Energy sufficiency of an administrative building based on real data from one year of operation

2018 ◽  
Author(s):  
Nikolaos Skandalos ◽  
Sofiane Kichou ◽  
Petr Wolf
Keyword(s):  
Real Data ◽  
One Year

Pareto Set as a Model for Dispatching Resources in Emergency Centres (Preprint)

2018 ◽  
Author(s):  
Ricardo Guedes ◽  
Vasco Furtado ◽  
Tarcísio Pequeno ◽  
Joel Rodrigues
Keyword(s):  
Operational Research ◽  
Real Data ◽  
Pareto Set ◽  
Multi Objective Optimization ◽  
Pareto Dominance ◽  
Four Dimensions ◽  
Multi Objective ◽  
The Best Approximation ◽  
One Year ◽  
The Cost

UNSTRUCTURED The article investigates policies for helping emergency-centre authorities for dispatching resources aimed at reducing goals such as response time, the number of unattended calls, the attending of priority calls, and the cost of displacement of vehicles. Pareto Set is shown to be the appropriated way to support the representation of policies of dispatch since it naturally fits the challenges of multi-objective optimization. By means of the concept of Pareto dominance a set with objectives may be ordered in a way that guides the dispatch of resources. Instead of manually trying to identify the best dispatching strategy, a multi-objective evolutionary algorithm coupled with an Emergency Call Simulator uncovers automatically the best approximation of the optimal Pareto Set that would be the responsible for indicating the importance of each objective and consequently the order of attendance of the calls. The scenario of validation is a big metropolis in Brazil using one-year of real data from 911 calls. Comparisons with traditional policies proposed in the literature are done as well as other innovative policies inspired from different domains as computer science and operational research. The results show that strategy of ranking the calls from a Pareto Set discovered by the evolutionary method is a good option because it has the second best (lowest) waiting time, serves almost 100% of priority calls, is the second most economical, and is the second in attendance of calls. That is to say, it is a strategy in which the four dimensions are considered without major impairment to any of them.

scholarly journals Clustered Based Prediction for Batteries in the Data Centers

2020 ◽  
Author(s):  
Syed Naeem Haider ◽  
Qianchuan Zhao ◽  
Xueliang Li
Keyword(s):  
Large Scale ◽  
Data Centers ◽  
Arima Model ◽  
Real Life ◽  
Real Data ◽  
Battery Life ◽  
Battery Management System ◽  
Battery Management ◽  
Failure Data ◽  
One Year

This paper proposes an ARIMA approach to battery health forecasting with accuracy improvement by K shape-based clustered predictors. The health prediction of the battery pack is an important function of a battery management system in data centers. Accurate forecasting of battery life turns out to be very difficult without failure data to train a good forecasting model in real life. The conventional ARIMA model is compared with total and clustered predictors for battery health forecasting. Results show that the forecasting accuracy of the ARIMA model significantly improved by utilizing the results of the clustered predictors for 40 batteries in a real data center. One year of actual historical data of 40 batteries of large scale datacenter is presented to validate the effectiveness of the proposed methodology.

scholarly journals MDA financial distress prediction model for selected Balkan countries

2018 ◽  
pp. 85-93
Author(s):  
Marek Durica ◽  
Peter Adamko ◽  
Katarina Valaskova
Keyword(s):  
Prediction Model ◽  
Financial Distress ◽  
Emerging Market ◽  
Real Data ◽  
Private Companies ◽  
Public And Private ◽  
Financial Distress Prediction ◽  
Balkan Region ◽  
The 1960S ◽  
One Year

The issue of company financial distress and the early prediction of potential bankruptcy is one of the most discussed issues of economists around the world in recent decades. The most widely used method to create these models is Multidimensional Discrimination Analysis from the first attempts in the 1960s to the present. In the paper we present prediction model for some emerging market countries in Balkan region created using a Multidimensional Discriminant Analysis method based on real data from the financial statements obtained from Amadeus - A database of comparable financial information for public and private companies across Europe. Our database contains data more than 200 000 companies and about 25 predictors. Using this model, it is possible to predict the financial difficulties of companies one year in advance.

scholarly journals Comparison of uniformity decisions in DUS testing for full and reduced numbers of measurements

2015 ◽  
Vol 52 (1) ◽  
pp. 47-53
Author(s):  
Wiesław Pilarczyk ◽  
Bogna Kowalczyk ◽  
Ewa Bakinowska
Keyword(s):  
Real Data ◽  
Limited Impact ◽  
Dus Testing ◽  
One Year

SummaryIt is investigated how a reduction in the number of measurements influences uniformity decisions in distinctness, uniformity and stability (DUS) testing. Using real data from DUS trials performed in Poland for several species, it is shown that when final decisions are taken after three years of testing, a reduction in the number of measurements by 50% of the numbers indicated in the Guidelines has very limited impact on decisions (rejection or approval of candidate variety). Decisions taken after one year (or two years) are more dependent on the numbers of measurements.

scholarly journals Methods to Apply a 3-Parameter Logistic Model to Wind Turbine Data

2020 ◽  
Vol 10 (9) ◽  
pp. 3317 ◽  
Author(s):  
Daniel Villanueva ◽  
Adrián Sixto ◽  
Andrés Feijóo ◽  
Antonio Fernández ◽  
Edelmiro Miguez
Keyword(s):  
Wind Turbine ◽  
Real Life ◽  
Mathematical Expression ◽  
Wind Farms ◽  
Real Data ◽  
Logistic Function ◽  
Mean Values ◽  
One Year ◽  
The One ◽  
Power Curves

