Research on Short-Term Load Forecasting of Distribution Stations Based on the Clustering Improvement Fuzzy Time Series Algorithm

An improved fuzzy time series algorithmbased on clustering is designed in this paper.The algorithm is successfully applied to short-term load forecasting in the distribution stations.Firstly,the K-means clustering method is used to cluster the data,and the midpoint of two adjacent clustering centers is taken as the dividing point of domain division.On this basis,the data is fuzzed to form a fuzzy time series.Secondly,a high-order fuzzy relation with multiple antecedents is established according to the main measurement indexes of power load,which is used to predict the short-term trend change of load in the distribution stations.Matlab/Simulink simulation results show that the load forecasting errors of the typical fuzzy time series on the time scale of one day and one week are[−50,20]and[−50,30],while the load forecasting errors of the improved fuzzy time series on the time scale of one day and one week are[−20,15]and[−20,25].It shows that the fuzzy time series algorithm improved by clustering improves the prediction accuracy and can effectively predict the short-term load trend of distribution stations.

Integrated parallel forecasting model based on modified fuzzy time series and SVM

A dynamic parallel forecasting model is proposed, which is based on the problem of current forecasting models and their combined model. According to the process of the model, the fuzzy C-means clustering algorithm is improved in outliers operation and distance in the clusters and among the clusters. Firstly, the input data sets are optimized and their coherence is ensured, the region scale algorithm is modified and non-isometric multi scale region fuzzy time series model is built. At the same time, the particle swarm optimization algorithm about the particle speed, location and inertia weight value is improved, this method is used to optimize the parameters of support vector machine, construct the combined forecast model, build the dynamic parallel forecast model, and calculate the dynamic weight values and regard the product of the weight value and forecast value to be the final forecast values. At last, the example shows the improved forecast model is effective and accurate.

Multi-factor high-order intuitionistic fuzzy timeseries forecasting model

Fuzzy sets theory cannot describe the neutrality degreeof data, which has largely limited the objectivity of fuzzy time seriesin uncertain data forecasting. With this regard, a multi-factor highorderintuitionistic fuzzy time series forecasting model is built. Inthe new model, a fuzzy clustering algorithm is used to get unequalintervals, and a more objective technique for ascertaining membershipand non-membership functions of the intuitionistic fuzzy setis proposed. On these bases, forecast rules based on multidimensionalintuitionistic fuzzy modus ponens inference are established.Finally, contrast experiments on the daily mean temperature ofBeijing are carried out, which show that the novel model has aclear advantage of improving the forecast accuracy.

Denoising Nonlinear Time Series Using Singular Spectrum Analysis and Fuzzy Entropy

We present a hybrid singular spectrum analysis （SSA） and fuzzy entropy method to filter noisy nonlinear time series. With this approach, SSA decomposes the noisy time series into its constituent components including both the deterministic behavior and noise, while fuzzy entropy automatically differentiates the optimal dominant components from the noise based on the complexity of each component. We demonstrate the effectiveness of the hybrid approach in reconstructing the Lorenz and Mackey--Class attractors, as well as improving the multi-step prediction quality of these two series in noisy environments.

Modeling the Nigerian Bonny Light Crude Oil Price: The Power of Fuzzy Time Series

Several authors have used different classical statistical models to fit the Nigerian Bonny Light crude oil price but the application of machine learning models and Fuzzy Time Series model on the crude oil price has been grossly understudied. Therefore, in this study, a classical statistical model—Autoregressive Integrated Moving Average (ARIMA), two machine learning models—Artificial Neural Network (ANN) and Random Forest (RF) and Fuzzy Time Series (FTS) Model were compared in modeling the Nigerian Bonny Light crude oil price data for the periods from January, 2006 to December, 2020. The monthly secondary data were collected from the Nigerian National Petroleum Corporation (NNPC) and Reuters website and divided into train (70%) and test (30%) sets. The train set was used in building the models and the models were validated using the test set. The performance measures used for the comparison include: The modified Diebold-Mariano test, the Root Mean Square Error (RMSE), the Mean Absolute Percentage Error (MAPE) and Nash-Sutcliffe Efficiency (NSE) values. Based on the performance measures, ANN (4, 1, 1) and RF performed better than ARIMA (1, 1, 0) model but FTS model using Chen’s algorithm outperformed every other model. The results recommend the use of FTS model for forecasting future values of the Nigerian Bonny Light Crude oil. However, a hybrid model of ARIMA-ANN or ARIMA-RF should be built and compared with Chen’s algorithm FTS model for the same data set to further verify the power of FTS model using Chen’s algorithm.

FUZZY APPROACHING SET AND FUZZYAPPROACHING FUNCTIONAL MAPPING

By establishing the concepts of fuzzy approaching set and fuzzy approaching functional mapping and making research on them, a new method for time series prediction is introduced.

A fuzzy time series forecasting method induced by intuitionistic fuzzy sets

Intuitionistic fuzzy sets(IFSs)are well established as a tool to handle the hesitation in the decision system.In this research paper,fuzzy sets induced by IFS are used to develop a fuzzy time series forecasting model to incorporate degree of hesitation(nondeterminacy).To improve the forecasting accuracy,induced fuzzy sets are used to establish fuzzy logical relations.To verify the performance of the proposed model,it is implemented on one of the benchmarking time series data.Further,developed forecasting method is also tested and validated by applying it on a financial time series data.In order to show the accuracy in forecasting,the method is compared with other forecasting methods using different error measures.

