scholarly journals Implementation of a Demand-Side Management Solution for South Korea’s Demand Response Program

2020 ◽  
Vol 10 (5) ◽  
pp. 1751 ◽  
Author(s):  
Wonsuk Ko ◽  
Hamsakutty Vettikalladi ◽  
Seung-Ho Song ◽  
Hyeong-Jin Choi
Keyword(s):  
South Korea ◽  
Real Time ◽  
Demand Response ◽  
Mean Squared Error ◽  
Demand Side Management ◽  
Demand Side ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Demand Response Program ◽  
Relative Root

In this paper, we show the development of a demand-side management solution (DSMS) for demand response (DR) aggregator and actual demand response operation cases in South Korea. To show an experience, Korea’s demand response market outline, functions of DSMS, real contracted capacity, and payment between consumer and load aggregator and DR operation cases are revealed. The DSMS computes the customer baseline load (CBL), relative root mean squared error (RRMSE), and payments of the customers in real time. The case of 10 MW contracted customers shows 108.03% delivery rate and a benefit of 854,900,394 KRW for two years. The results illustrate that an integrated demand-side management solution contributes by participating in a DR market and gives a benefit and satisfaction to the consumer.

Kalman-filter-based sensor fusion applied to road-objects detection and tracking for autonomous vehicles

2020 ◽  
pp. 095965182097552
Author(s):  
Wael Farag
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Extended Kalman Filter ◽  
Mean Squared Error ◽  
Unscented Kalman Filter ◽  
Measurement Data ◽  
Root Mean Squared Error ◽  
Time Performance ◽  
Detection And Tracking ◽  
Squared Error

In this article, a real-time road-Object Detection and Tracking (LR_ODT) method for autonomous driving is proposed. This method is based on the fusion of lidar and radar measurement data, where they are installed on the ego car, and a customized Unscented Kalman Filter is employed for their data fusion. The merits of both devices are combined using the proposed fusion approach to precisely provide both pose and velocity information for objects moving in roads around the ego car. Unlike other detection and tracking approaches, the balanced treatment of both pose estimation accuracy and its real-time performance is the main contribution in this work. The proposed technique is implemented using the high-performance language C++ and utilizes highly optimized math and optimization libraries for best real-time performance. Simulation studies have been carried out to evaluate the performance of the LR_ODT for tracking bicycles, cars, and pedestrians. Moreover, the performance of the Unscented Kalman Filter fusion is compared to that of the Extended Kalman Filter fusion showing its superiority. The Unscented Kalman Filter has outperformed the Extended Kalman Filter on all test cases and all the state variable levels (−24% average Root Mean Squared Error). The employed fusion technique shows how outstanding is the improvement in tracking performance compared to the use of a single device (−29% Root Mean Squared Error with lidar and −38% Root Mean Squared Error with radar).

scholarly journals Real-Time Load Variability Control Using Energy Storage System for Demand-Side Management in South Korea

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6292
Author(s):  
Kyo Beom Han ◽  
Jaesung Jung ◽  
Byung O Kang
Keyword(s):  
Energy Storage ◽  
South Korea ◽  
Real Time ◽  
Storage System ◽  
Demand Side Management ◽  
Energy Storage System ◽  
Time Variability ◽  
Demand Side ◽  
Neuron Network ◽  
Demand Control

In today’s power systems, the widespread adoption of smart grid applications requires sophisticated control of load variability for effective demand-side management (DSM). Conventional Energy Storage System (ESS)-based DSM methods in South Korea are limited to real-time variability control owing to difficulties with model development using customers’ load profiles from sampling with higher temporal resolution. Herein, this study thus proposes a method of controlling the variability of customers’ load profiles for real-time DSM using customer-installed ESSs. To optimize the reserved capacity for the proposed maximum demand control within ESSs, this study also proposes a hybrid method of load generation, which synthesizes approaches based on Markov Transition Matrix (MTM) and Artificial Neuron Network (ANN) to estimate load variations every 15 min and, in turn reserve capacity in ESSs. The proposed ESS-based DSM strategy primarily reserves capacity in ESSs based on estimated variation in load, and performs real-time maximum demand control with the reserved capacity during scheduled peak shaving operations. To validate the proposed methods, this study used load profiles accumulated from industrial and general (i.e., commercial) customers under the time-of-use (TOU) rate. Simulation verified the improved performance of the proposed ESS-based DSM method for all customers, and results of Kolmogorov-Smirnov (K–S) testing indicate advances in the proposed hybrid estimation beyond the stand-alone estimation using the MTM- or ANN-based approach.

