scholarly journals A New Approach to Improve Accuracy of Grey Model GMC(1,n)in Time Series Prediction

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Sompop Moonchai ◽  
Wanwisa Rakpuang
Keyword(s):  
Prediction Accuracy ◽  
Time Series Prediction ◽  
Electricity Consumption ◽  
Prediction Equation ◽  
Grey Model ◽  
System Behavior ◽  
New Approach ◽  
Proposed Model ◽  
Improve Accuracy ◽  
Relative Factors

This paper presents a modified grey model GMC(1,n)for use in systems that involve one dependent system behavior andn-1relative factors. The proposed model was developed from the conventional GMC(1,n)model in order to improve its prediction accuracy by modifying the formula for calculating the background value, the system of parameter estimation, and the model prediction equation. The modified GMC(1,n)model was verified by two cases: the study of forecasting CO2emission in Thailand and forecasting electricity consumption in Thailand. The results demonstrated that the modified GMC(1,n)model was able to achieve higher fitting and prediction accuracy compared with the conventional GMC(1,n)and D-GMC(1,n)models.

scholarly journals A Novel Discrete Grey Model and Its Application

2019 ◽  
Vol 2019 ◽  
pp. 1-6 ◽  
Author(s):  
Wen-Ze Wu ◽  
Jianming Jiang ◽  
Qi Li
Keyword(s):  
Tensile Strength ◽  
Arbitrary Number ◽  
Prediction Accuracy ◽  
Historical Data ◽  
Prediction Performance ◽  
Grey Model ◽  
Original Series ◽  
Proposed Model ◽  
Grey Models ◽  
Future Data

This paper aims to further increase the prediction accuracy of the grey model based on the existing discrete grey model, DGM(1,1). Herein, we begin by studying the connection between forecasts and the first entry of the original series. The results comprehensively show that the forecasts are independent of the first entry in the original series. On this basis, an effective method of inserting an arbitrary number in front of the first item of the original series to extract messages is applied to produce a novel grey model, which is abbreviated as FDGM(1,1) for simplicity. Incidentally, the proposed model can even forecast future data using only three historical data. To demonstrate the effectiveness of the proposed model, two classical examples of the tensile strength and life of the product are employed in this paper. The numerical results indicate that FDGM(1,1) has a better prediction performance than most commonly used grey models.

scholarly journals An Improved Nonequidistant Grey Model Based on Simpson Formula and Its Application

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhiming Hu ◽  
Chong Liu
Keyword(s):  
Prediction Accuracy ◽  
Prediction Models ◽  
Prediction Performance ◽  
Grey System Theory ◽  
Grey Model ◽  
Grey Prediction ◽  
Grey System ◽  
Proposed Model ◽  
Grey Models ◽  
High Prediction

Grey prediction models have been widely used in various fields of society due to their high prediction accuracy; accordingly, there exists a vast majority of grey models for equidistant sequences; however, limited research is focusing on nonequidistant sequence. The development of nonequidistant grey prediction models is very slow due to their complex modeling mechanism. In order to further expand the grey system theory, a new nonequidistant grey prediction model is established in this paper. To further improve the prediction accuracy of the NEGM (1, 1, t2) model, the background values of the improved nonequidistant grey model are optimized based on Simpson formula, which is abbreviated as INEGM (1, 1, t2). Meanwhile, to verify the validity of the proposed model, this model is applied in two real-world cases in comparison with three other benchmark models, and the modeling results are evaluated through several commonly used indicators. The results of two cases show that the INEGM (1, 1, t2) model has the best prediction performance among these competitive models.

