scholarly journals Using Different ML Algorithms and Hyperparameter Optimization to Predict Heat Meters’ Failures

2019 ◽  
Vol 9 (18) ◽  
pp. 3719
Author(s):  
Przemysław Pałasz ◽  
Radosław Przysowa
Keyword(s):  
Failure Rate ◽  
High Performance ◽  
High Efficiency ◽  
Measurement Data ◽  
Heating System ◽  
Active Management ◽  
Large Set ◽  
State Variables ◽  
Hyperparameter Optimization ◽  
Heat Meters

The need to increase the energy efficiency of buildings, as well as the use of local renewable heat sources has caused heat meters to be used not only to calculate the consumed energy, but also for the active management of central heating systems. Increasing the reading frequency and the use of measurement data to control the heating system expands the requirements for the reliability of heat meters. The aim of the research is to analyze a large set of meters in the real network and predict their faults to avoid inaccurate readings, incorrect billing, heating system disruption, and unnecessary maintenance. The reliability analysis of heat meters, based on historical data collected over several years, shows some regularities, which cannot be easily described by physics-based models. The failure rate is almost constant and does depend on the past, but is a non-linear combination of state variables. To predict meters’ failures in the next billing period, three independent machine learning models are implemented and compared with selected metrics, because even the high performance of a single model (87% true positive for neural network) may be insufficient to make a maintenance decision. Additionally, performing hyperparameter optimization boosts the models’ performance by a few percent. Finally, three improved models are used to build an ensemble classifier, which outperforms the individual models. The proposed procedure ensures the high efficiency of fault detection (>95%), while maintaining overfitting at the minimum level. The methodology is universal and can be utilized to study the reliability and predict faults of other types of meters and different objects with the constant failure rate.

scholarly journals Using Different ML Algorithms and Hyperparameter Optimisation to Predict Heat Meters' Failures

2019 ◽  
Author(s):  
Przemysław Pałasz ◽  
Radosław Przysowa
Keyword(s):  
Failure Rate ◽  
High Performance ◽  
High Efficiency ◽  
Measurement Data ◽  
Heating System ◽  
Ensemble Classifier ◽  
Active Management ◽  
Large Set ◽  
State Variables ◽  
Heat Meters

The need to increase the energy efficiency of buildings as well as the use of local renewable heat sources has caused that heat meters are used not only to calculate the consumed energy but also for the active management of central heating systems. Increasing the reading frequency and the use of measurement data to control the heating system expands the requirements for the reliability of heat meters. The aim of the research is to analyse a large set of meters in the real network and predict their faults to avoid inaccurate readings, incorrect billing, heating system disruption and unnecessary maintenance. The reliability analysis of heat metres, based on historical data collected over several years, shows some regularities which cannot be easily described by physics-based models. The failure rate is almost constant and does depend on the past but is a non-linear combination of state variables. To predict meters' failures in the next settlement period, three independent machine learning models are implemented and compared with selected metrics because even the high performance of a single model (87\% True Positive for Neural Network) may be insufficient to make a maintenance decision. Additionally, performing hyperparameters optimisation boosts models' performance by a few percent. Finally, three improved models are used to build an ensemble classifier which outperforms the individual models. The proposed procedure ensures the high efficiency of fault detection (>95\%), while maintaining overfitting at the minimum level. The methodology is universal and can be utilised to study the reliability and predict faults of other types of meters and different objects with the constant failure rate.

High-Performance Hardware Interpolation Architecture for High Efficiency Video Coding Decoder

2016 ◽  
Vol 11 (9) ◽  
pp. 764
Author(s):  
Lella Aicha Ayadi ◽  
Nihel Neji ◽  
Hassen Loukil ◽  
Mouhamed Ali Ben Ayed ◽  
Nouri Masmoudi
Keyword(s):  
Video Coding ◽  
High Performance ◽  
High Efficiency ◽  
High Efficiency Video Coding

scholarly journals Investigation on the La Replacement and Little Additive Modification of High-Performance Permanent Magnetic Strontium-Ferrite

Processes ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 1034
Author(s):  
Ching-Chien Huang ◽  
Chin-Chieh Mo ◽  
Guan-Ming Chen ◽  
Hsiao-Hsuan Hsu ◽  
Guo-Jiun Shu
Keyword(s):  
High Performance ◽  
Single Phase ◽  
Permanent Magnets ◽  
High Efficiency ◽  
Cobalt Content ◽  
Preparation Condition ◽  
Milling Process ◽  
Experimental Parameters ◽  
La Substitution ◽  
Hard Magnets

