Relevant bibliographies by topics / Energy Efficient Machine Learning System

Academic literature on the topic 'Energy Efficient Machine Learning System'

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Published: 6 September 2023

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Journal articles on the topic "Energy Efficient Machine Learning System"

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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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Dissertations / Theses on the topic "Energy Efficient Machine Learning System"

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OSTA, MARIO. "Energy-efficient embedded machine learning algorithms for smart sensing systems." Doctoral thesis, Università degli studi di Genova, 2020. http://hdl.handle.net/11567/997732.

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Embedded autonomous electronic systems are required in numerous application domains such as Internet of Things (IoT), wearable devices, and biomedical systems. Embedded electronic systems usually host sensors, and each sensor hosts multiple input channels (e.g., tactile, vision), tightly coupled to the electronic computing unit (ECU). The ECU extracts information by often employing sophisticated methods, e.g., Machine Learning. However, embedding Machine Learning algorithms poses essential challenges in terms of hardware resources and energy consumption because of: 1) the high amount of data to be processed; 2) computationally demanding methods. Leveraging on the trade-off between quality requirements versus computational complexity and time latency could reduce the system complexity without affecting the performance. The objectives of the thesis are to develop: 1) energy-efficient arithmetic circuits outperforming state of the art solutions for embedded machine learning algorithms, 2) an energy-efficient embedded electronic system for the “electronic-skin” (e-skin) application. As such, this thesis exploits two main approaches: Approximate Computing: In recent years, the approximate computing paradigm became a significant major field of research since it is able to enhance the energy efficiency and performance of digital systems. “Approximate Computing”(AC) turned out to be a practical approach to trade accuracy for better power, latency, and size . AC targets error-resilient applications and offers promising benefits by conserving some resources. Usually, approximate results are acceptable for many applications, e.g., tactile data processing,image processing , and data mining ; thus, it is highly recommended to take advantage of energy reduction with minimal variation in performance . In our work, we developed two approximate multipliers: 1) the first one is called “META” multiplier and is based on the Error Tolerant Adder (ETA), 2) the second one is called “Approximate Baugh-Wooley(BW)” multiplier where the approximations are implemented in the generation of the partial products. We showed that the proposed approximate arithmetic circuits could achieve a relevant reduction in power consumption and time delay around 80.4% and 24%, respectively, with respect to the exact BW multiplier. Next, to prove the feasibility of AC in real world applications, we explored the approximate multipliers on a case study as the e-skin application. The e-skin application is defined as multiple sensing components, including 1) structural materials, 2) signal processing, 3) data acquisition, and 4) data processing. Particularly, processing the originated data from the e-skin into low or high-level information is the main problem to be addressed by the embedded electronic system. Many studies have shown that Machine Learning is a promising approach in processing tactile data when classifying input touch modalities. In our work, we proposed a methodology for evaluating the behavior of the system when introducing approximate arithmetic circuits in the main stages (i.e., signal and data processing stages) of the system. Based on the proposed methodology, we first implemented the approximate multipliers on the low-pass Finite Impulse Response (FIR) filter in the signal processing stage of the application. We noticed that the FIR filter based on (Approx-BW) outperforms state of the art solutions, while respecting the tradeoff between accuracy and power consumption, with an SNR degradation of 1.39dB. Second, we implemented approximate adders and multipliers respectively into the Coordinate Rotational Digital Computer (CORDIC) and the Singular Value Decomposition (SVD) circuits; since CORDIC and SVD take a significant part of the computationally expensive Machine Learning algorithms employed in tactile data processing. We showed benefits of up to 21% and 19% in power reduction at the cost of less than 5% accuracy loss for CORDIC and SVD circuits when scaling the number of approximated bits. 2) Parallel Computing Platforms (PCP): Exploiting parallel architectures for near-threshold computing based on multi-core clusters is a promising approach to improve the performance of smart sensing systems. In our work, we exploited a novel computing platform embedding a Parallel Ultra Low Power processor (PULP), called “Mr. Wolf,” for the implementation of Machine Learning (ML) algorithms for touch modalities classification. First, we tested the ML algorithms at the software level; for RGB images as a case study and tactile dataset, we achieved accuracy respectively equal to 97% and 83.5%. After validating the effectiveness of the ML algorithm at the software level, we performed the on-board classification of two touch modalities, demonstrating the promising use of Mr. Wolf for smart sensing systems. Moreover, we proposed a memory management strategy for storing the needed amount of trained tensors (i.e., 50 trained tensors for each class) in the on-chip memory. We evaluated the execution cycles for Mr. Wolf using a single core, 2 cores, and 3 cores, taking advantage of the benefits of the parallelization. We presented a comparison with the popular low power ARM Cortex-M4F microcontroller employed, usually for battery-operated devices. We showed that the ML algorithm on the proposed platform runs 3.7 times faster than ARM Cortex M4F (STM32F40), consuming only 28 mW. The proposed platform achieves 15× better energy efficiency than the classification done on the STM32F40, consuming 81mJ per classification and 150 pJ per operation.
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Azmat, Freeha. "Machine learning and energy efficient cognitive radio." Thesis, University of Warwick, 2016. http://wrap.warwick.ac.uk/85990/.

