scholarly journals A Small-Sample Adaptive Hybrid Model for Annual Electricity Consumption Forecasting

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Ming Meng ◽  
Yanan Fu ◽  
Huifeng Shi ◽  
Xinfang Wang
Keyword(s):  
Hybrid Model ◽  
Electricity Consumption ◽  
Small Sample ◽  
Small Samples ◽  
Proposed Model ◽  
Starting Point ◽  
Forecasting Performance ◽  
Accelerated Growth ◽  
Artificial Neural Network Ann

Annual electricity consumption forecasting is one of the important foundations of power system planning. Considering that the long-term electricity consumption curves of developing countries usually present approximately exponential growth trends and linear and accelerated growth rate trends may also appear in certain periods, this paper first proposes a small-sample adaptive hybrid model (AHM) to extrapolate the above curves. The iterative trend extrapolation equation of the proposed model can simulate the linear, exponential, and steep trends adaptively at the same time. To estimate the equation parameters using small samples, the partial least squares (PLS) and iteration starting point optimization algorithms are suggested. To evaluate forecasting performance, the artificial neural network (ANN), grey model (GM), and AHM are used to forecast electricity consumption in China from 1991 to 2014, and then the results of these models are compared. Analysis of the forecasting results shows that the AHM can overcome stochastic changes and respond quickly to changes in the main electricity consumption trend because of its specialized equation structure. Overall error analysis indicators also show that AHM often obtains more precise forecasting results than the other two models.

scholarly journals Interval Forecasting in Supply Chain with Small Sample

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Lifeng Wu ◽  
Yan Chen
Keyword(s):  
Supply Chain ◽  
Small Sample ◽  
Decision Makers ◽  
Small Samples ◽  
Forecasting Model ◽  
Future Trends ◽  
Human Judgment ◽  
Proposed Model ◽  
Grey Models ◽  
Short Time

To deal with the forecasting with small samples in the supply chain, three grey models with fractional order accumulation are presented. Human judgment of future trends is incorporated into the order number of accumulation. The output of the proposed model will provide decision-makers in the supply chain with more forecasting information for short time periods. The results of practical real examples demonstrate that the model provides remarkable prediction performances compared with the traditional forecasting model.

scholarly journals Voiceprint Identification for Limited Dataset Using the Deep Migration Hybrid Model Based on Transfer Learning

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2399 ◽  
Author(s):  
Cunwei Sun ◽  
Yuxin Yang ◽  
Chang Wen ◽  
Kai Xie ◽  
Fangqing Wen
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Hybrid Model ◽  
Data Augmentation ◽  
Small Sample ◽  
Restricted Boltzmann Machine ◽  
Small Samples ◽  
Boltzmann Machine ◽  
Training Time

The convolutional neural network (CNN) has made great strides in the area of voiceprint recognition; but it needs a huge number of data samples to train a deep neural network. In practice, it is too difficult to get a large number of training samples, and it cannot achieve a better convergence state due to the limited dataset. In order to solve this question, a new method using a deep migration hybrid model is put forward, which makes it easier to realize voiceprint recognition for small samples. Firstly, it uses Transfer Learning to transfer the trained network from the big sample voiceprint dataset to our limited voiceprint dataset for the further training. Fully-connected layers of a pre-training model are replaced by restricted Boltzmann machine layers. Secondly, the approach of Data Augmentation is adopted to increase the number of voiceprint datasets. Finally, we introduce fast batch normalization algorithms to improve the speed of the network convergence and shorten the training time. Our new voiceprint recognition approach uses the TLCNN-RBM (convolutional neural network mixed restricted Boltzmann machine based on transfer learning) model, which is the deep migration hybrid model that is used to achieve an average accuracy of over 97%, which is higher than that when using either CNN or the TL-CNN network (convolutional neural network based on transfer learning). Thus, an effective method for a small sample of voiceprint recognition has been provided.

scholarly journals A Hybrid Modeling of Long-Term Electricity Consumption Forecasting Based on ARIMA and ANN: The Case of Thailand with Projection

2018 ◽  
Vol 7 (2.28) ◽  
pp. 20
Author(s):  
Nattapon Jaisumroum ◽  
Jirarat Teeravaraprug
Keyword(s):  
Historical Data ◽  
Electricity Consumption ◽  
Absolute Error ◽  
Percentage Error ◽  
Data Sets ◽  
Modeling And Forecasting ◽  
Auto Regressive ◽  
Power Operation ◽  
Artificial Neural Network Ann