In this work, an experiment was carried out to investigate the preparation condition of anisotropic, Fe-deficient, M-type Sr ferrite with optimum magnetic and physical properties by changing experimental parameters, such as the La substitution amount and little additive modification during fine milling process. The compositions of the calcined ferrites were chosen according to the stoichiometry LaxSr1-xFe12-2xO19, where M-type single-phase calcined powder was synthesized with a composition of x = 0.30. The effect of CaCO3, SiO2, and Co3O4 inter-additives on the Sr ferrite was also discussed in order to obtain low-temperature sintered magnets. The magnetic properties of Br = 4608 Gauss, bHc = 3650 Oe, iHc = 3765 Oe, and (BH)max = 5.23 MGOe were obtained for Sr ferrite hard magnets with low cobalt content at 1.7 wt%, which will eventually be used as high-end permanent magnets for the high-efficiency motor application in automobiles with Br > 4600 ± 50 G and iHc > 3600 ± 50 Oe.

New hole transport styrene polymers bearing highly π-extended conjugated side-chain moieties for high-performance solution-processable thermally activated delayed fluorescence OLEDs

2021 ◽  
Vol 12 (11) ◽  
pp. 1692-1699
Author(s):  
Ji Hye Lee ◽  
Jinhyo Hwang ◽  
Chai Won Kim ◽  
Amit Kumar Harit ◽  
Han Young Woo ◽  
...  
Keyword(s):  
High Performance ◽  
High Efficiency ◽  
Delayed Fluorescence ◽  
Hole Transport ◽  
Side Chain ◽  
Solution Processed ◽  
Thermally Activated Delayed Fluorescence ◽  
Thermally Activated ◽  
Turn On ◽  
Solution Processable

New polystyrene-based polymers with high π-extended hole transport pendants were synthesized to obtain a low turn-on voltage and high efficiency in solution-processed green TADF-OLEDs.

scholarly journals Data-Driven Intelligent 3D Surface Measurement in Smart Manufacturing: Review and Outlook

Machines ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 13
Author(s):  
Yuhang Yang ◽  
Zhiqiao Dong ◽  
Yuquan Meng ◽  
Chenhui Shao
Keyword(s):  
High Resolution ◽  
High Performance ◽  
Sampling Design ◽  
Three Dimensional ◽  
Measurement Data ◽  
Data Driven ◽  
Surface Measurement ◽  
Smart Manufacturing ◽  
Future Research ◽  
3D Surface

High-fidelity characterization and effective monitoring of spatial and spatiotemporal processes are crucial for high-performance quality control of many manufacturing processes and systems in the era of smart manufacturing. Although the recent development in measurement technologies has made it possible to acquire high-resolution three-dimensional (3D) surface measurement data, it is generally expensive and time-consuming to use such technologies in real-world production settings. Data-driven approaches that stem from statistics and machine learning can potentially enable intelligent, cost-effective surface measurement and thus allow manufacturers to use high-resolution surface data for better decision-making without introducing substantial production cost induced by data acquisition. Among these methods, spatial and spatiotemporal interpolation techniques can draw inferences about unmeasured locations on a surface using the measurement of other locations, thus decreasing the measurement cost and time. However, interpolation methods are very sensitive to the availability of measurement data, and their performances largely depend on the measurement scheme or the sampling design, i.e., how to allocate measurement efforts. As such, sampling design is considered to be another important field that enables intelligent surface measurement. This paper reviews and summarizes the state-of-the-art research in interpolation and sampling design for surface measurement in varied manufacturing applications. Research gaps and future research directions are also identified and can serve as a fundamental guideline to industrial practitioners and researchers for future studies in these areas.

scholarly journals Neighborhood Energy Modeling and Monitoring: A Case Study

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3716
Author(s):  
Francesco Causone ◽  
Rossano Scoccia ◽  
Martina Pelle ◽  
Paola Colombo ◽  
Mario Motta ◽  
...  
Keyword(s):  
High Performance ◽  
High Efficiency ◽  
Early Stage ◽  
Energy Performance ◽  
Monitoring Plan ◽  
Performance Targets ◽  
Carrier Energy ◽  
Zero Carbon ◽  
Energy Grid

