Bi-Objective Superior Combination Method of the Post-Construction Settlement Forecasting of Subgrade

2011 ◽  
Vol 243-249 ◽  
pp. 4283-4287
Author(s):  
Tian Wen Lai ◽  
Qi Yun Zhou
Keyword(s):  
Measured Data ◽  
Combination Method ◽  
Forecasting Model ◽  
Model Parameters ◽  
Forecasting Accuracy ◽  
Forecasting Methods ◽  
Combination Forecasting ◽  
Forecasting Method ◽  
Curve Method ◽  
Index Curve

The more used forecasting method of post-construction settlement of subgrade is to analyze the measured data to determine the forecasting model parameters，and then use this model to forecast the post-construction settlement in actual projects. Currently used forecasting methods are Hoshino method, index curve method, hyperbolic method and so on, these methods have their advantages, disadvantages and applicability. To make the best use of the advantages and avoid the disadvantages, accordingly the combination forecasting method is proposed that can both comprehensive utilization the information provided in a variety of forecasting methods and also improvement of the forecasting accuracy. Then a superior combination forecasting model is established by the highest forecasting precision and the best forecasting stability. Taking the measured data of post-construction settlement of subgrade for example, comparison of fitting precision and forecasting precision of various forecasting methods , and show that the superior combination method has advantages of higher fitting precision, forecasting precision and their stability.

Combination Forecasting Method Based on IOWA Operator and Application

2014 ◽  
Vol 945-949 ◽  
pp. 2515-2518
Author(s):  
Di Liang ◽  
Long Fei Ma ◽  
Ya Feng Hu ◽  
Shuang Wu
Keyword(s):  
Mean Value ◽  
Weighted Averaging ◽  
Forecasting Model ◽  
Predictive Values ◽  
Forecasting Accuracy ◽  
Optimal Weights ◽  
Combination Forecasting ◽  
Forecasting Method ◽  
Iowa Operator ◽  
Better Than

The combination forecasting model based on induced ordered weighted averaging IOWA operators. First, individual forecasting model that has higher forecasting accuracy is chosen as a criterion. Then, the deviation of predictive values between other models and standard model is computed. The weights are given according to the mean value size of the absolute value sum of deviation in every individual forecasting model in every period. Finally, a new forecasting model is built in accordance with the weighted error sum of squares. And genetic algorithm is used to solve the optimal weights. Verified by an example, the improved combination forecasting method is better than the original combination forecasting method based on IOWA operator. Forecasting accuracy is improved effectively.

scholarly journals Load forecasting of refrigerated display cabinet based on CEEMD–IPSO–LSTM combined model

Open Physics ◽  
2021 ◽  
Vol 19 (1) ◽  
pp. 360-374
Author(s):  
Yuan Pei ◽  
Lei Zhenglin ◽  
Zeng Qinghui ◽  
Wu Yixiao ◽  
Lu Yanli ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Data ◽  
Load Forecasting ◽  
Series Data ◽  
Forecasting Model ◽  
Combined Model ◽  
Forecasting Accuracy ◽  
Forecasting Method ◽  
Consumption Reduction

Abstract The load of the showcase is a nonlinear and unstable time series data, and the traditional forecasting method is not applicable. Deep learning algorithms are introduced to predict the load of the showcase. Based on the CEEMD–IPSO–LSTM combination algorithm, this paper builds a refrigerated display cabinet load forecasting model. Compared with the forecast results of other models, it finally proves that the CEEMD–IPSO–LSTM model has the highest load forecasting accuracy, and the model’s determination coefficient is 0.9105, which is obviously excellent. Compared with other models, the model constructed in this paper can predict the load of showcases, which can provide a reference for energy saving and consumption reduction of display cabinet.

A Multi-Dimensional Method for Nodal Load Forecasting

2014 ◽  
Vol 1070-1072 ◽  
pp. 708-717
Author(s):  
Zhi Yuan Pan ◽  
Chao Nan Liu ◽  
Jing Wang ◽  
Yong Wang
Keyword(s):  
Energy Flow ◽  
Load Forecasting ◽  
Least Square ◽  
Support Vector ◽  
Forecasting Accuracy ◽  
Combination Forecasting ◽  
Forecasting Method ◽  
Load Pattern ◽  
And Control

The intelligent dispatch and control of future smart grid demands grasping of any nodal load pattern in the general great grid, therefore to realize the load forecasting of any nodal load is quite important. To solve this problem, focusing on overcoming the weakness of isolated nodal load forecasting and based on the correlation analysis, this paper proposes a multi-dimensional nodal load forecast system and corresponding method for effective prediction of any nodal load of the grid. This system includes topology partitioning of the grid energy flow according to layers and regions, basic forecasting unit composed of each layer’s total amount of load and its nodal loads, and combination forecasting for any node. The forecasting method is based on techniques including the multi-output least square support vector machine, Kalman filtering and the approximate optimal prediction. A case study shows that the multi-dimensional nodal load forecasting model helps to improve the forecasting accuracy, and has practical prospects.