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With an explosion of wireless mobile devices and services, system designers are facing a challenge of spectrum scarcity and high energy consumption. Cognitive radio (CR) is a promising solution for fulfilling the growing demand of radio spectrum using dynamic spectrum access. It has the ability of sensing, allocating, sharing and adapting to the radio environment. In this thesis, an analytical performance evaluation of the machine learning and energy efficient cognitive radio systems has been investigated while taking some realistic conditions into account. Firstly, bio-inspired techniques, including re y algorithm (FFA), fish school search (FSS) and particle swarm optimization (PSO), have been utilized in this thesis to evaluate the optimal weighting vectors for cooperative spectrum sensing (CSS) and spectrum allocation in the cognitive radio systems. This evaluation is performed for more realistic signals that suffer from the non-linear distortions, caused by the power amplifiers. The thesis then takes the investigation further by analysing the spectrum occupancy in the cognitive radio systems using different machine learning techniques. Four machine learning algorithms, including naive bayesian classifier (NBC), decision trees (DT), support vector machine (SVM) and hidden markov model (HMM) have been studied to find the best technique with the highest classification accuracy (CA). A detailed comparison of the supervised and unsupervised algorithms in terms of the computational time and classification accuracy has been presented. In addition to this, the thesis investigates the energy efficient cognitive radio systems because energy harvesting enables the perpetual operation of the wireless networks without the need of battery change. In particular, energy can be harvested from the radio waves in the radio frequency spectrum. For ensuring reliable performance, energy prediction has been proposed as a key component for optimizing the energy harvesting because it equips the harvesting nodes with adaptation to the energy availability. Two machine learning techniques, linear regression (LR) and decision trees (DT) have been utilized to predict the harvested energy using real-time power measurements in the radio spectrum. Furthermore, the conventional energy harvesting cognitive radios do not assume any energy harvesting capability at the primary users (PUs). However, this is not the case when primary users are wirelessly powered. In this thesis, a novel framework has been proposed where PUs possess the energy harvesting capabilities and can get benefit from the presence of the secondary user (SU) without any predetermined agreement. The performances of the wireless powered PUs and the SU has also been analysed. Numerical results have been presented to show the accuracy of the analysis. First, it has been observed that bio-inspired techniques outperform the conventional algorithms used for collaborative spectrum sensing and allocation. Second, it has been noticed that SVM is the best algorithm among all the supervised and unsupervised classifiers. Based on this, a new SVM algorithm has been proposed by combining SVM with FFA. It has also been observed that SVM+FFA outperform all other machine leaning classifiers Third, it has been noticed in the energy predictive modelling framework that LR outperforms DT by achieving smaller prediction error. It has also been shown that optimal time and frequency attained using energy predictive model can be used for defining the scheduling policies of the harvesting nodes. Last, it has been shown that wirelessly powered PUs having energy harvesting capabilities can attain energy gain from the transmission of SU and SU can attain the throughput gain from the extra transmission time allocated for energy harvesting PUs.
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García-Martín, Eva. "Extraction and Energy Efficient Processing of Streaming Data." Licentiate thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-15532.

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The interest in machine learning algorithms is increasing, in parallel with the advancements in hardware and software required to mine large-scale datasets. Machine learning algorithms account for a significant amount of energy consumed in data centers, which impacts the global energy consumption. However, machine learning algorithms are optimized towards predictive performance and scalability. Algorithms with low energy consumption are necessary for embedded systems and other resource constrained devices; and desirable for platforms that require many computations, such as data centers. Data stream mining investigates how to process potentially infinite streams of data without the need to store all the data. This ability is particularly useful for companies that are generating data at a high rate, such as social networks. This thesis investigates algorithms in the data stream mining domain from an energy efficiency perspective. The thesis comprises of two parts. The first part explores how to extract and analyze data from Twitter, with a pilot study that investigates a correlation between hashtags and followers. The second and main part investigates how energy is consumed and optimized in an online learning algorithm, suitable for data stream mining tasks. The second part of the thesis focuses on analyzing, understanding, and reformulating the Very Fast Decision Tree (VFDT) algorithm, the original Hoeffding tree algorithm, into an energy efficient version. It presents three key contributions. First, it shows how energy varies in the VFDT from a high-level view by tuning different parameters. Second, it presents a methodology to identify energy bottlenecks in machine learning algorithms, by portraying the functions of the VFDT that consume the largest amount of energy. Third, it introduces dynamic parameter adaptation for Hoeffding trees, a method to dynamically adapt the parameters of Hoeffding trees to reduce their energy consumption. The results show an average energy reduction of 23% on the VFDT algorithm.
Scalable resource-efficient systems for big data analytics
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Harmer, Keith. "An energy efficient brushless drive system for a domestic washing machine." Thesis, University of Sheffield, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.265571.

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Cui, Henggang. "Exploiting Application Characteristics for Efficient System Support of Data-Parallel Machine Learning." Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/908.

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Large scale machine learning has many characteristics that can be exploited in the system designs to improve its efficiency. This dissertation demonstrates that the characteristics of the ML computations can be exploited in the design and implementation of parameter server systems, to greatly improve the efficiency by an order of magnitude or more. We support this thesis statement with three case study systems, IterStore, GeePS, and MLtuner. IterStore is an optimized parameter server system design that exploits the repeated data access pattern characteristic of ML computations. The designed optimizations allow IterStore to reduce the total run time of our ML benchmarks by up to 50×. GeePS is a parameter server that is specialized for deep learning on distributed GPUs. By exploiting the layer-by-layer data access and computation pattern of deep learning, GeePS provides almost linear scalability from single-machine baselines (13× more training throughput with 16 machines), and also supports neural networks that do not fit in GPU memory. MLtuner is a system for automatically tuning the training tunables of ML tasks. It exploits the characteristic that the best tunable settings can often be decided quickly with just a short trial time. By making use of optimization-guided online trial-and-error, MLtuner can robustly find and re-tune tunable settings for a variety of machine learning applications, including image classification, video classification, and matrix factorization, and is over an order of magnitude faster than traditional hyperparameter tuning approaches.
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Le, Borgne Yann-Aël. "Learning in wireless sensor networks for energy-efficient environmental monitoring." Doctoral thesis, Universite Libre de Bruxelles, 2009. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210334.

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Wireless sensor networks form an emerging class of computing devices capable of observing the world with an unprecedented resolution, and promise to provide a revolutionary instrument for environmental monitoring. Such a network is composed of a collection of battery-operated wireless sensors, or sensor nodes, each of which is equipped with sensing, processing and wireless communication capabilities. Thanks to advances in microelectronics and wireless technologies, wireless sensors are small in size, and can be deployed at low cost over different kinds of environments in order to monitor both over space and time the variations of physical quantities such as temperature, humidity, light, or sound.

In environmental monitoring studies, many applications are expected to run unattended for months or years. Sensor nodes are however constrained by limited resources, particularly in terms of energy. Since communication is one order of magnitude more energy-consuming than processing, the design of data collection schemes that limit the amount of transmitted data is therefore recognized as a central issue for wireless sensor networks.

An efficient way to address this challenge is to approximate, by means of mathematical models, the evolution of the measurements taken by sensors over space and/or time. Indeed, whenever a mathematical model may be used in place of the true measurements, significant gains in communications may be obtained by only transmitting the parameters of the model instead of the set of real measurements. Since in most cases there is little or no a priori information about the variations taken by sensor measurements, the models must be identified in an automated manner. This calls for the use of machine learning techniques, which allow to model the variations of future measurements on the basis of past measurements.

This thesis brings two main contributions to the use of learning techniques in a sensor network. First, we propose an approach which combines time series prediction and model selection for reducing the amount of communication. The rationale of this approach, called adaptive model selection, is to let the sensors determine in an automated manner a prediction model that does not only fits their measurements, but that also reduces the amount of transmitted data.