Power curves provided by wind turbine manufacturers are obtained under certain conditions that are different from those of real life operation and, therefore, they actually do not describe the behavior of these machines in wind farms. In those cases where one year of data is available, a logistic function may be fitted and used as an accurate model for such curves, with the advantage that it describes the power curve by means of a very simple mathematical expression. Building such a curve from data can be achieved by different methods, such as using mean values or, alternatively, all the possible values for given intervals. However, when using the mean values, some information is missing and when using all the values the model obtained can be wrong. In this paper, some methods are proposed and applied to real data for comparison purposes. Among them, the one that combines data clustering and simulation is recommended in order to avoid some errors made by the other methods. Besides, a data filtering recommendation and two different assessment procedures for the error provided by the model are proposed.

scholarly journals Comparative and Cost Analysis of a Novel Predictive Power Ramp Rate Control Method: A Case Study in a PV Power Plant in Puerto Rico

2021 ◽  
Vol 11 (13) ◽  
pp. 5766
Author(s):  
Juan F. Patarroyo-Montenegro ◽  
Jesus D. Vasquez-Plaza ◽  
Omar F. Rodriguez-Martinez ◽  
Yuly V. Garcia ◽  
Fabio Andrade
Keyword(s):  
Puerto Rico ◽  
Power Plant ◽  
Control Strategy ◽  
Rate Control ◽  
Control Method ◽  
Real Data ◽  
Control Methods ◽  
Ramp Rate ◽  
Proposed Model ◽  
One Year

One of the most important aspects that need to be addressed to increase solar energy penetration is the power ramp-rate control. In weak grids such as the one found in Puerto Rico, it is important to smooth power fluctuations caused by the intermittence of passing clouds. In this work, a novel power ramp-rate control strategy is proposed. Additionally, a comparison with some of the most common power ramp-rate control methods is performed using a proposed model and real solar radiation data from the Coto Laurel photovoltaic power plant located in Ponce, Puerto Rico. The proposed model was validated using one-year real data from Coto Laurel. The power ramp-rate control methods were compared in real-time simulations using the OP5700 from Opal-RT Technologies considering power ramp rate fluctuations, power ramp-rate violations, fluctuations in the state-of-charge, among other indicators. Moreover, the proposed power ramp-rate control strategy, called predictive dynamic smoothing was explained and compared. Results indicate that the predictive dynamic smoothing produced a considerably reduced Levelized Cost of Storage compared to other power ramp-rate control methods and provided a higher lifetime expectancy for lithium batteries.

scholarly journals Prediction of the COVID-19 Spread in China Based on Long Short-Term Memory Network

2021 ◽  
Vol 2138 (1) ◽  
pp. 012015
Author(s):  
Haifei Zhang ◽  
Jian Xu ◽  
Lanmei Qian ◽  
Jianlin Qiu
Keyword(s):  
Short Term Memory ◽  
Real Data ◽  
Development Trend ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Lstm Network ◽  
One Year ◽  
The Masses ◽  
The Development Trend

Abstract The sudden outbreak of COVID-19 has caused great losses to the economy and the life of the masses. Long short-term memory (LSTM) network is a time recursive neural network, which is suitable for processing and predicting important events with relatively long interval and delay in time series. Using LSTM network to predict and analyze the development trend of epidemic situation, it is imperative to prevent epidemic situation from causing secondary harm to China’s development. In this paper, we first obtained the COVID-19 data published by China Health Net using crawler technology, which is the accurate value of infection trend after the outbreak of COVID-19 in China. Then, based on these data, the LSTM model is used to predict the development trend of the epidemic in one year, and the mean square error is used to calculate the error between the prediction and the real data. The experimental model is used to predict and analyze the development trend of COVID-19. The results show that the error between predicted data and real data is small and the effect is very good, which provides a reasonable basis and forecast for scientific prevention and control of epidemic situation.

scholarly journals COMPOSITE FINANCIAL PERFORMANCE INDEX PREDICTION – A NEURAL NETWORKS APPROACH

2021 ◽  
Vol 22 (2) ◽  
pp. 277-296
Author(s):  
Diana Claudia Sabău Popa ◽  
Dorina Nicoleta Popa ◽  
Victoria Bogdan ◽  
Ramona Simut
Keyword(s):  
Neural Networks ◽  
Financial Performance ◽  
Performance Index ◽  
Business Performance ◽  
Composite Index ◽  
Real Data ◽  
Measurement Model ◽  
Financial Index ◽  
Composite Financial Index ◽  
One Year

Financial indicators are the most used variables in measuring the business performance of companies, signaling about the financial position, comprehensive income, and other significant reporting aspects. In a competitive environment, the performance measurement model allows performing comparative analysis in the same industry and between industries. This paper aims to design a composite financial index to determine the financial performance of listed companies, further used in predicting business performance through neural networks. Principal components analysis was used to build a composite financial index, employing four traditional accounting indicators and four value-based indicators for the period 2011–2018. Five experiments were conducted to predict business performance through the composite financial index. The results showed that observations from two years, of the first three experiments, indicate a better predictive behavior than the same experiments using observations from one year. Therefore, we concluded that observations from more than one year are necessary to predict the value of the financial performance index. Findings led us to the conclusion that recurrent neural networks model predicted better financial performance composite index when taken into consideration more real data for the financial performance index (2012–2018) instead of just for one year (2018).

Sign in / Sign up

Export Citation Format

Share Document