Properties of Weighted Geometric Means Combination Forecasting Model Based on Absolute of Grey Incidence

2012 ◽  
Vol 490-495 ◽  
pp. 442-446 ◽  
Author(s):  
Li Hong Sun
Keyword(s):  
Forecasting Model ◽  
Sufficient Condition ◽  
Redundant Information ◽  
Geometric Means ◽  
Model Based ◽  
Combination Forecasting ◽  
Forecasting Method ◽  
Combination Forecasting Model

weighted geometric means combination forecasting is a kind of nonlinear combination forecasting model. Based on absolute of grey incidence, a weighted geometric means combination forecasting model is proposed. Superior combination forecasting, dominant forecasting method and redundant degree are put forward. Under certain conditions the sufficient condition of existence of non-inferior combination and superior combination forecasting are discussed, redundant information is pointed out in a judging theorem.

scholarly journals Selecting Single Model in Combination Forecasting Based on Cointegration Test and Encompassing Test

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Chuanjin Jiang ◽  
Jing Zhang ◽  
Fugen Song
Keyword(s):  
Model Selection ◽  
Empirical Analysis ◽  
Cointegration Test ◽  
Single Model ◽  
Forecasting Accuracy ◽  
Combination Forecasting ◽  
Forecasting Method ◽  
Encompassing Test ◽  
Accuracy And Stability ◽  
Selection Of

Combination forecasting takes all characters of each single forecasting method into consideration, and combines them to form a composite, which increases forecasting accuracy. The existing researches on combination forecasting select single model randomly, neglecting the internal characters of the forecasting object. After discussing the function of cointegration test and encompassing test in the selection of single model, supplemented by empirical analysis, the paper gives the single model selection guidance: no more than five suitable single models can be selected from many alternative single models for a certain forecasting target, which increases accuracy and stability.

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 APLIKASI METODE ARIMA BOX-JENKINS UNTUK MERAMALKAN KASUS DBD DI PROVINSI JAWA TIMUR

2019 ◽  
Vol 13 (2) ◽  
pp. 183
Author(s):  
Muhammad Bintang Pamungkas
Keyword(s):  
Time Series ◽  
Significance Test ◽  
Time Series Forecasting ◽  
Forecasting Model ◽  
Upward Trend ◽  
Independent Variables ◽  
Forecasting Methods ◽  
Forecasting Method ◽  
Dependent Variables ◽  
Seasonal Data

The Box-Jenkins forecasting method is one of the time series forecasting methods. This method uses past values as dependent variables and independent variables are ignored. Box-Jenkins (ARIMA) method has advantages that can be used on non-stationary data, can be used on all data patterns including seasonal data patterns so this method can be used to predict cases of DHF in East Java Province. This research was conducted to determine the best model with seasonal ARIMA forecasting model and also to analyze the result of DHF case forecasting in East Java Province. The analysis result shows that the best model for DHF case in East Java Province is ARIMA (1,1,2)(2,1,1)12. The best model has fulfilled the test requirement that is parameter significance test and diagnostics check. Forecasting results show the number of DHF cases in 2017-2018 will experience an upward trend. The total number of DHF cases in 2017 was 14,277 cases and increased to 22,284.54 DHF cases in 2018. The forecasting results showed that the highest peak of DHF cases occurred in January 2017 with 1,914.22 cases and then decrease in the next month until the lowest case occurred in October with 768.46. The forecast for 2018 also shows that the highest DHF cases occurred in January with 3455.55 and declined to the lowest in October with 1126.49 cases. MAPE value in the forecast is 43.51%. The MAPE value indicates that the forecasting is good enough, adequate and feasible to use.

scholarly journals Clustering-based forecasting method for individual consumers electricity load using time series representations

2018 ◽  
Vol 8 (1) ◽  
pp. 38-50 ◽  
Author(s):  
Peter Laurinec ◽  
Mária Lucká
Keyword(s):  
Time Series ◽  
Smart Grid ◽  
Series Representation ◽  
Smart Meter ◽  
Grid Data ◽  
Forecasting Accuracy ◽  
Forecasting Methods ◽  
Forecasting Method ◽  
Series Representations ◽  
Electricity Load

Abstract This paper presents a new method for forecasting a load of individual electricity consumers using smart grid data and clustering. The data from all consumers are used for clustering to create more suitable training sets to forecasting methods. Before clustering, time series are efficiently preprocessed by normalisation and the computation of various model-based time series representation methods. Final centroid-based forecasts are scaled by saved normalisation parameters to create the forecast for every consumer. Our method is compared with the approach that creates forecasts for every consumer separately. Evaluation and experiments were conducted on three smart meter datasets from residences of Ireland and Australia, and factories of Slovakia. The achieved results proved that our clustering-based method improves forecasting accuracy mainly for residential consumers.We can also proclaim that it can be found such time series representation and clustering setting that will our forecasting method perform more accurately than fully disaggregated approach. Our method is also more scalable since it is necessary to train the model only on clusters and not for every consumer separately

Wind Power Combination Forecasting Model Based on Drift

2014 ◽  
Vol 953-954 ◽  
pp. 522-528 ◽  
Author(s):  
Quan Cheng Lyu ◽  
Wen Ying Liu ◽  
Dan Dan Zhu ◽  
Wei Zhou Wang ◽  
Xu Shan Han ◽  
...  
Keyword(s):  
Wind Power ◽  
Forecasting Model ◽  
Wind Power Forecasting ◽  
Model Based ◽  
Forecasting Methods ◽  
Forecast Precision ◽  
Combination Forecasting ◽  
Wind Power Forecast ◽  
Combination Forecasting Model ◽  
Power Forecasting

As different wind power forecasting methods provide different information and differ in forecast precision, the combined wind power forecasting model is employed to better forecast wind power. Wind power combination forecasting model based on drift and complementarity of different single wind power forecasting models is proposed in this paper. The combination forecasting model provides a new solution to wind power forecast. Finally a practical example is given to show that wind power combination forecasting model based on drift can improve forecasting precision and is effective in practice.

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