The second main contribution is the design of a distributed approach for modeling sensed data, based on the principal component analysis (PCA). The proposed method allows to transform along a routing tree the measurements taken in such a way that (i) most of the variability in the measurements is retained, and (ii) the network load sustained by sensor nodes is reduced and more evenly distributed, which in turn extends the overall network lifetime. The framework can be seen as a truly distributed approach for the principal component analysis, and finds applications not only for approximated data collection tasks, but also for event detection or recognition tasks.

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Les réseaux de capteurs sans fil forment une nouvelle famille de systèmes informatiques permettant d'observer le monde avec une résolution sans précédent. En particulier, ces systèmes promettent de révolutionner le domaine de l'étude environnementale. Un tel réseau est composé d'un ensemble de capteurs sans fil, ou unités sensorielles, capables de collecter, traiter, et transmettre de l'information. Grâce aux avancées dans les domaines de la microélectronique et des technologies sans fil, ces systèmes sont à la fois peu volumineux et peu coûteux. Ceci permet leurs deploiements dans différents types d'environnements, afin d'observer l'évolution dans le temps et l'espace de quantités physiques telles que la température, l'humidité, la lumière ou le son.

Dans le domaine de l'étude environnementale, les systèmes de prise de mesures doivent souvent fonctionner de manière autonome pendant plusieurs mois ou plusieurs années. Les capteurs sans fil ont cependant des ressources limitées, particulièrement en terme d'énergie. Les communications radios étant d'un ordre de grandeur plus coûteuses en énergie que l'utilisation du processeur, la conception de méthodes de collecte de données limitant la transmission de données est devenue l'un des principaux défis soulevés par cette technologie.

Ce défi peut être abordé de manière efficace par l'utilisation de modèles mathématiques modélisant l'évolution spatiotemporelle des mesures prises par les capteurs. En effet, si un tel modèle peut être utilisé à la place des mesures, d'importants gains en communications peuvent être obtenus en utilisant les paramètres du modèle comme substitut des mesures. Cependant, dans la majorité des cas, peu ou aucune information sur la nature des mesures prises par les capteurs ne sont disponibles, et donc aucun modèle ne peut être a priori défini. Dans ces cas, les techniques issues du domaine de l'apprentissage machine sont particulièrement appropriées. Ces techniques ont pour but de créer ces modèles de façon autonome, en anticipant les mesures à venir sur la base des mesures passées.

Dans cette thèse, deux contributions sont principalement apportées permettant l'applica-tion de techniques d'apprentissage machine dans le domaine des réseaux de capteurs sans fil. Premièrement, nous proposons une approche qui combine la prédiction de série temporelle avec la sélection de modèles afin de réduire la communication. La logique de cette approche, appelée sélection de modèle adaptive, est de permettre aux unités sensorielles de determiner de manière autonome un modèle de prédiction qui anticipe correctement leurs mesures, tout en réduisant l'utilisation de leur radio.

Deuxièmement, nous avons conçu une méthode permettant de modéliser de façon distribuée les mesures collectées, qui se base sur l'analyse en composantes principales (ACP). La méthode permet de transformer les mesures le long d'un arbre de routage, de façon à ce que (i) la majeure partie des variations dans les mesures des capteurs soient conservées, et (ii) la charge réseau soit réduite et mieux distribuée, ce qui permet d'augmenter également la durée de vie du réseau. L'approche proposée permet de véritablement distribuer l'ACP, et peut être utilisée pour des applications impliquant la collecte de données, mais également pour la détection ou la classification d'événements.


Doctorat en Sciences
info:eu-repo/semantics/nonPublished

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Yurur, Ozgur. "Energy Efficient Context-Aware Framework in Mobile Sensing." Scholar Commons, 2013. http://scholarcommons.usf.edu/etd/4797.

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The ever-increasing technological advances in embedded systems engineering, together with the proliferation of small-size sensor design and deployment, have enabled mobile devices (e.g., smartphones) to recognize daily occurring human based actions, activities and interactions. Therefore, inferring a vast variety of mobile device user based activities from a very diverse context obtained by a series of sensory observations has drawn much interest in the research area of ubiquitous sensing. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users, and this allows network services to respond proactively and intelligently based on such awareness. Hence, with the evolution of smartphones, software developers are empowered to create context aware applications for recognizing human-centric or community based innovative social and cognitive activities in any situation and from anywhere. This leads to the exciting vision of forming a society of ``Internet of Things" which facilitates applications to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network which is capable of making autonomous logical decisions to actuate environmental objects. More significantly, it is believed that introducing the intelligence and situational awareness into recognition process of human-centric event patterns could give a better understanding of human behaviors, and it also could give a chance for proactively assisting individuals in order to enhance the quality of lives. Mobile devices supporting emerging computationally pervasive applications will constitute a significant part of future mobile technologies by providing highly proactive services requiring continuous monitoring of user related contexts. However, the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth as compared to the capabilities of PCs and servers. Above all, power concerns are major restrictions standing up to implementation of context-aware applications. These requirements unfortunately shorten device battery lifetimes due to high energy consumption caused by both sensor and processor operations. Specifically, continuously capturing user context through sensors imposes heavy workloads in hardware and computations, and hence drains the battery power rapidly. Therefore, mobile device batteries do not last a long time while operating sensor(s) constantly. In addition to that, the growing deployment of sensor technologies in mobile devices and innumerable software applications utilizing sensors have led to the creation of a layered system architecture (i.e., context aware middleware) so that the desired architecture can not only offer a wide range of user-specific services, but also respond effectively towards diversity in sensor utilization, large sensory data acquisitions, ever-increasing application requirements, pervasive context processing software libraries, mobile device based constraints and so on. Due to the ubiquity of these computing devices in a dynamic environment where the sensor network topologies actively change, it yields applications to behave opportunistically and adaptively without a priori assumptions in response to the availability of diverse resources in the physical world as well as in response to scalability, modularity, extensibility and interoperability among heterogeneous physical hardware. In this sense, this dissertation aims at proposing novel solutions to enhance the existing tradeoffs in mobile sensing between accuracy and power consumption while context is being inferred under the intrinsic constraints of mobile devices and around the emerging concepts in context-aware middleware framework.
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Westphal, Florian. "Efficient Document Image Binarization using Heterogeneous Computing and Interactive Machine Learning." Licentiate thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-16797.

