Optimization Forecasting Model of Foundation Settlement Based on Grey Model Groups

2006 ◽  
Author(s):  
J. Sun ◽  
Q. Gao
Keyword(s):  
Grey Model ◽  
Forecasting Model ◽  
Foundation Settlement

scholarly journals Foundation Settlement Prediction Based on a Novel NGM Model

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Peng-Yu Chen ◽  
Hong-Ming Yu
Keyword(s):  
Prediction Accuracy ◽  
Forecasting Model ◽  
Foundation Settlement ◽  
Engineering Construction ◽  
Settlement Prediction ◽  
Exponential Law ◽  
Grey Forecasting ◽  
Index Sequence ◽  
Settlement Time ◽  
Time Sequences

Prediction of foundation or subgrade settlement is very important during engineering construction. According to the fact that there are lots of settlement-time sequences with a nonhomogeneous index trend, a novel grey forecasting model called NGM(1,1,k,c)model is proposed in this paper. With an optimized whitenization differential equation, the proposed NGM(1,1,k,c)model has the property of white exponential law coincidence and can predict a pure nonhomogeneous index sequence precisely. We used two case studies to verify the predictive effect of NGM(1,1,k,c)model for settlement prediction. The results show that this model can achieve excellent prediction accuracy; thus, the model is quite suitable for simulation and prediction of approximate nonhomogeneous index sequence and has excellent application value in settlement prediction.

Comparative Analysis of Statistical, Machine Learning, and Grey Methods for Short-Term Electricity Load Forecasting

2016 ◽  
pp. 1161-1183 ◽  
Author(s):  
Tuncay Ozcan ◽  
Tarik Küçükdeniz ◽  
Funda Hatice Sezgin
Keyword(s):  
Support Vector Regression ◽  
Electricity Market ◽  
Load Forecasting ◽  
Forecast Accuracy ◽  
Grey Model ◽  
Support Vector ◽  
Forecasting Model ◽  
Data Sets ◽  
Electricity Load ◽  
Electricity Load Forecasting

Electricity load forecasting is crucial for electricity generation companies, distributors and other electricity market participants. In this study, several forecasting techniques are applied to time series modeling and forecasting of the hourly loads. Seasonal grey model, support vector regression, random forests, seasonal ARIMA and linear regression are benchmarked on seven data sets. A rolling forecasting model is developed and 24 hours of the next day is predicted for the last 14 days of each data set. This day-ahead forecasting model is especially important in day-ahead market activities and plant scheduling operations. Experimental results indicate that support vector regression and seasonal grey model outperforms other approaches in terms of forecast accuracy for day-ahead load forecasting.

scholarly journals Grey Forecasting of Inbound Tourism to Bali and Financial Loses from the COVID-19

2021 ◽  
Vol 1 (1) ◽  
pp. 48-57
Author(s):  
Irsyad Yoga ◽  
I Gede Agus Yudiarta
Keyword(s):  
Service Providers ◽  
Tourism Industry ◽  
Grey Model ◽  
Forecasting Model ◽  
Financial Loss ◽  
Policy Makers ◽  
Industry Sector ◽  
Grey Forecasting ◽  
Inbound Tourism ◽  
Statistical Agency

Management and planning in the Indonesian tourism industry is an important matter. It involves responding to changes and uncertain conditions, especially in the tourism industry sector in Bali, Indonesia. Bali is a tourist spot that relies on foreign tourists. When a situation is not conducive, such as the COVID-19 outbreak that befell unexpectedly, proper management and planning are challenging without accurate forecasts. The current study used the Even Grey Forecasting model EGM (1,1,α,θ) to forecast the number of tourists to Bali, a famous tourist spot in Indonesia, and the approximate financial loss incurred from the pandemic in 2020 is quantified. These objectives are achieved through the data collected from the Bali statistical agency and analyzed through the grey model and some mathematical computations. The results indicated that the pandemic's impact on inbound tourism was severe, and the economy needs some time to recover. The study reported a loss of more than $7.3 billion to Bali due to the COVID-19 outbreak. It is possibly the first study of its kind, and its findings are important for the policy-makers, Tour & Travel service providers, and tourism-related businesses.  

scholarly journals A New GM(1,1) Model Based on Cubic Monotonicity-Preserving Interpolation Spline