scholarly journals Validasi Data Curah Hujan Satelit TRMM (Tropical Rainfall Measuring Mission) dengan Data Pos Penakar Hujan di DAS Grindulu, Kabupaten Pacitan, Jawa Timur

2021 ◽  
Vol 1 (2) ◽  
pp. 772-785
Author(s):  
Dieta Putri Jarwanti ◽  
◽  
Ery Suhartanto ◽  
Jadfan Sidqi Fidari ◽  
◽  
...  
Keyword(s):  
Real Time ◽  
Mean Squared Error ◽  
Tropical Rainfall Measuring Mission ◽  
Tropical Rainfall ◽  
Root Mean Squared Error ◽  
Squared Error

Pos penakar hujan di Indonesia lokasinya masih kurang tersebar merata, padahal data hujan yang dihasilkan sangat penting. Maka diperlukan analisis validasi dengan data satelit TRMM karena dapat mencakup wilayah luas, tersedia secara near real-time dan aksesnya yang cepat. Penelitian ini bertujuan untuk memvalidasi data satelit dengan data observasi di DAS Grindulu yang datanya dianggap lengkap dan dapat diandalkan. Nantinya digunakan untuk mengantisipasi data curah hujan observasi yang mungkin error atau tidak tersedia. Metode validasi yang digunakan berupa Root Mean Squared Error (RMSE), Uji Kesalahan Relatif (KR), Nash Sutcliffe Efficiency (NSE) serta Koefisien Korelasi (R). Penelitian ini menggunakan dua tahap perhitungan, yaitu analisis validasi data tidak terkoreksi dan data terkoreksi, dimana data terkoreksi dilakukan kalibrasi data terlebih dahulu, hasil dari validasi data TRMM terkoreksi terbaik terdapat pada periode bulanan dengan rentang kalibrasi 9 tahun dan validasi 1 tahun dengan hasil NSE = 0,929; R = 0,969; RMSE = 46,48; KR = 8,9%. Hasil tersebut menunjukkan bahwa data TRMM terkoreksi menghasilkan nilai yang lebih baik dibandingan data TRMM tidak terkoreksi karena memiliki nilai NSE dan R yang mendekati satu dan nilai RMSE dan Kesalahan Relatifnya rendah. Secara kesluruhan, dapat disimpulkan bahwa data TRMM dapat digunakan sebagai data alternatif hidrologi di DAS Grindulu.

Real-Time Wellhead Bending Moment Measurement Using Motion Reference Unit (MRU) Sensors and Machine Learning

2018 ◽  
Author(s):  
Rohit Srikonda ◽  
Rune Haakonsen ◽  
Massimiliano Russo ◽  
Peri Periyasamy
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Real Time ◽  
Mean Squared Error ◽  
Bending Moment ◽  
Sensor Data ◽  
Fe Model ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Tension Time

In order to facilitate real-time monitoring of accumulated wellhead fatigue damage, it is necessary to measure the wellhead bending moment in real-time. This paper presents a novel method to estimate the wellhead bending moment in realtime using acceleration and inclination data from the motion reference unit (MRU) sensors installed on BOP and LRJ, riser tension data and a trained neural network model. The method proposed in this paper is designed with a Recursive Neural Network (RNN) model to be trained to estimate the wellhead bending moment in real-time with high accuracy based on motion MRU sensor data and riser tension time series of a few previous cycles. In addition to the power of modeling complex nonlinearities, RNNs provide the advantage of better capturing the dynamic effects by learning to recognize the patterns in the sensor data and riser tension time series. The RNN model is trained using virtual sensor data and wellhead bending moment from a finite element (FE) model of the drilling riser subjected to irregular wave time domain analyses based on a training matrix with limited number of significant height (Hs) and peak period (Tp) combinations. Once trained, tested and deployed, the RNN model can make real-time estimation of the wellhead bending moment based on MRU sensor data and riser tension time series. The RNN model can be an efficient and accurate alternative to a physical model based on the indirect method for real-time calculation of wellhead bending moment using real-time sensor data. A case study is presented to explain the training procedures for the RNN model. A set of test cases that are not included in the training dataset are used to demonstrate the accuracy of the RNN model using Root Mean Squared Error (RMSE), Normalized Root Mean Squared Error (NRMSE) and coefficient of determination (R2) as a metrics.