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Large collections of historical document images have been collected by companies and government institutions for decades. More recently, these collections have been made available to a larger public via the Internet. However, to make accessing them truly useful, the contained images need to be made readable and searchable. One step in that direction is document image binarization, the separation of text foreground from page background. This separation makes the text shown in the document images easier to process by humans and other image processing algorithms alike. While reasonably well working binarization algorithms exist, it is not sufficient to just being able to perform the separation of foreground and background well. This separation also has to be achieved in an efficient manner, in terms of execution time, but also in terms of training data used by machine learning based methods. This is necessary to make binarization not only theoretically possible, but also practically viable. In this thesis, we explore different ways to achieve efficient binarization in terms of execution time by improving the implementation and the algorithm of a state-of-the-art binarization method. We find that parameter prediction, as well as mapping the algorithm onto the graphics processing unit (GPU) help to improve its execution performance. Furthermore, we propose a binarization algorithm based on recurrent neural networks and evaluate the choice of its design parameters with respect to their impact on execution time and binarization quality. Here, we identify a trade-off between binarization quality and execution performance based on the algorithm’s footprint size and show that dynamically weighted training loss tends to improve the binarization quality. Lastly, we address the problem of training data efficiency by evaluating the use of interactive machine learning for reducing the required amount of training data for our recurrent neural network based method. We show that user feedback can help to achieve better binarization quality with less training data and that visualized uncertainty helps to guide users to give more relevant feedback.
Scalable resource-efficient systems for big data analytics
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Chakraborty, Debaditya. "Detection of Faults in HVAC Systems using Tree-based Ensemble Models and Dynamic Thresholds." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1543582336141076.

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Sala, Cardoso Enric. "Advanced energy management strategies for HVAC systems in smart buildings." Doctoral thesis, Universitat Politècnica de Catalunya, 2019. http://hdl.handle.net/10803/668528.

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The efficacy of the energy management systems at dealing with energy consumption in buildings has been a topic with a growing interest in recent years due to the ever-increasing global energy demand and the large percentage of energy being currently used by buildings. The scale of this sector has attracted research effort with the objective of uncovering potential improvement avenues and materializing them with the help of recent technological advances that could be exploited to lower the energetic footprint of buildings. Specifically, in the area of heating, ventilating and air conditioning installations, the availability of large amounts of historical data in building management software suites makes possible the study of how resource-efficient these systems really are when entrusted with ensuring occupant comfort. Actually, recent reports have shown that there is a gap between the ideal operating performance and the performance achieved in practice. Accordingly, this thesis considers the research of novel energy management strategies for heating, ventilating and air conditioning installations in buildings, aimed at narrowing the performance gap by employing data-driven methods to increase their context awareness, allowing management systems to steer the operation towards higher efficiency. This includes the advancement of modeling methodologies capable of extracting actionable knowledge from historical building behavior databases, through load forecasting and equipment operational performance estimation supporting the identification of a building’s context and energetic needs, and the development of a generalizable multi-objective optimization strategy aimed at meeting these needs while minimizing the consumption of energy. The experimental results obtained from the implementation of the developed methodologies show a significant potential for increasing energy efficiency of heating, ventilating and air conditioning systems while being sufficiently generic to support their usage in different installations having diverse equipment. In conclusion, a complete analysis and actuation framework was developed, implemented and validated by means of an experimental database acquired from a pilot plant during the research period of this thesis. The obtained results demonstrate the efficacy of the proposed standalone contributions, and as a whole represent a suitable solution for helping to increase the performance of heating, ventilating and air conditioning installations without affecting the comfort of their occupants.
L’eficàcia dels sistemes de gestió d’energia per afrontar el consum d’energia en edificis és un tema que ha rebut un interès en augment durant els darrers anys a causa de la creixent demanda global d’energia i del gran percentatge d’energia que n’utilitzen actualment els edificis. L’escala d’aquest sector ha atret l'atenció de nombrosa investigació amb l’objectiu de descobrir possibles vies de millora i materialitzar-les amb l’ajuda de recents avenços tecnològics que es podrien aprofitar per disminuir les necessitats energètiques dels edificis. Concretament, en l’àrea d’instal·lacions de calefacció, ventilació i climatització, la disponibilitat de grans bases de dades històriques als sistemes de gestió d’edificis fa possible l’estudi de com d'eficients són realment aquests sistemes quan s’encarreguen d'assegurar el confort dels seus ocupants. En realitat, informes recents indiquen que hi ha una diferència entre el rendiment operatiu ideal i el rendiment generalment assolit a la pràctica. En conseqüència, aquesta tesi considera la investigació de noves estratègies de gestió de l’energia per a instal·lacions de calefacció, ventilació i climatització en edificis, destinades a reduir la diferència de rendiment mitjançant l’ús de mètodes basats en dades per tal d'augmentar el seu coneixement contextual, permetent als sistemes de gestió dirigir l’operació cap a zones de treball amb un rendiment superior. Això inclou tant l’avanç de metodologies de modelat capaces d’extreure coneixement de bases de dades de comportaments històrics d’edificis a través de la previsió de càrregues de consum i l’estimació del rendiment operatiu dels equips que recolzin la identificació del context operatiu i de les necessitats energètiques d’un edifici, tant com del desenvolupament d’una estratègia d’optimització multi-objectiu generalitzable per tal de minimitzar el consum d’energia mentre es satisfan aquestes necessitats energètiques. Els resultats experimentals obtinguts a partir de la implementació de les metodologies desenvolupades mostren un potencial important per augmentar l'eficiència energètica dels sistemes de climatització, mentre que són prou genèrics com per permetre el seu ús en diferents instal·lacions i suportant equips diversos. En conclusió, durant aquesta tesi es va desenvolupar, implementar i validar un marc d’anàlisi i actuació complet mitjançant una base de dades experimental adquirida en una planta pilot durant el període d’investigació de la tesi. Els resultats obtinguts demostren l’eficàcia de les contribucions de manera individual i, en conjunt, representen una solució idònia per ajudar a augmentar el rendiment de les instal·lacions de climatització sense afectar el confort dels seus ocupants
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Books on the topic "Energy Efficient Machine Learning System"

1

The design and analysis of efficient learning algorithms. Cambridge, Mass: MIT Press, 1992.

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Awad, Mariette. Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers. Springer Nature, 2015.

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Khanna, Rahul, and Mariette Awad. Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers. Apress, 2015.

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Kumar, C. Daniel Nesa, 1st. Performance Measure and Analysis on Machine Learning Techniques for Energy Efficient Secured Multipath Multicast Routing in MANET. Selfypage Developers Pvt Ltd, 2022.

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Gershman, Samuel. What Makes Us Smart. Princeton University Press, 2021. http://dx.doi.org/10.23943/princeton/9780691205717.001.0001.

