self-covered network, self-healing network, and association of system component circuit selections that enable the implementation process. Wireless sensor network lifetime stabilization is essential to providing a higher quality experience to consumers. The wireless sensor network is associated with classifiers that keep learning the data pattern and further modify the cluster selection to produce dynamic results. The presented system is focused on creating an efficient wireless sensor network in which cluster head selection is dynamically programmed to improve the life span of the devices. The energy utilized by each node is pre-programmed with random assignments. The values are configured by the machine learning techniques to improve the hop death. The models developed using the parameters help project energy consumption. The proposed system considers a support vector machine (SVM), and the Gaussian regression process (GRP) enabled the comparative study of lifespan analysis and support systems to make cluster selection efficient. The proposed model is used to test the current selection of cluster heads using a support rectangle machine and further modify the regression process using the Gaussian regression model. Evaluation of network lifetime and flexibility obtained in cluster selection.
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Aladwani, Habeeb A. H. R., Mohd Khairol Anuar Ariffin, and Faizal Mustapha. "A supervised machine-learning method for optimizing the automatic transmission system of wind turbines." Engineering Solid Mechanics 10, no. 1 (2022): 35–56. http://dx.doi.org/10.5267/j.esm.2021.11.001.
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Large-scale wind turbines mostly use Continuously Variable Transmission (CVT) as the transmission system, which is highly efficient. However, it comes with high complexity and cost too. In contrast, the small-scale wind turbines that are available in the market offer a one-speed gearing system only where no gear ratios are varied, resulting in low efficiency of harvesting energy and leading to gears failure. In this research, an unsupervised machine-learning algorithm is proposed to address the energy efficiency of the automatic transmission system in vertical axis wind turbines (VAWT), to increase its efficiency in harvesting energy. The aim is to find the best adjustment for VAWT while the automatic transmission system is taken into account. For this purpose, the system is simulated and tested under various gear ratios conditions while a centrifugal clutch is applied to automatic gear shifting. The outcomes indicated that the automatic transmission system could successfully adjust the spinning in line with the wind speed. As a result, the obtained level of harvested voltage and power by VAWT with the automatic transmission system are improved significantly. Consequently, it is concluded that automatic VAWTs, equipped with the machine-learning capability can readjust themselves with the wind speed more efficiently.
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Tovar, Nathaniel, Sean (Seok-Chul) Kwon, and Jinseong Jeong. "Image Upscaling with Deep Machine Learning for Energy-Efficient Data Communications." Electronics 12, no. 3 (January 30, 2023): 689. http://dx.doi.org/10.3390/electronics12030689.
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Advanced algorithms of image quality enhancement have been attracting substantial attention recently due to the successful business model of video streaming services. The extremely high image quality in video streaming demands a significant increase in the transmit data rate. In turn, the required ultrahigh data rate causes the saturation of the video streaming service network if there is no remedy for this situation. Compression algorithms have contributed to the energy-efficient transmission of data; however, they have almost reached the upper bound. The demand for ultrahigh image quality by the user is significantly increasing. Meanwhile, minimizing data transmission is inevitable in energy-efficient communications. Therefore, to improve energy efficiency, we propose to decrease the image resolution at the transmitter (Tx) and upscale the image at the receiver (Rx). However, standard upscaling does not yield ultrahigh-quality images. Deep machine learning contributes to image super-resolution techniques with the cost of enormous time and resources at the user end. Hence, it is inappropriate for real-time applications. With this motivation, this paper proposes a deep machine learning-based real-time image super-resolution with a residual neural network on the prevalent resources at the user end. The proposed scheme provides better quality than conventional image upscaling such as interpolation. The comprehensive simulation verifies that our scheme substantially outperforms the conventional methods, utilizing the seven-layer residual neural network.