Handling higher order time series forecasting approach in intuitionistic fuzzy environment

This study presents a new method of forecasting based on a higher order intuitionistic fuzzy time series(FTS)by transforming FTS data into intuitionistic FTS data via defining their appropriate membership and non-membership grades.The fuzzification of time series data is intuitionistic fuzzification which is based on the maximum score degree of intuitionistic fuzzy numbers.Also,the intuitionistic fuzzy logical relationship groups are defined and introduced into a defuzzification process for a higher order intuitionistic FTS that enhances in the forecasted output.In order to assess the performance of the proposed method,the method has been implemented on the historical data of rice production.The comparison result shows that the proposed method can achieve a better forecasting accuracy rate in terms of RMSE and MAPE than the existing methods such as Song and Chissom[(1993).Forecasting enrolments with fuzzy time series-Part I.Fuzzy Sets and System,54,1-9],Chen[(1996).Forecasting enrolments based on fuzzy time series.Fuzzy Sets and System,81,311-319],Singh[(2007a).A simple method of forecasting based on fuzzy time series.Applied Mathematics and Computation,186,330-339]and Abhishekh,Gautam,and Singh[(2017).A refined weighted method for forecasting based on type 2 fuzzy time series.International Journal of ModellingandSimulation,38,180-188;(2018).A score function based method of forecasting using intuitionistic fuzzy time series.New Mathematics and Natural Computation,14(1),91-111].

A Fuzzy Time Series Model Based on Improved Fuzzy Function and Cluster Analysis Problem

Based on the improvement in establishing the relations of data,this study proposes a new fuzzy time series model.In this model,the suitable number of fuzzy sets and their specific elements are determined automatically.In addition,using the percentage variations of series between consecutive periods of time,we build the fuzzy function.Incorporating all these improvements,we have a new fuzzy time series model that is better than many existing ones through the well-known data sets.The calculation of the proposed model can be performed conveniently and efficiently by a MATLAB procedure.The proposed model is also used in forecasting for an urgent problem in Vietnam.This application also shows the advantages of the proposed model and illustrates its effectiveness in practical application.

Two-Factor Fuzzy Time Series for Equipment Data Prediction

The data forecasting of plant equipment plays an important role in assurance of the safe and reliable operation of the plant equipment. Thus, it is necessary to improve the accuracy of data forecasting of the equipment. A new two-factor fuzzy time series algorithm is proposed to forecast the data of the plant equipment.This method not only overcomes the limitations of one factor fuzzy time series algorithm, but also overcomes the drawbacks of traditional two-factor fuzzy time series algorithm. The collected data is used in the power plant to conduct experiments, where the metrics is Mean Absolute Percentage Error(MAPE). The results show that this method is superior to the existing two-factor fuzzy time series algorithms, and yields good results in the equipment prediction.

Strongα-cut and associated membership-based modeling for fuzzy time series forecasting

In this paper,a method is proposed to deal with factors affecting the fuzzy time series forecasting.A new fuzzification process is used by considering all the fuzzy sets with nonzero membership values corresponding to the data points.A strong alpha-cut based method is presented to select appropriate fuzzy logical relationships that carry importance in analyzing the trend of time series.Further,a unique defuzzification approach based on weights is proposed to get crisp variation.This obtained variation is finally converted to the forecasted value.The presented method is tested on the benchmark enrolment dataset of Alabama University and seven datasets of the Taiwan Capitalization Weighted Stock Index.On comparing the results,it is observed that the presented method performs better than the existing methods.Also,the statistical measures indicate the good forecasting results of the presented method.

A COMPUTATIONAL METHOD FOR FUZZY TIME SERIES FORECASTING BASED ON DIFFERENCE PARAMETERS

Present study proposes a method for fuzzy time series forecasting based on difference parameters.The developed method has been presented in a form of simple computational algorithm.It utilizes various difference parameters being implemented on current state for forecasting the next state values to accommodate the possible vagueness in the data in an efficient way.The developed model has been simulated on the historical student enrollments data of University of Alabama and the obtained forecasted values have been compared with the existing methods to show its superiority.Further,the developed model has also been implemented in forecasting the movement of market prices of share of State Bank of India(SBI)at Bombay Stock Exchange(BSE),India.

Nonstationary fuzzy forecasting of wind and wave climate in very long-term scales

Global climate change may have serious impact on human activities in coastal and other areas.Climate change may affect the degree of storminess and,hence,change the wind-driven ocean wave climate.This may affect the risks associated with maritime activities such as shipping and offshore oil and gas.So,there is a recognized need to understand better how climate change will affect such processes.Typically,such understanding comes from future projections of the wind and wave climate from numerical climate models and from the stochastic modelling of such projections.This work investigates the applicability of a recently proposed nonstationary fuzzy modelling to wind and wave climatic simulations.According to this,fuzzy inference models(FIS)are coupled with nonstationary time series modelling,providing us with less biased climatic estimates.Two long-term datasets for an area in the North Atlantic Ocean are used in the present study,namely NORA10(57 years)and ExWaCli(30 years in the present and 30 years in the future).Two distinct experiments have been performed to simulate future values of the time series in a climatic scale.The assessment of the simulations by means of the actual values kept for comparison purposes gives very good results.