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At the heart of human intelligence rests a fundamental puzzle: How are we incredibly smart and stupid at the same time? No existing machine can match the power and flexibility of human perception, language, and reasoning. Yet, we routinely commit errors that reveal the failures of our thought processes. This book makes sense of this paradox by arguing that our cognitive errors are not haphazard. Rather, they are the inevitable consequences of a brain optimized for efficient inference and decision making within the constraints of time, energy, and memory—in other words, data and resource limitations. Framing human intelligence in terms of these constraints, the book shows how a deeper computational logic underpins the “stupid” errors of human cognition. Embarking on a journey across psychology, neuroscience, computer science, linguistics, and economics, the book presents unifying principles that govern human intelligence. First, inductive bias: any system that makes inferences based on limited data must constrain its hypotheses in some way before observing data. Second, approximation bias: any system that makes inferences and decisions with limited resources must make approximations. Applying these principles to a range of computational errors made by humans, the book demonstrates that intelligent systems designed to meet these constraints yield characteristically human errors. Examining how humans make intelligent and maladaptive decisions, the book delves into the successes and failures of cognition.
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Delgado Martín, Jordi, Andrea Muñoz-Ibáñez, and Ismael Himar Falcón-Suárez. 6th International Workshop on Rock Physics: A Coruña, Spain 13 -17 June 2022: Book of Abstracts. 2022nd ed. Servizo de Publicacións da UDC, 2022. http://dx.doi.org/10.17979/spudc.000005.

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[Abstract] The 6th International Workshop on Rock Physics (6IWRP) was held A Coruña, Spain, between 13th and 17th of June, 2022. This meeting follows the track of the five successful encounters held in Golden (USA, 2011), Southampton (UK, 2013), Perth (Australia, 2015), Trondheim (Norway, 2017) and Hong Kong (China, 2019). The aim of the workshop was to bring together experiences allowing to illustrate, discuss and exchange recent advances in the wide realm of rock physics, including theoretical developments, in situ and laboratory scale experiments as well as digital analysis. While rock physics is at the core of the oil & gas industry applications, it is also essential to enable the energy transition challenge (e.g. CO2 and H2 storage, geothermal), ensure a safe and adequate use of natural resources and develop efficient waste management strategies. The topics of 6IWRP covered a broad spectrum of rock physics-related research activities, including: • Experimental rock physics. New techniques, approaches and applications; Characterization of the static and dynamic properties of rocks and fluids; Multiphysics measurements (NMR, electrical resistivity…); Deep/crustal scale rock physics. • Modelling and multiscale applications: from the lab to the field. Numerical analysis and model development; Data science applications; Upscaling; Microseismicity and earthquakes; Subsurface stresses and tectonic deformations. • Coupled phenomena and rock properties: exploring interactions. Anisotropy; Flow and fractures; Temperature effects; Rock-fluid interaction; Fluid and pressure effects on geophysical signatures. • The energy transition challenge. Applications to energy storage (hydrogen storage in porous media), geothermal resources, energy production (gas hydrates), geological utilization and storage of CO2, nuclear waste disposal. • Rock physics templates: advances and applications. Quantitative assessment; Applications to reser voir characterization (role of seismic wave anisotropy and fracture networks). • Advanced rock physics tools. Machine learning; application of imaging (X-ray CT, X-ray μCT, FIB-SEM…) to obtain rock proper ties. This book compiles more than 50 abstracts, summarizing the works presented in the 6IWRP by rock physicists from all over the world, belonging to both academia and industry. This book means an updated overview of the rock physics research worldwide.
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Shengelia, Revaz. Modern Economics. Universal, Georgia, 2021. http://dx.doi.org/10.36962/rsme012021.