Modeling and forecasting of electricity consumption can provide reliable guidance for power operation and planning in developing countries such as Thailand. In this study, formulates the effects of two different historical data type is modeled by auto regressive integrated moving averaged (ARIMA) and artificial neural network (ANN) based on population and gross domestic product per capita (GDP). The derived model is validated by various statistical approaches such as the determination coefficient. Additionally, the performances of the derived model are assessed using mean absolute percentage error (MAPE), root mean square error (RMSE) and mean absolute error (MAE). Three scenarios are used for forecasting Thailand’s electricity consumption in 2011 – 2015.  The simulation results are validated by actual data sets observed from 1993 to 2010. Empirical results showed that the proposed method has higher accuracy compared to single ARIMA and artificial intelligence based models.  

scholarly journals Minimizing the supply temperature at the district heating plant – dynamic optimization

2019 ◽  
Vol 118 ◽  
pp. 02004 ◽  
Author(s):  
Stanislav Chicherin ◽  
Lyazzat Junussova ◽  
Timur Junussov
Keyword(s):  
Promising Result ◽  
Electricity Consumption ◽  
District Heating ◽  
Heat Losses ◽  
Knowing That ◽  
Pump Station ◽  
Starting Point ◽  
Point Set

The constraint contains two elements, namely the heat losses and the electricity consumption for pumping at the producer. The aim was to achieve the lowest acceptable costs in an operation. The options with the supply temperature at the area starting point set to 80/60, then 60/40, and eventually 50/30 (low temperature, 4th generation district heating) were tested. The balance between the savings due to lower heat losses and the electricity consumption of pumps could be performed to assess the economic viability of the solution. This means that if the electricity price is sufficiently high, the model will always choose to minimize electricity consumption and thereby, maximise the profit from high temperature difference. Results concerning heat losses consider both experiences of proper insulation of pipes with variety of design outdoor temperatures (DOTs) and long term measurements from a pump station for district heating (DH) network in Canberra, Australia. We also noted that the heat energy tariffs and purchase price of electricity affect a lot optimal configuration of a DH system. For the best scenario, solutions are obtained that reach over 12% of the available saving potential after calculating 11 equations. Knowing that the policy is updated on a case study base, this is considered a promising result.

scholarly journals Simulation Study on Modified Weibull Distribution for Modelling of Investment Return

2021 ◽  
Vol 29 (4) ◽  
Author(s):  
Hamza Abubakar ◽  
Shamsul Rijal Muhammad Sabri
Keyword(s):  
Weibull Distribution ◽  
Small Sample ◽  
Rate Of Return ◽  
Distribution Model ◽  
Internal Rate Of Return ◽  
Internal Rate ◽  
Investment Return ◽  
Proposed Model ◽  
Two Parameter

The Weibull distribution is one of the most popular statistical models extensively applied to lifetime data analysis such as survival data, reliability data, wind speed, and recently in financial data, due to itsts flexibility to adaptably imitate different families of statistical distributions. This study proposed a modified version of the two-parameter Weibull distribution by incorporating additional parameters in the internal rate of return and insurance claims data. The objective is to examine the behaviour of investment return on the assumption of the proposed model. The proposed and the existing Weibull distribution parameters have been estimated via a simulated annealing algorithm. Experimental simulations have been conducted mimicking the internal rate of return (IRR) data for both short time (small sample) and long-term investment periods (large samples). The performance of the proposed model has been compared with the existing two-parameter Weibull distribution model in terms of their R-square (R2), mean absolute error (MAE), root mean squared error (RMSE), Akaike’s information criterion (AIC), and the Kolmogorov-Smirnov test (KS). The numerical simulation revealed that the proposed model outperformed the existing two-parameter Weibull distribution model in terms of accuracy, robustness, and sensitivity. Therefore, it can be concluded that the proposed model is entirely suitable for the long-term investment period. The study will be extended using the internal rate of return real data set. Furthermore, a comparison of the various Weibull distribution parameter estimators such as metaheuristics or evolutionary algorithms based on the proposed model will be carried out.

scholarly journals Describing proof on hybrid model of ICT and collaborative learning

2018 ◽  
Vol 7 (4) ◽  
pp. 2684
Author(s):  
Nityashree Nadar ◽  
Dr.R.Kamatchi Iyer
Keyword(s):  
Social Media ◽  
Collaborative Learning ◽  
Hybrid Model ◽  
Paper Analysis ◽  
Data Set ◽  
Novel Approach ◽  
Proposed Model ◽  
Starting Point

This paper analysis document to explore tendencies of implementing of ICT in social media and collaborative learning which was explained in the previous paper with an experiment using the study of algebra data set. After analysing the hybrid model, this paper explains the analysis of that novel approach has another remarkable role as a developer and source of innovation. A proposed model that has been suggested to form a starting point for the next ICT education.