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Li, Sijia, Arman Oshnoei, Frede Blaabjerg, and Amjad Anvari-Moghaddam. "Hierarchical Control for Microgrids: A Survey on Classical and Machine Learning-Based Methods." Sustainability 15, no. 11 (June 1, 2023): 8952. http://dx.doi.org/10.3390/su15118952.
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Microgrids create conditions for efficient use of integrated energy systems containing renewable energy sources. One of the major challenges in the control and operation of microgrids is managing the fluctuating renewable energy generation, as well as sudden load changes that can affect system frequency and voltage stability. To solve the above problems, hierarchical control techniques have received wide attention. At present, although some progress has been made in hierarchical control systems using classical control, machine learning-based approaches have shown promising features and performance in the control and operation management of microgrids. This paper reviews not only the application of classical control in hierarchical control systems in the last five years of references, but also the application of machine learning techniques. The survey also provides a comprehensive description of the use of different machine learning algorithms at different control levels, with a comparative analysis for their control methods, advantages and disadvantages, and implementation methods from multiple perspectives. The paper also presents the structure of primary and secondary control applications utilizing machine learning technology. In conclusion, it is highlighted that machine learning in microgrid hierarchical control can enhance control accuracy and address system optimization concerns. However, challenges, such as computational intensity, the need for stability analysis, and experimental validation, remain to be addressed.
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Pandey, Mrs Arjoo. "Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 8 (August 31, 2023): 864–69. http://dx.doi.org/10.22214/ijraset.2023.55224.
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Abstract: Machine learning refers to the study and development of machine learning algorithms and techniques at a conceptual level, focusing on theoretical foundations, algorithmic design, and mathematical analysis rather than specific implementation details or application domains. It aimsto provide a deeper understanding of the fundamental principles and limitations of machine learning, enabling researchers to develop novel algorithms and advance the field. In abstract machine learning, the emphasis is on formalizing and analyzing learning tasks, developing mathematical models for learning processes, and studying the properties and behavior of various learning algorithms. This involves investigating topics such as learning theory, statistical learning, optimization, computational complexity, and generalization. The goalis to develop theoretical frameworks and mathematical tools that help explain why certain algorithms work and how they can be improved. Abstract machine learning also explores fundamental questions related to the theoretical underpinnings of machine learning, such as the trade-offs between bias and variance, the existence of optimal learning algorithms, the sample complexity of learning tasks, and the limits of what can be learned from data. It provides a theoretical foundation for understanding the capabilities and limitations of machine learning algorithms, guiding the development of new algorithms and techniques. Moreover, abstract machine learning serves as a bridge between theory and practice, facilitating the transfer of theoretical insights into practical applications. Theoretical advances in abstract machine learning can inspire new algorithmic approaches and inform the design of real-world machine learning systems. Conversely, practical challenges and observations from realworld applications can motivate and guide theoretical investigations in abstract machine learning. Overall, abstract machine learning plays a crucial role in advancing the field of machine learning by providing rigorous theoretical frameworks, mathematical models, and algorithmic principles that deepen our understanding of learning processes and guide the development of more effectiveand efficient machine learning algorithms.
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Gayke, Prof P. S., Gaurav Bhise, Nikhil Bhor, Jagdish Bhagwat, and Tanvi Alkute. "An Efficient Spam Detection Technique for IoT Devices Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 7534–37. http://dx.doi.org/10.22214/ijraset.2023.53527.
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Abstract: The Internet of Things (IoT) is a group of millions of devices having sensors and actuators linked over wired or wireless channels for data transmission. IoT has grown rapidly over the past decade with more than 25 billion devices are expected to be connected by 2020. The volume of data released from these devices will increase many-fold in the years to come. In addition to an increased volume, the IoT devices produces a large amount of data with a number of different modalities having varying data quality defined by its speed in terms of time and position dependency. In such an environment, machine learning algorithms can play an important role in ensuring security and authorization based on biotechnology, anomalous detection to improve the usability and security of IoT systems. On the other hand, attackers often view learning algorithms to exploit the vulnerabilities in smart IoT-based systems. Motivated from these, in this paper, we propose an innovative approach for spam detection in IoT devices using machine learning. Our technique harnesses the power of advanced machine learning algorithms to accurately identify and mitigate spam attacks, ensuring the integrity and security of IoT ecosystems. We present a comprehensive methodology that combines data collection, feature extraction, model training, and evaluation to build a robust spam detection system.