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Economy and mankind are inextricably interlinked. Just as the economy or the production of material wealth is unimaginable without a man, so human existence and development are impossible without the wealth created in the economy. Shortly, both the goal and the means of achieving and realization of the economy are still the human resources. People have long ago noticed that it was the economy that created livelihoods, and the delays in their production led to the catastrophic events such as hunger, poverty, civil wars, social upheavals, revolutions, moral degeneration, and more. Therefore, the special interest of people in understanding the regulatory framework of the functioning of the economy has existed and exists in all historical epochs [A. Sisvadze. Economic theory. Part One. 2006y. p. 22]. The system of economic disciplines studies economy or economic activities of a society. All of them are based on science, which is currently called economic theory in the post-socialist space (the science of economics, the principles of economics or modern economics), and in most countries of the world - predominantly in the Greek-Latin manner - economics. The title of the present book is also Modern Economics. Economics (economic theory) is the science that studies the efficient use of limited resources to produce and distribute goods and services in order to satisfy as much as possible the unlimited needs and demands of the society. More simply, economics is the science of choice and how society manages its limited resources. Moreover, it should be emphasized that economics (economic theory) studies only the distribution, exchange and consumption of the economic wealth (food, beverages, clothing, housing, machine tools, computers, services, etc.), the production of which is possible and limited. And the wealth that exists indefinitely: no economic relations are formed in the production and distribution of solar energy, air, and the like. This current book is the second complete updated edition of the challenges of the modern global economy in the context of the coronary crisis, taking into account some of the priority directions of the country's development. Its purpose is to help students and interested readers gain a thorough knowledge of economics and show them how this knowledge can be applied pragmatically (professionally) in professional activities or in everyday life. To achieve this goal, this textbook, which consists of two parts and tests, discusses in simple and clear language issues such as: the essence of economics as a science, reasons for origin, purpose, tasks, usefulness and functions; Basic principles, problems and peculiarities of economics in different economic systems; Needs and demand, the essence of economic resources, types and limitations; Interaction, mobility, interchangeability and efficient use of economic resources. The essence and types of wealth; The essence, types and models of the economic system; The interaction of households and firms in the market of resources and products; Market mechanism and its elements - demand, supply and price; Demand and supply elasticity; Production costs and the ways to reduce them; Forms of the market - perfect and incomplete competition markets and their peculiarities; Markets for Production Factors and factor incomes; The essence of macroeconomics, causes and importance of origin; The essence and calculation of key macroeconomic indicators (gross national product, gross domestic product, net national product, national income, etc.); Macroeconomic stability and instability, unemployment, inflation and anti-inflationary policies; State regulation of the economy and economic policy; Monetary and fiscal policy; Income and standard of living; Economic Growth; The Corona Pandemic as a Defect and Effect of Globalization; National Economic Problems and New Opportunities for Development in the conditions of the Coronary Crisis; The Socio-economic problems of moral obsolescence in digital technologies; Education and creativity are the main solution way to overcome the economic crisis caused by the coronavirus; Positive and negative effects of tourism in Georgia; Formation of the middle class as a contributing factor to the development of tourism in Georgia; Corporate culture in Georgian travel companies, etc. The axiomatic truth is that economics is the union of people in constant interaction. Given that the behavior of the economy reflects the behavior of the people who make up the economy, after clarifying the essence of the economy, we move on to the analysis of the four principles of individual decision-making. Furtermore, the book describes how people make independent decisions. The key to making an individual decision is that people have to choose from alternative options, that the value of any action is measured by the value of what must be given or what must be given up to get something, that the rational, smart people make decisions based on the comparison of the marginal costs and marginal returns (benefits), and that people behave accordingly to stimuli. Afterwards, the need for human interaction is then analyzed and substantiated. If a person is isolated, he will have to take care of his own food, clothes, shoes, his own house and so on. In the case of such a closed economy and universalization of labor, firstly, its productivity will be low and, secondly, it will be able to consume only what it produces. It is clear that human productivity will be higher and more profitable as a result of labor specialization and the opportunity to trade with others. Indeed, trade allows each person to specialize, to engage in the activities that are most successful, be it agriculture, sewing or construction, and to buy more diverse goods and services from others at a relatively lower price. The key to such human interactions is that trade is mutually beneficial; That markets are usually the good means of coordination between people and that the government can improve the results of market functioning if the market reveals weakness or the results of market functioning are not fair. Moroever, it also shows how the economy works as a whole. In particular, it is argued that productivity is a key determinant of living standards, that an increase in the money supply is a major source of inflation, and that one of the main impediments to avoiding inflation is the existence of an alternative between inflation and unemployment in the short term, that the inflation decrease causes the temporary decline in unemployement and vice versa. The Understanding creatively of all above mentioned issues, we think, will help the reader to develop market economy-appropriate thinking and rational economic-commercial-financial behaviors, to be more competitive in the domestic and international labor markets, and thus to ensure both their own prosperity and the functioning of the country's economy. How he/she copes with the tasks, it is up to the individual reader to decide. At the same time, we will receive all the smart useful advices with a sense of gratitude and will take it into account in the further work. We also would like to thank the editor and reviewers of the books. Finally, there are many things changing, so it is very important to realize that the XXI century has come: 1. The century of the new economy; 2. Age of Knowledge; 3. Age of Information and economic activities are changing in term of innovations. 1. Why is the 21st century the century of the new economy? Because for this period the economic resources, especially non-productive, non-recoverable ones (oil, natural gas, coal, etc.) are becoming increasingly limited. According to the World Energy Council, there are currently 43 years of gas and oil reserves left in the world (see “New Commersant 2007 # 2, p. 16). Under such conditions, sustainable growth of real gross domestic product (GDP) and maximum satisfaction of uncertain needs should be achieved not through the use of more land, labor and capital (extensification), but through more efficient use of available resources (intensification) or innovative economy. And economics, as it was said, is the science of finding the ways about the more effective usage of the limited resources. At the same time, with the sustainable growth and development of the economy, the present needs must be met in a way that does not deprive future generations of the opportunity to meet their needs; 2. Why is the 21st century the age of knowledge? Because in a modern economy, it is not land (natural resources), labor and capital that is crucial, but knowledge. Modern production, its factors and products are not time-consuming and capital-intensive, but science-intensive, knowledge-intensive. The good example of this is a Japanese enterprise (firm) where the production process is going on but people are almost invisible, also, the result of such production (Japanese product) is a miniature or a sample of how to get the maximum result at the lowest cost; 3. Why is the 21st century the age of information? Because the efficient functioning of the modern economy, the effective organization of the material and personal factors of production largely depend on the right governance decision. The right governance decision requires prompt and accurate information. Gone are the days when the main means of transport was a sailing ship, the main form of data processing was pencil and paper, and the main means of transmitting information was sending letters through a postman on horseback. By the modern transport infrastructure (highways, railways, ships, regular domestic and international flights, oil and gas pipelines, etc.), the movement of goods, services and labor resoucres has been significantly accelerated, while through the modern means of communication (mobile phone, internet, other) the information is spreading rapidly globally, which seems to have "shrunk" the world and made it a single large country. The Authors of the book: Ushangi Samadashvili, Doctor of Economic Sciences, Associate Professor of Ivane Javakhishvili Tbilisi State University - Introduction, Chapters - 1, 2, 3, 4, 5, 6, 9, 10, 11,12, 15,16, 17.1,18 , Tests, Revaz Shengelia, Doctor of Economics, Professor of Georgian Technical University, Chapters_7, 8, 13. 14, 17.2, 17.4; Zhuzhuna Tsiklauri - Doctor of Economics, Professor of Georgian Technical University - Chapters 13.6, 13.7,17.2, 17.3, 18. We also thank the editor and reviewers of the book.
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Book chapters on the topic "Energy Efficient Machine Learning System"

1

Berral, Josep Ll, Iñigo Goiri, Ramon Nou, Ferran Julià, Josep O. Fitó, Jordi Guitart, Ricard Gavaldá, and Jordi Torres. "Toward Energy-Aware Scheduling Using Machine Learning." In Energy-Efficient Distributed Computing Systems, 215–44. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118342015.ch8.

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Kruglov, Artem, Giancarlo Succi, and Gcinizwe Dlamini. "System Energy Consumption Measurement." In Developing Sustainable and Energy-Efficient Software Systems, 27–38. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11658-2_3.

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AbstractOver the years, the task to reduce energy consumed by a system has been mainly assigned to computer hardware developers. This is mainly because it is believed that the hardware is the principal component that consumes more electrical energy. However, the software also plays a vital role in power usage. Hardware works hand in hand with software programs. It has become equally important to estimate the energy consumed as a whole using artificial intelligence-based approaches. Machine learning is presented as one of the scalable approaches toward efficiently and accurately estimating energy consumed in the software development domain.
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Chakraborty, Indrasis, Aritra Dasgupta, Javier Rubio-Herrero, Sai Pushpak Nandanoori, Soumya Kundu, and Vikas Chandan. "Application of Machine Learning for Energy-Efficient Buildings." In Handbook of Smart Energy Systems, 837–58. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-030-97940-9_102.

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Chakraborty, Indrasis, Aritra Dasgupta, Javier Rubio-Herrero, Sai Pushpak Nandanoori, Soumya Kundu, and Vikas Chandan. "Application of Machine Learning for Energy-Efficient Buildings." In Handbook of Smart Energy Systems, 1–22. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-72322-4_102-1.

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Wang, Xi, Xiangbin Yu, Tao Teng, and Guangying Wang. "Energy-Efficient Power Allocation Scheme Based on Discrete-Rate Adaptive Modulation in Distributed Antenna System." In Machine Learning and Intelligent Communications, 284–92. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00557-3_29.