How to Correct for Chance Agreement in the Estimation of Sensitivity and Specificity of Diagnostic Tests

1994 ◽  
Vol 33 (02) ◽  
pp. 180-186 ◽  
Author(s):  
H. Brenner ◽  
O. Gefeller
Keyword(s):  
Sensitivity And Specificity ◽  
Disease Status ◽  
Disease Prevalence ◽  
Small Sample ◽  
Small Samples ◽  
Interval Estimate ◽  
Traditional Concept ◽  
Interval Estimates ◽  
Major Disadvantage ◽  
Test Result

Abstract:The traditional concept of describing the validity of a diagnostic test neglects the presence of chance agreement between test result and true (disease) status. Sensitivity and specificity, as the fundamental measures of validity, can thus only be considered in conjunction with each other to provide an appropriate basis for the evaluation of the capacity of the test to discriminate truly diseased from truly undiseased subjects. In this paper, chance-corrected analogues of sensitivity and specificity are presented as supplemental measures of validity, which pay attention to the problem of chance agreement and offer the opportunity to be interpreted separately. While recent proposals of chance-correction techniques, suggested by several authors in this context, lead to measures which are dependent on disease prevalence, our method does not share this major disadvantage. We discuss the extension of the conventional ROC-curve approach to chance-corrected measures of sensitivity and specificity. Furthermore, point and asymptotic interval estimates of the parameters of interest are derived under different sampling frameworks for validation studies. The small sample behavior of the estimates is investigated in a simulation study, leading to a logarithmic modification of the interval estimate in order to hold the nominal confidence level for small samples.

Research for a sustainable European sugar sector

2010 ◽  
pp. 487-495
Author(s):  
Martin Bruhns ◽  
Peter Glaviè ◽  
Arne Sloth Jensen ◽  
Michael Narodoslawsky ◽  
Giorgio Pezzi ◽  
...  
Keyword(s):  
Sugar Industry ◽  
Research Priorities ◽  
Decision Makers ◽  
Sustainable Chemistry ◽  
Technology Platforms ◽  
Research Themes ◽  
Starting Point ◽  
Manufacturing Applications ◽  
European Technology

The paper is based on the results of international project entitled “Towards Sustainable Sugar Industry in Europe (TOSSIE)”. 33 research topics of major importance to the sugar sector are listed and briefly described, and compared with research priorities of the European Technology Platforms: “Food for Life”, “Sustainable Chemistry”, “Biofuels”, and “Plant for the Future”. Most topics are compatible with the research themes included in the COOPERATION part of the 7th Framework Program of the EU (2007-2013). However, some topics may require long-term R&D with the time horizon of up to 15 years. The list of topics is divided into four parts: Sugar manufacturing, Applications of biotechnology and biorefinery processing, Sugarbeet breeding and growing, Horizontal issues. Apart from possible use of the list by policy- and decision makers with an interest in sugarbeet sector, the description of each research topic can be used as a starting point in setting up a research project or other R&D activities.

Modeling of unforced demand response programs

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Hassan Jalili ◽  
Pierluigi Siano
Keyword(s):  
Demand Response ◽  
Load Shifting ◽  
Response Models ◽  
Temperature Changes ◽  
Proposed Model ◽  
Equipment Failures ◽  
Demand Response Programs ◽  
Simultaneous Implementation ◽  
Long Term Studies

Abstract Demand response programs are useful options in reducing electricity price, congestion relief, load shifting, peak clipping, valley filling and resource adequacy from the system operator’s viewpoint. For this purpose, many models of these programs have been developed. However, the availability of these resources has not been properly modeled in demand response models making them not practical for long-term studies such as in the resource adequacy problem where considering the providers’ responding uncertainties is necessary for long-term studies. In this paper, a model considering providers’ unavailability for unforced demand response programs has been developed. Temperature changes, equipment failures, simultaneous implementation of demand side management resources, popular TV programs and family visits are the main reasons that may affect the availability of the demand response providers to fulfill their commitments. The effectiveness of the proposed model has been demonstrated by numerical simulation.

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