Fuzzy grey forecasting model optimized by moth-flame optimization algorithm for short time electricity consumption

2021 ◽  
pp. 1-10
Author(s):  
Ceyda Tanyolaç Bilgiç ◽  
Boğaç Bilgiç ◽  
Ferhan Çebi
Keyword(s):  
Optimization Algorithm ◽  
Electricity Consumption ◽  
Grey Model ◽  
Forecasting Model ◽  
Model Parameters ◽  
Forecasting Accuracy ◽  
Forecasting Models ◽  
True Value ◽  
Proposed Model ◽  
Moth Flame Optimization Algorithm

It is significant that the forecasting models give the closest result to the true value. Forecasting models are widespread in the literature. The grey model gives successful results with limited data. The existing Triangular Fuzzy Grey Model (TFGM (1,1)) in the literature is very useful in that it gives the maximum, minimum and average value directly in the data. A novel combined forecasting model named, Moth Flame Optimization Algorithm optimization of Triangular Fuzzy Grey Model, MFO-TFGM (1,1), is presented in this study. The existing TFGM (1,1) model parameters are optimized by a new nature- inspired heuristic algorithm named Moth-Flame Optimization algorithm which is inspired by the moths flying path. Unlike the studies in the literature, in order to improve the forecasting accuracy, six parameters (λL, λM, λR, α, β and γ) were optimized. After the steps of the model is presented, a forecasting implementation has been made with the proposed model. Turkey’s hourly electricity consumption data is utilized to show the success of the prediction model. Prediction results of proposed model is compared with TFGM (1,1). MFO-TFGM (1,1) performs higher forecasting accuracy.

scholarly journals Forecasting Natural Gas Consumption of China Using a Novel Grey Model

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Chengli Zheng ◽  
Wen-Ze Wu ◽  
Jianming Jiang ◽  
Qi Li
Keyword(s):  
Natural Gas ◽  
Prediction Accuracy ◽  
Prediction Performance ◽  
Grey Model ◽  
Energy Market ◽  
Original Series ◽  
Proposed Model ◽  
Grey Models ◽  
Gas Consumption ◽  
Natural Gas Consumption

As is known, natural gas consumption has been acted as an extremely important role in energy market of China, and this paper is to present a novel grey model which is based on the optimized nonhomogeneous grey model (ONGM (1,1)) in order to accurately predict natural gas consumption. This study begins with proving that prediction results are independent of the first entry of original series using the product theory of determinant; on this basis, it is a reliable approach by inserting an arbitrary number in front of the first entry of original series to extract messages, which has been proved that it is an appreciable approach to increase prediction accuracy of the traditional grey model in the earlier literature. An empirical example often appeared in testing for prediction accuracy of the grey model is utilized to demonstrate the effectiveness of the proposed model; the numerical results indicate that the proposed model has a better prediction performance than other commonly used grey models. Finally, the proposed model is applied to predict China’s natural gas consumption from 2019 to 2023 in order to provide some valuable information for energy sectors and related enterprises.

Adaptive prediction model for effective electrical machine maintenance

2019 ◽  
Vol 26 (1) ◽  
pp. 166-180
Author(s):  
Ganga D. ◽  
Ramachandran V.
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Adaptive Filter ◽  
Prediction Accuracy ◽  
Time Series Prediction ◽  
System Response ◽  
Electrical Machine ◽  
Subsequent Stage ◽  
Content Type ◽  
Proposed Model

Purpose The purpose of this paper is to propose an optimal predictive model for the short-term forecast of real-time non-stationary machine variables by combining time series prediction with adaptive algorithms to minimize the error and to improve the prediction accuracy. Design/methodology/approach The proposed model is applied for prediction of speed and controller set point of three-phase induction motor operating on closed loop speed control with AC drive and PI controller. At Stage 1, the trend of the machine variables has been extracted and added to auto-regressive moving average (ARMA) time series prediction. ARMA prediction has been carried out using different combinations of AR and MA methods in order to make prediction with less Mean Squared Error (MSE). Findings The prediction error indicates the inadequacy of the model to estimate the data characteristics, which has been resolved at the subsequent stage by cascading an adaptive least mean square finite impulse response filter to the time series model. The adaptive filter receives the predicted output including training data and iteratively adjusts its coefficients for zero error convergence. Research limitations/implications The componentized data prediction based on time series and cascade adaptive filter algorithm decomposes the non-stationary data characteristics for predictive maintenance. Evaluation of the model with different combination of time series algorithms and parameter settings of adaptive filter has been carried out to illustrate the performance of the prediction model. This prediction accuracy is compared with existing linear adaptive filter prediction using MSE as comparison index. The wide margin in the MSE values substantiates the prediction efficiency of the proposed model for machine data. Originality/value This model predicts the dynamic machine data with component decomposition at high accuracy, which enables to interpret the system response under dynamic conditions efficiently.