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Trenz, André, Christoph Hoffmann, Christopher Lange, and Richard Öchsner. "Increasing Energy Efficiency and Flexibility by Forecasting Production Energy Demand Based on Machine Learning." In Lecture Notes in Mechanical Engineering, 449–56. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-28839-5_50.

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AbstractThe ability of manufacturing companies to compete depends strongly on the efficient use of production resources and the flexibility to adapt to changing production conditions. Essential requirements for the energetic infrastructure (EGI) result from the production itself, e.g., security of supply, efficiency and peak shaving. Since production always takes priority and must not be disturbed, the flexibility potential in terms of energy efficiency lies primarily in the EGI. Based on this, strategies will be developed that support companies in increasing their efficiency and flexibility by optimizing the configuration and operation of the EGI, while production processes are reliably supplied and not adapted. This is reached with intelligent operation strategies for the heating and cooling network based on forecasts, the use of energy storage systems, and the coupling of energy sectors. This paper presents an approach for energy forecasts used for the optimization of operation strategies. Hence, an energy-forecast-tool was developed, which is used for the prediction of electrical and thermal loads depending on the expected production. Therefore, machine learning models are trained with past weather, energy, and production data. Using production planning data and weather forecasts, the model can predict energy demands as input for an EGI optimization.
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Loni, Mohammad, Ali Zoljodi, Sima Sinaei, Masoud Daneshtalab, and Mikael Sjödin. "NeuroPower: Designing Energy Efficient Convolutional Neural Network Architecture for Embedded Systems." In Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation, 208–22. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30487-4_17.

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Ioshchikhes, Borys, Daniel Piendl, Henrik Schmitz, Jasper Heiland, and Matthias Weigold. "Development of a Holistic Framework for Identifying Energy Efficiency Potentials of Production Machines." In Lecture Notes in Mechanical Engineering, 431–39. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-28839-5_48.

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AbstractA prerequisite to identify energy efficiency potentials and to improve energy efficiency is the measurement and analysis of the energy demand. However, in industrial practice, approaches to identify energy efficiency measures of production machines are associated with high costs for metering equipment and time consuming analysis requiring expertise. Against this background, this paper describes a comprehensive and cost-efficient framework from acquisition to analysis of energy data to serve as a starting point to increase energy efficiency in manufacturing. For this purpose, an energy transparency and analysis system is being developed that can measure, record and analyze electrical quantities. The validity of the data acquisition can be verified by utilizing a Raspberry Pi as a low-cost edge analyzer device. Measurement data is stored with associated metadata in a SQLite database for subsequent processing in a Python-based web application, in which machine learning algorithms can be deployed. The algorithms can be used to process vast amounts of data and to provide a basis for calculating energy performance indicators to reveal energy efficiency potentials. The overall workflow is validated using a lathe and a cleaning machine within the ETA Research Factory at the Technical University of Darmstadt.
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Behura, Aradhana, and Manas Ranjan Kabat. "Energy-Efficient Optimization-Based Routing Technique for Wireless Sensor Network Using Machine Learning." In Advances in Intelligent Systems and Computing, 555–65. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2414-1_56.

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Araghi, Farhang Motallebi, Aaron Rabinwoitz, Chon Chia Ang, Sachin Sharma, Parth Kadav, Richard T. Meyer, Thomas Bradley, and Zachary D. Asher. "Identifying and Assessing Research Gaps for Energy Efficient Control of Electrified Autonomous Vehicle Eco-Driving." In Machine Learning and Optimization Techniques for Automotive Cyber-Physical Systems, 759–86. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-28016-0_27.

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Conference papers on the topic "Energy Efficient Machine Learning System"

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Hussein, Ramy, Rabab Ward, Z. Jane Wand, and Amr Mohamed. "Energy Efficient EEG Monitoring System for Wireless Epileptic Seizure Detection." In 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2016. http://dx.doi.org/10.1109/icmla.2016.0055.

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Ramkumar, S., G. Emayavaramban, K. Sathesh Kumar, K. Shankar, M. Ilayaraja, P. Sriramakrishnan, and J. Macklin Abraham Navamani. "Designing Communication System for Person with Locked in Syndrome Using Machine Learning Technique." In 2019 IEEE International Conference on Clean Energy and Energy Efficient Electronics Circuit for Sustainable Development (INCCES). IEEE, 2019. http://dx.doi.org/10.1109/incces47820.2019.9167686.

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Zhou, Shiyang, Yufan Cheng, and Xia Lei. "Model-Based Machine Learning for Energy-Efficient UAV Placement." In 2022 7th International Conference on Computer and Communication Systems (ICCCS). IEEE, 2022. http://dx.doi.org/10.1109/icccs55155.2022.9846781.

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Murthy, Akshay, Curtis Green, Radu Stoleru, Suman Bhunia, Charles Swanson, and Theodora Chaspari. "Machine Learning-based Irrigation Control Optimization." In BuildSys '19: The 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3360322.3360854.

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Wissing, J., and S. Scheele. "A4.1 - Boosting Energy Efficient Machine Learning in Smart Sensor Systems." In SMSI 2023. AMA Service GmbH, Von-Münchhausen-Str. 49, 31515 Wunstorf, Germany, 2023. http://dx.doi.org/10.5162/smsi2023/a4.1.

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Gryzlov, Anton, Liliya Mironova, Sergey Safonov, and Muhammad Arsalan. "Evaluation of Machine Learning Methods for Prediction of Multiphase Production Rates." In SPE Symposium: Artificial Intelligence - Towards a Resilient and Efficient Energy Industry. SPE, 2021. http://dx.doi.org/10.2118/208648-ms.

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Abstract Multiphase flow metering is an important tool for production monitoring and optimization. Although there are many technologies available on the market, the existing multiphase meters are only accurate to a certain extend and generally are expensive to purchase and maintain. Virtual flow metering (VFM) is a low-cost alternative to conventional production monitoring tools, which relies on mathematical modelling rather than the use of hardware instrumentation. Supported by the availability of the data from different sensors and production history, the development of different virtual flow metering systems has become a focal point for many companies. This paper discusses the importance of flow modelling for virtual flow metering. In addition, main data-driven algorithms are introduced for the analysis of several dynamic production data sets. Artificial Neural Networks (ANN) together with advanced machine learning methods such as GRU and XGBoost have been considered as possible candidates for virtual flow metering. The obtained results indicate that the machine learning algorithms estimate oil, gas and water rates with acceptable accuracy. The feasibility of the data-driven virtual metering approach for continuous production monitoring purposes has been demonstrated via a series of simulation-based cases. Amongst the used algorithms the deep learning methods provided the most accurate results combined with reasonable time for model training.
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Osta, Mario, Mohamad Alameh, Hamoud Younes, Ali Ibrahim, and Maurizio Valle. "Energy Efficient Implementation of Machine Learning Algorithms on Hardware Platforms." In 2019 26th IEEE International Conference on Electronics, Circuits and Systems (ICECS). IEEE, 2019. http://dx.doi.org/10.1109/icecs46596.2019.8965157.