Symmetry ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 420 ◽  
Author(s):  
Yuanpeng Zhu ◽  
Zehua Jian ◽  
Yurui Du ◽  
Wenqing Chen ◽  
Jiwei Fang
Keyword(s):  
Model Prediction ◽  
Time Response ◽  
Grey Model ◽  
Forecasting Model ◽  
Interpolation Spline ◽  
Quality Of Data ◽  
Numerical Examples ◽  
Background Value ◽  
Monotonicity Preserving

In the classical GM(1,1) model, an accumulated generating operation is made on the original non-negative sequence to obtain a monotone increasing 1-AGO sequence, and the forecasting model is established based on the 1-AGO sequence. A great number of scholars have improved the accuracy of grey model prediction through better developed background value and the equation for the time response. In this work, we reconstruct the background value based on a new developed monotonicity-preserving piecewise cubic interpolations spline, and thereby establish a new GM(1,1) model. Numerical examples show that the new GM(1,1) model has better prediction quality of data than the original GM(1,1) model and improves the precision of prediction in practice.

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.

The Analysis and Forecast about the Supply and Demand of China's Rubber Raw Materials Based on Grey Model

2013 ◽  
Vol 404 ◽  
pp. 796-801
Author(s):  
Zhao Jun Wang ◽  
Zhou Lin ◽  
Shuai Liu
Keyword(s):  
Natural Rubber ◽  
National Economy ◽  
Raw Materials ◽  
Supply And Demand ◽  
Grey Model ◽  
Forecasting Model ◽  
Rubber Industry ◽  
Grey Forecasting ◽  
Synthetic Rubber ◽  
The Status

The rubber industry is an important sector in the national economy. The article took the natural rubber and synthetic rubber as the main studying objects to analyze and forecast the amount of supply and demand of Chinas rubber raw materials. Analyzed the status of supply and demand of Chinas rubber raw materials from 2006 to 2011, and established the Grey Forecasting Model to forecast the supply and demand from 2012 to 2017 in China, and concluded that the prosperous supply and demand of rubber raw materials would be continued in the future.

scholarly journals Using a Novel Grey System Model to Forecast Natural Gas Consumption in China

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Lifeng Wu ◽  
Sifeng Liu ◽  
Haijun Chen ◽  
Na Zhang
Keyword(s):  
Grey Model ◽  
Forecasting Model ◽  
Limited Data ◽  
Grey System ◽  
Data Set ◽  
Grey Forecasting ◽  
Grey Forecasting Model ◽  
Energy Needs ◽  
New Information ◽  
Gas Consumption

Accurate prediction of the future energy needs is crucial for energy management. This work presents a novel grey forecasting model that integrates the principle of new information priority into accumulated generation. This grey model can better reflect the priority of the new information theoretically. The results of two practical examples demonstrate that this grey model provides very remarkable short-term predication performance compared with traditional grey forecasting model for limited data set forecasting. It is applied to Chinese gas consumption forecasting to show its superiority and applicability.

scholarly journals Forecasting Carbon Dioxide Emissions for Singapore using Grey Model with Cramer’s Rule

2021 ◽  
Vol 17 (4) ◽  
pp. 437-445
Author(s):  
Assif Shamim Mustaffa Sulaiman ◽  
Ani Shabri
Keyword(s):  
Carbon Dioxide ◽  
Global Warming ◽  
Carbon Dioxide Emissions ◽  
Grey Model ◽  
Forecasting Model ◽  
Fossil Carbon ◽  
Forecasting Models ◽  
Grey Forecasting ◽  
Grey Forecasting Model ◽  
Cramer’S Rule

This article analyses and forecasts carbon dioxide () emissions in Singapore for the 2012 to 2016 period. The study analysed the data using grey forecasting model with Cramer’s rule to calculate the best SOGM(2,1) model with the highest accuracy of precision compared to conventional grey forecasting model. According to the forecasted result, the fitted values using SOGM(2,1) model has a higher accuracy precision with better capability in handling information to fit larger scale of uncertain feature compared to other conventional grey forecasting models. This article offers insightful information to policymakers in Singapore to develop better renewable energy instruments to combat the greater issues of global warming and reducing the fossil carbon dioxide emissions into the environment.

Sign in / Sign up

Export Citation Format

Share Document