scholarly journals Comparison of sampling methods for estimation of nearest-neighbor index values

2017 ◽  
Vol 47 (6) ◽  
pp. 703-715 ◽  
Author(s):  
Francisco Mauro ◽  
Zane Haxtema ◽  
Hailemariam Temesgen
Keyword(s):  
Mean Squared Error ◽  
Relative Bias ◽  
Random Selection ◽  
Horizontal Distance ◽  
Root Mean Squared Error ◽  
Reference Tree ◽  
Squared Error ◽  
Relative Root ◽  
Diameter Differentiation ◽  
Selection Of

Neighborhood-based indices such as mingling index and diameter differentiation are a set of diversity measures that are based on the relationship between a reference tree and a certain number of nearest neighbors (i.e., trees to which it has the lowest horizontal distance). Using stem-mapped data from eight headwater sites, we compared the relative bias and relative root mean square error (relative to the true mean of each site) of several different methods of choosing reference trees for calculation of diameter differentiation ([Formula: see text]) and species mingling ([Formula: see text]) index. Indices were defined using two, three, and four neighbors and methods for selection of the reference tree were random selection of a tree in a fixed-radius plot (FI), random selection of a tree in a variable-radius plot (VA), azimuth selection method (AZ), and nearest tree selection (NT). In general, the relative bias was lower than ±2.5% for [Formula: see text] and lower than ±10% for [Formula: see text] regardless of the method. The FI method consistently had the lowest relative bias and relative root mean squared error. The NT and AZ methods were second in terms of relative root mean squared error for [Formula: see text] and [Formula: see text], respectively. Simplicity of these two methods might outweigh their slightly worse performance.

scholarly journals Comparison of statistical indices for the evaluation of crop models performance

2021 ◽  
Vol 74 (3) ◽  
pp. 9675-9684
Author(s):  
Tatiana María Saldaña Villota ◽  
José Miguel Cotes Torres
Keyword(s):  
Mean Squared Error ◽  
Potato Crop ◽  
Coefficient Of Determination ◽  
Model Assessment ◽  
Data Set ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Statistical Indices ◽  
Index Of Agreement ◽  
Relative Root

This study presents a comparison of the usual statistical methods used for crop model assessment. A case study was conducted using a data set from observations of the total dry weight in diploid potato crop, and six simulated data sets derived from the observationsaimed to predict the measured data. Statistical indices such as the coefficient of determination, the root mean squared error, the relative root mean squared error, mean error, index of agreement, modified index of agreement, revised index of agreement, modeling efficiency, and revised modeling efficiency were compared. The results showed that the coefficient of determination is not a useful statistical index for model evaluation. The root mean squared error together with the relative root mean squared error offer an excellent notion of how deviated the simulations are in the same unit of the variable and percentage terms, and they leave no doubt when evaluating the quality of the simulations of a model.

Use of reflectance spectroscopy to estimate the organic carbon and CaCO3 contents of soils

2012 ◽  
Vol 61 (2) ◽  
pp. 277-290 ◽  
Author(s):  
Ádám Csorba ◽  
Vince Láng ◽  
László Fenyvesi ◽  
Erika Michéli
Keyword(s):  
Organic Carbon ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Mean Squared Error ◽  
Reflectance Spectroscopy ◽  
Least Squares Regression ◽  
Root Mean Squared Error ◽  
Squared Error

Napjainkban egyre nagyobb igény mutatkozik olyan technológiák és módszerek kidolgozására és alkalmazására, melyek lehetővé teszik a gyors, költséghatékony és környezetbarát talajadat-felvételezést és kiértékelést. Ezeknek az igényeknek felel meg a reflektancia spektroszkópia, mely az elektromágneses spektrum látható (VIS) és közeli infravörös (NIR) tartományában (350–2500 nm) végzett reflektancia-mérésekre épül. Figyelembe véve, hogy a talajokról felvett reflektancia spektrum információban nagyon gazdag, és a vizsgált tartományban számos talajalkotó rendelkezik karakterisztikus spektrális „ujjlenyomattal”, egyetlen görbéből lehetővé válik nagyszámú, kulcsfontosságú talajparaméter egyidejű meghatározása. Dolgozatunkban, a reflektancia spektroszkópia alapjaira helyezett, a talajok ösz-szetételének meghatározását célzó módszertani fejlesztés első lépéseit mutatjuk be. Munkánk során talajok szervesszén- és CaCO3-tartalmának megbecslését lehetővé tévő többváltozós matematikai-statisztikai módszerekre (részleges legkisebb négyzetek módszere, partial least squares regression – PLSR) épülő prediktív modellek létrehozását és tesztelését végeztük el. A létrehozott modellek tesztelése során megállapítottuk, hogy az eljárás mindkét talajparaméter esetében magas R2értéket [R2(szerves szén) = 0,815; R2(CaCO3) = 0,907] adott. A becslés pontosságát jelző közepes négyzetes eltérés (root mean squared error – RMSE) érték mindkét paraméter esetében közepesnek mondható [RMSE (szerves szén) = 0,467; RMSE (CaCO3) = 3,508], mely a reflektancia mérési előírások standardizálásával jelentősen javítható. Vizsgálataink alapján arra a következtetésre jutottunk, hogy a reflektancia spektroszkópia és a többváltozós kemometriai eljárások együttes alkalmazásával, gyors és költséghatékony adatfelvételezési és -értékelési módszerhez juthatunk.