scholarly journals A novel ensemble learning-based grey model for electricity supply forecasting in China

2021 ◽  
Vol 6 (11) ◽  
pp. 12339-12358
Author(s):  
Yubin Cai ◽  
◽  
Xin Ma ◽  
Keyword(s):  
Ensemble Learning ◽  
Real World ◽  
Electricity Consumption ◽  
Majority Voting ◽  
Grey Model ◽  
Electricity Supply ◽  
Data Sets ◽  
Grey System ◽  
Proposed Model ◽  
Electricity Power

<abstract><p>Electricity consumption is one of the most important indicators reflecting the industrialization of a country. Supply of electricity power plays an import role in guaranteeing the running of a country. However, with complex circumstances, it is often difficult to make accurate forecasting with limited reliable data sets. In order to take most advantages of the existing grey system model, the ensemble learning is adopted to provide a new stratagy of building forecasting models for electricity supply of China. The nonhomogeneous grey model with different types of accumulation is firstly fitted with multiple setting of acculumation degrees. Then the majority voting is used to select and combine the most accurate and stable models validated by the grid search cross validation. Two numerical validation cases are taken to validate the proposed method in comparison with other well-known models. Results of the real-world case study of forecasting the electricity supply of China indicate that the proposed model outperforms the other 15 exisiting grey models, which illustrates the proposed model can make much more accurate and stable forecasting in such real-world applications.</p></abstract>

Intelligent recognition of semantic relationships based on antonymy

2020 ◽  
Vol 16 (3) ◽  
pp. 263-290
Author(s):  
Hui Guan ◽  
Chengzhen Jia ◽  
Hongji Yang
Keyword(s):  
Semantic Similarity ◽  
New Approach ◽  
Word Similarity ◽  
Semantic Relationships ◽  
Proposed Model ◽  
Path Distance ◽  
The Hierarchical Structure ◽  
Thinking Process ◽  
Similarity Measuring ◽  
Intelligent Recognition

Since computing semantic similarity tends to simulate the thinking process of humans, semantic dissimilarity must play a part in this process. In this paper, we present a new approach for semantic similarity measuring by taking consideration of dissimilarity into the process of computation. Specifically, the proposed measures explore the potential antonymy in the hierarchical structure of WordNet to represent the dissimilarity between concepts and then combine the dissimilarity with the results of existing methods to achieve semantic similarity results. The relation between parameters and the correlation value is discussed in detail. The proposed model is then applied to different text granularity levels to validate the correctness on similarity measurement. Experimental results show that the proposed approach not only achieves high correlation value against human ratings but also has effective improvement to existing path-distance based methods on the word similarity level, in the meanwhile effectively correct existing sentence similarity method in some cases in Microsoft Research Paraphrase Corpus and SemEval-2014 date set.

scholarly journals A Study of Developing a Prediction Equation of Electricity Energy Output via Photovoltaic Modules

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1503
Author(s):  
Minsu Kim ◽  
Hongmyeong Kim ◽  
Jae Hak Jung
Keyword(s):  
Experimental Data ◽  
Real Time ◽  
Air Temperature ◽  
Temperature Difference ◽  
Prediction Accuracy ◽  
Prediction Equation ◽  
Energy Output ◽  
Photovoltaic Modules ◽  
Mean Error ◽  
Pv Module

Various equations are being developed and applied to predict photovoltaic (PV) module generation. Currently, quite diverse methods for predicting module generation are available, with most equations showing accuracy with ≤5% error. However, the accuracy can be determined only when the module temperature and the value of irradiation that reaches the module surface are precisely known. The prediction accuracy of outdoor generation is actually extremely low, as the method for predicting outdoor module temperature has extremely low accuracy. The change in module temperature cannot be predicted accurately because of the real-time change of irradiation and air temperature outdoors. Calculations using conventional equations from other studies show a mean error of temperature difference of 4.23 °C. In this study, an equation was developed and verified that can predict the precise module temperature up to 1.64 °C, based on the experimental data obtained after installing an actual outdoor module.