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Jiang, Shixiong, Sheena Ratnam Priya, Naveena Elango, James Clay, and Ramalingam Sridhar. "An Energy Efficient In-Memory Computing Machine Learning Classifier Scheme." In 2019 32nd International Conference on VLSI Design and 2019 18th International Conference on Embedded Systems (VLSID). IEEE, 2019. http://dx.doi.org/10.1109/vlsid.2019.00046.

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Herzog, Benedict, Stefan Reif, Fabian Hügel, Timo Hönig, and Wolfgang Schröder-Preikschat. "Towards Automated System-Level Energy-Efficiency Optimisation using Machine Learning." In e-Energy '21: The Twelfth ACM International Conference on Future Energy Systems. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3447555.3466566.

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Fayzrakhmanov, Rustam Abubakirovich, Polina Yurievna Fominykh, Daniil Sergeevich Kurushin, Ekaterina Dmitrievna Orlova, Olga Vladimirovna Soboleva, and Denis Vladimirovich Yarullin. "Machine Learning for Building Literary Mapping Geoinformation System." In 2020 2nd International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency (SUMMA). IEEE, 2020. http://dx.doi.org/10.1109/summa50634.2020.9280665.

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Reports on the topic "Energy Efficient Machine Learning System"

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Choquette, Gary. PR-000-16209-WEB Data Management Best Practices Learned from CEPM. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), April 2019. http://dx.doi.org/10.55274/r0011568.

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DATE: Wednesday, May 1, 2019 TIME: 2:00 - 3:30 p.m. ET PRESENTER: Gary Choquette, PRCI CLICK DOWNLOAD/BUY TO ACCESS THE REGISTRATION LINK FOR THIS WEBINAR Systems that manage large sets of data are becoming more common in the energy transportation industry. Having access to the data offers the opportunity to learn from previous experiences to help efficiently manage the future. But how does one manage to digest copious quantities of data to find nuggets within the ore? This webinar will outline some of the data management best practices learned from the research projects associated with CEPM. - Logging/capturing data tips - Techniques to identify 'bad' data - Methods of mapping equipment and associated regressions - Tips for pre-processing data for regressions - Machine learning tips - Establishing alarm limits - Identifying equipment problems - Multiple case studies Who Should Attend? - Data analysts - Equipment support specialists - Those interested in learning more about 'big data' and 'machine learning' Recommended Pre-reading: - PR-309-11202-R01 Field Demonstration Test of Advanced Engine and Compressor Diagnostics for CORE - PR-312-12210-R01 CEPM Monitoring Plan for 2SLB Reciprocating Engines* - PR-309-13208-R01 Field Demonstration of Integrated System and Expert Level Continuous Performance Monitoring for CORE* - PR-309-14209-R01 Field Demo of Integrated Expert Level Continuous Performance Monitoring - PR-309-15205-R01 Continuous Engine Performance Monitoring Technical Specification - PR-000-15208-R01 Reciprocating Engine Speed Stability as a Measure of Combustion Stability - PR-309-15209-R01 Evaluation of NSCR Specific Models for Use in CEPM - PR-000-16209-R01 Demonstration of Continuous Equipment Performance Monitoring - PR-015-17606-Z02 Elbow Meter Test Results* *Documents available to PRCI member only Attendance will be limited to the first 500 registrants to join the webinar. All remaining registrants will receive a link to view the recording after the webinar. Not able to attend? Register anyway to automatically receive a link to the recording after the webinar to view at your convenience! After registering, you will receive a confirmation email containing information about joining the webinar. Please visit our website for other webinars that may be of interest to you!
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Aguiar, Brandon, Paul Bianco, and Arvind Agarwal. Using High-Speed Imaging and Machine Learning to Capture Ultrasonic Treatment Cavitation Area at Different Amplitudes. Florida International University, October 2021. http://dx.doi.org/10.25148/mmeurs.009773.

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The ultrasonic treatment process strengthens metals by increasing nucleation and decreasing grain size in an energy efficient way, without having to add anything to the material. The goal of this research endeavor was to use machine learning to automatically measure cavitation area in the Ultrasonic Treatment process to understand how amplitude influences cavitation area. For this experiment, a probe was placed into a container filled with turpentine because it has a similar viscosity to liquid aluminum. The probe gyrates up and down tens of micrometers at a frequency of 20 kHz, which causes cavitations to form in the turpentine. Each experimental trial ran for 5 seconds. We took footage on a high-speed camera running the UST probe from 20% to 35% amplitude in increments of 1%. Our research examined how the amplitude of the probe changed the cavitation area per unit time. It was vital to get a great contrast between the cavitations and the turpentine so that we could train a machine learning model to measure the cavitation area in a software called Dragonfly. We observed that as amplitude increased, average cavitation area also increased. Plotting cavitation area versus time shows that the cavitation area for a given amplitude increases and decreases in a wave-like pattern as time passes.
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Yang, Yu, and Hen-Geul Yeh. Electrical Vehicle Charging Infrastructure Design and Operations. Mineta Transportation Institute, July 2023. http://dx.doi.org/10.31979/mti.2023.2240.

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California aims to achieve five million zero-emission vehicles (ZEVs) on the road by 2030 and 250,000 electrical vehicle (EV) charging stations by 2025. To reduce barriers in this process, the research team developed a simulation-based system for EV charging infrastructure design and operations. The increasing power demand due to the growing EV market requires advanced charging infrastructures and operating strategies. This study will deliver two modules in charging station design and operations, including a vehicle charging schedule and an infrastructure planning module for the solar-powered charging station. The objectives are to increase customers’ satisfaction, reduce the power grid burden, and maximize the profitability of charging stations using state-of-the-art global optimization techniques, machine-learning-based solar power prediction, and model predictive control (MPC). The proposed research has broad societal impacts and significant intellectual merits. First, it meets the demand for green transportation by increasing the number of EV users and reducing the transportation sector’s impacts on climate change. Second, an optimal scheduling tool enables fast charging of EVs and thus improves the mobility of passengers. Third, the designed planning tools enable an optimal design of charging stations equipped with a solar panel and battery energy storage system (BESS) to benefit nationwide transportation system development.
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