Cities and nations worldwide are pledging to energy and carbon neutral objectives that imply a huge contribution from buildings. High-performance targets, either zero energy or zero carbon, are typically difficult to be reached by single buildings, but groups of properly-managed buildings might reach these ambitious goals. For this purpose we need tools and experiences to model, monitor, manage and optimize buildings and their neighborhood-level systems. The paper describes the activities pursued for the deployment of an advanced energy management system for a multi-carrier energy grid of an existing neighborhood in the area of Milan. The activities included: (i) development of a detailed monitoring plan, (ii) deployment of the monitoring plan, (iii) development of a virtual model of the neighborhood and simulation of the energy performance. Comparisons against early-stage energy monitoring data proved promising and the generation system showed high efficiency (EER equal to 5.84), to be further exploited.

scholarly journals Iodine reduction for reproducible and high-performance perovskite solar cells and modules

2021 ◽  
Vol 7 (10) ◽  
pp. eabe8130
Author(s):  
Shangshang Chen ◽  
Xun Xiao ◽  
Hangyu Gu ◽  
Jinsong Huang
Keyword(s):  
Solar Cells ◽  
High Performance ◽  
High Efficiency ◽  
Electronic Materials ◽  
Substantial Reduction ◽  
Perovskite Solar Cells ◽  
High Yield ◽  
Operation Conditions ◽  
Record Value ◽  
Precursor Powders

Perovskite-based electronic materials and devices such as perovskite solar cells (PSCs) have notoriously bad reproducibility, which greatly impedes both fundamental understanding of their intrinsic properties and real-world applications. Here, we report that organic iodide perovskite precursors can be oxidized to I2 even for carefully sealed precursor powders or solutions, which markedly deteriorates the performance and reproducibility of PSCs. Adding benzylhydrazine hydrochloride (BHC) as a reductant into degraded precursor solutions can effectively reduce the detrimental I2 back to I−, accompanied by a substantial reduction of I3−-induced charge traps in the films. BHC residuals in perovskite films further stabilize the PSCs under operation conditions. BHC improves the stabilized efficiency of the blade-coated p-i-n structure PSCs to a record value of 23.2% (22.62 ± 0.40% certified by National Renewable Energy Laboratory), and the high-efficiency devices have a very high yield. A stabilized aperture efficiency of 18.2% is also achieved on a 35.8-cm2 mini-module.

scholarly journals Solution-Processed Transparent Conducting Electrodes for Flexible Organic Solar Cells with 16.61% Efficiency

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Juanyong Wan ◽  
Yonggao Xia ◽  
Junfeng Fang ◽  
Zhiguo Zhang ◽  
Bingang Xu ◽  
...  
Keyword(s):  
Solar Cells ◽  
Work Function ◽  
Organic Solar Cells ◽  
High Performance ◽  
High Efficiency ◽  
Electrical Stability ◽  
Solution Processed ◽  
Good Thermal Stability ◽  
Transparent Conducting ◽  
High Flexibility

AbstractNonfullerene organic solar cells (OSCs) have achieved breakthrough with pushing the efficiency exceeding 17%. While this shed light on OSC commercialization, high-performance flexible OSCs should be pursued through solution manufacturing. Herein, we report a solution-processed flexible OSC based on a transparent conducting PEDOT:PSS anode doped with trifluoromethanesulfonic acid (CF3SO3H). Through a low-concentration and low-temperature CF3SO3H doping, the conducting polymer anodes exhibited a main sheet resistance of 35 Ω sq−1 (minimum value: 32 Ω sq−1), a raised work function (≈ 5.0 eV), a superior wettability, and a high electrical stability. The high work function minimized the energy level mismatch among the anodes, hole-transporting layers and electron-donors of the active layers, thereby leading to an enhanced carrier extraction. The solution-processed flexible OSCs yielded a record-high efficiency of 16.41% (maximum value: 16.61%). Besides, the flexible OSCs afforded the 1000 cyclic bending tests at the radius of 1.5 mm and the long-time thermal treatments at 85 °C, demonstrating a high flexibility and a good thermal stability.

CTF/MWCNT hybrid multi-functional separator as high-efficiency polysulfide tamer for high-performance Li–S battery

2021 ◽  
Vol 367 ◽  
pp. 137418
Author(s):  
Qing Xuan Shi ◽  
Chen Chang ◽  
Hui Jie Pei ◽  
Xin Guan ◽  
Liang Liang Yin ◽  
...  
Keyword(s):  
High Performance ◽  
High Efficiency
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