scholarly journals Predicting Daily Dry Matter Intake Using Feed Intake of First Two Hours after Feeding in Mid and Late Lactation Dairy Cows with Fed Ration Three Times Per Day

Animals ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 104
Author(s):  
Shulin Liang ◽  
Chaoqun Wu ◽  
Wenchao Peng ◽  
Jian-Xin Liu ◽  
Hui-Zeng Sun
Keyword(s):  
Dairy Cows ◽  
Milk Yield ◽  
Dry Matter ◽  
Prediction Equation ◽  
Dry Matter Intake ◽  
Validation Dataset ◽  
Nutrient Requirements ◽  
Independent Dataset ◽  
Lactating Dairy Cows ◽  
Proposed Model

The objective of this study was to evaluate the feasibility of using the dry matter intake of first 2 h after feeding (DMI-2h), body weight (BW), and milk yield to estimate daily DMI in mid and late lactating dairy cows with fed ration three times per day. Our dataset included 2840 individual observations from 76 cows enrolled in two studies, of which 2259 observations served as development dataset (DDS) from 54 cows and 581 observations acted as the validation dataset (VDS) from 22 cows. The descriptive statistics of these variables were 26.0 ± 2.77 kg/day (mean ± standard deviation) of DMI, 14.9 ± 3.68 kg/day of DMI-2h, 35.0 ± 5.48 kg/day of milk yield, and 636 ± 82.6 kg/day of BW in DDS and 23.2 ± 4.72 kg/day of DMI, 12.6 ± 4.08 kg/day of DMI-2h, 30.4 ± 5.85 kg/day of milk yield, and 597 ± 63.7 kg/day of BW in VDS, respectively. A multiple regression analysis was conducted using the REG procedure of SAS to develop the forecasting models for DMI. The proposed prediction equation was: DMI (kg/day) = 8.499 + 0.2725 × DMI-2h (kg/day) + 0.2132 × Milk yield (kg/day) + 0.0095 × BW (kg/day) (R2 = 0.46, mean bias = 0 kg/day, RMSPE = 1.26 kg/day). Moreover, when compared with the prediction equation for DMI in Nutrient Requirements of Dairy Cattle (2001) using the independent dataset (VDS), our proposed model shows higher R2 (0.22 vs. 0.07) and smaller mean bias (−0.10 vs. 1.52 kg/day) and RMSPE (1.77 vs. 2.34 kg/day). Overall, we constructed a feasible forecasting model with better precision and accuracy in predicting daily DMI of dairy cows in mid and late lactation when fed ration three times per day.

scholarly journals A New Approach to Inform Restoration and Management Decisions for Sustainable Apiculture

2021 ◽  
Vol 13 (11) ◽  
pp. 6109
Author(s):  
Joanne Lee Picknoll ◽  
Pieter Poot ◽  
Michael Renton
Keyword(s):  
Floral Resources ◽  
Pollination Services ◽  
New Approach ◽  
Honey Production ◽  
Proposed Model ◽  
Colony Loss ◽  
Supply Potential ◽  
Australian Case ◽  
Stocking Rates

Habitat loss has reduced the available resources for apiarists and is a key driver of poor colony health, colony loss, and reduced honey yields. The biggest challenge for apiarists in the future will be meeting increasing demands for pollination services, honey, and other bee products with limited resources. Targeted landscape restoration focusing on high-value or high-yielding forage could ensure adequate floral resources are available to sustain the growing industry. Tools are currently needed to evaluate the likely productivity of potential sites for restoration and inform decisions about plant selections and arrangements and hive stocking rates, movements, and placements. We propose a new approach for designing sites for apiculture, centred on a model of honey production that predicts how changes to plant and hive decisions affect the resource supply, potential for bees to collect resources, consumption of resources by the colonies, and subsequently, amount of honey that may be produced. The proposed model is discussed with reference to existing models, and data input requirements are discussed with reference to an Australian case study area. We conclude that no existing model exactly meets the requirements of our proposed approach, but components of several existing models could be combined to achieve these needs.

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