scholarly journals Techno-Economic Planning and Operation of the Microgrid Considering Real-Time Pricing Demand Response Program

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4597
Author(s):  
Zi-Xuan Yu ◽  
Meng-Shi Li ◽  
Yi-Peng Xu ◽  
Sheraz Aslam ◽  
Yuan-Kang Li
Keyword(s):  
Real Time ◽  
Demand Response ◽  
Positive Impact ◽  
Environmental Aspects ◽  
Component Size ◽  
Cost Of Energy ◽  
Four Seasons ◽  
Demand Response Program ◽  
Real Time Pricing

The optimal planning of grid-connected microgrids (MGs) has been extensively studied in recent years. While most of the previous studies have used fixed or time-of-use (TOU) prices for the optimal sizing of MGs, this work introduces real-time pricing (RTP) for implementing a demand response (DR) program according to the national grid prices of Iran. In addition to the long-term planning of MG, the day-ahead operation of MG is also analyzed to get a better understanding of the DR program for daily electricity dispatch. For this purpose, four different days corresponding to the four seasons are selected for further analysis. In addition, various impacts of the proposed DR program on the MG planning results, including sizing and best configuration, net present cost (NPC) and cost of energy (COE), and emission generation by the utility grid, are investigated. The optimization results show that the implementation of the DR program has a positive impact on the technical, economic, and environmental aspects of MG. The NPC and COE are reduced by about USD 3700 and USD 0.0025/kWh, respectively. The component size is also reduced, resulting in a reduction in the initial cost. Carbon emissions are also reduced by 185 kg/year.

Comparative study of the pencil-and-paper and digital formats of the Spanish DARS scale

2021 ◽  
pp. 1-21
Author(s):  
Elsa Arrua-Duarte ◽  
Marta Migoya-Borja ◽  
Igor Barahona ◽  
Lena C. Quilty ◽  
Sakina J. Rizvi ◽  
...  
Keyword(s):  
Rating Scale ◽  
Mean Squared Error ◽  
Intraclass Correlation ◽  
Test Validity ◽  
Wilcoxon Test ◽  
Digital Version ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Digital Format ◽  
Paper And Pencil

Abstract Objective: The Dimensional Anhedonia Rating Scale (DARS) is a novel questionnaire to assess anhedonia of recent validation. In this work we aim to study the equivalence between the traditional paper-and-pencil and the digital format of DARS. Methods: 69 patients filled the DARS in a paper-based and digital versions. We assessed differences between formats (Wilcoxon test), validity of the scales (Kappa and Intraclass Correlation Coefficients), and reliability (Cronbach’s alpha and Guttman’s coefficient). We calculated the Comparative Fit Index and the Root Mean Squared Error associated with the proposed one-factor structure. Results: Total scores were higher for paper-based format. Significant differences between both formats were found for three items. The weighted Kappa coefficient was approximately 0.40 for most of the items. Internal consistency was greater than 0.94, and the Intraclass Correlation Coefficient for the digital version was 0.95 and 0.94 for the paper-and-pencil version (F= 16.7, p < 0.001). Comparative Adjustment Index was 0.97 for the digital DARS and 0.97 for the paper-and-pencil DARS, and Root Mean Squared Error was 0.11 for the digital DARS and 0.10 for the paper-and-pencil DARS. Conclusion: The digital DARS is consistent in many respects to the paper-and-pencil questionnaire, but equivalence with this format cannot be assumed without caution.

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