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Styła, Michał, Bartłomiej Kiczek, Grzegorz Kłosowski, Tomasz Rymarczyk, Przemysław Adamkiewicz, Dariusz Wójcik, and Tomasz Cieplak. "Machine Learning-Enhanced Radio Tomographic Device for Energy Optimization in Smart Buildings." Energies 16, no. 1 (December 27, 2022): 275. http://dx.doi.org/10.3390/en16010275.
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Smart buildings are becoming a new standard in construction, which allows for many possibilities to introduce ergonomics and energy savings. These contain simple improvements, such as controlling lights and optimizing heating or air conditioning systems in the building, but also more complex ones, such as indoor movement tracking of building users. One of the necessary components is an indoor localization system, especially without any device worn by the person being located. These types of solutions are important in locating people inside smart buildings, managing hospitals of the future and other similar institutions. The article presents a prototype of an innovative energy-efficient device for radio tomography, in which the hardware and software layers of the solution are presented. The presented example consists of 32 radio sensors based on a Bluetooth 5 protocol controlled by a central unit. The preciseness of the system was verified both visually and quantitatively by the image reconstruction as a result of solving the inverse tomographic problem using three neural networks.
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Alagumalai, Avinash, Balaji Devarajan, Hua Song, Somchai Wongwises, Rodrigo Ledesma-Amaro, Omid Mahian, Mikhail Sheremet, and Eric Lichtfouse. "Machine learning in biohydrogen production: a review." Biofuel Research Journal 10, no. 2 (June 1, 2023): 1844–58. http://dx.doi.org/10.18331/brj2023.10.2.4.
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Biohydrogen is emerging as a promising carbon-neutral and sustainable energy carrier with high energy yield to replace conventional fossil fuels. However, biohydrogen commercial uptake is mainly hindered by the supply side. As a result, various operating parameters must be optimized to realize biohydrogen commercial uptake on a large-scale. Recently, machine learning algorithms have demonstrated the ability to handle large amounts of data while requiring less in-depth knowledge of the system and being capable of adapting to evolving circumstances. This review critically reviews the role of machine learning in categorizing and predicting data related to biohydrogen production. The accuracy and potential of different machine learning algorithms are reported. Also, the practical implications of machine learning models to realize biohydrogen uptake by the transportation sector are discussed. The review indicates that machine learning algorithms can successfully model non-linear and complex interactions between operational and performance parameters in biohydrogen production. Additionally, machine learning algorithms can help researchers identify the most efficient methods for producing biohydrogen, leading to a more sustainable and cost-effective energy source.
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Petruschke, L., G. Elserafi, B. Ioshchikhes, and M. Weigold. "MACHINE LEARNING BASED IDENTIFICATION OF ENERGY EFFICIENCY MEASURES FOR MACHINE TOOLS USING LOAD PROFILES AND MACHINE SPECIFIC META DATA." MM Science Journal 2021, no. 5 (November 3, 2021): 5061–68. http://dx.doi.org/10.17973/mmsj.2021_11_2021153.
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Approaches to detect energy efficiency measures are associated with time consuming analysis requiring expertise. Against this background, this paper presents an expert system to identify potentials for improving the energy efficiency of metal cutting machine tools based on measurement and meta data of 35 machines. For this purpose, it is necessary to determine energy states of machine tools and control strategies of their support units. Therefore, unsupervised and supervised learning algorithms are applied and evaluated. Based on energy states, control strategies and descriptive statistics, performance indicators are developed for enabling automatic selection and prioritization of application-dependent efficiency measures.