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>

Research and application of novel Euler polynomial-driven grey model for short-term PM10 forecasting

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xiwang Xiang ◽  
Xin Ma ◽  
Minda Ma ◽  
Wenqing Wu ◽  
Lang Yu
Keyword(s):  
Real World ◽  
Pm10 Concentration ◽  
Euler Polynomial ◽  
Grey Model ◽  
Grey System ◽  
Short Term ◽  
Content Type ◽  
New Model ◽  
Proposed Model ◽  
Grey Models

PurposePM10 is one of the most dangerous air pollutants which is harmful to the ecological system and human health. Accurate forecasting of PM10 concentration makes it easier for the government to make efficient decisions and policies. However, the PM10 concentration, particularly, the emerging short-term concentration has high uncertainties as it is often impacted by many factors and also time varying. Above all, a new methodology which can overcome such difficulties is needed.Design/methodology/approachThe grey system theory is used to build the short-term PM10 forecasting model. The Euler polynomial is used as a driving term of the proposed grey model, and then the convolutional solution is applied to make the new model computationally feasible. The grey wolf optimizer is used to select the optimal nonlinear parameters of the proposed model.FindingsThe introduction of the Euler polynomial makes the new model more flexible and more general as it can yield several other conventional grey models under certain conditions. The new model presents significantly higher performance, is more accurate and also more stable, than the six existing grey models in three real-world cases and the case of short-term PM10 forecasting in Tianjin China.Practical implicationsWith high performance in the real-world case in Tianjin China, the proposed model appears to have high potential to accurately forecast the PM10 concentration in big cities of China. Therefore, it can be considered as a decision-making support tool in the near future.Originality/valueThis is the first work introducing the Euler polynomial to the grey system models, and a more general formulation of existing grey models is also obtained. The modelling pattern used in this paper can be used as an example for building other similar nonlinear grey models. The practical example of short-term PM10 forecasting in Tianjin China is also presented for the first time.

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 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 Generalized Support Vector Machines (GSVMs) model for real-world time series forecasting

2021 ◽  
Author(s):  
Mehrnaz Ahmadi ◽  
Mehdi Khashei
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machines ◽  
Real World ◽  
Support Vector ◽  
Data Sets ◽  
Fuzzy Support Vector Machine ◽  
Data Set ◽  
Proposed Model ◽  
Vector Machines ◽  
Forecasting Performance

Abstract Support vector machines (SVMs) are one of the most popular and widely-used approaches in modeling. Various kinds of SVM models have been developed in the literature of prediction and classification in order to cover different purposes. Fuzzy and crisp support vector machines are a well-known branch of modeling approaches that frequently applied for certain and uncertain modeling, respectively. However, each of these models can only be efficiently used in its specified domain and cannot yield appropriate and accurate results if the opposite situations have occurred. While the real-world systems and data sets often contain both certain and uncertain patterns that are complicatedly mixed together and need to be simultaneously modeled. In this paper, a generalized support vector machine (GSVM) is proposed that can simultaneously benefit the unique advantages of certain and uncertain versions of the traditional support vector machines in their own specialized categories. In the proposed model, the underlying data set is first categorized into two classes of certain and uncertain patterns. Then, certain patterns are modeled by a support vector machine, and uncertain patterns are modeled by a fuzzy support vector machine. After that, the function of the relationship, as well as the relative importance of each component, are estimated by another support vector machine, and subsequently, the final forecasts of the proposed model are calculated. Empirical results of wind speed forecasting indicate that the proposed method not only can achieve more accurate results than support vector machines (SVMs) and fuzzy support vector machines (FSVMs) but also can yield better forecasting performance than traditional fuzzy and nonfuzzy single models and traditional preprocessing-based hybrid models of SVMs.

scholarly journals Forecasting China’s per Capita Living Energy Consumption by Employing a Novel DGM (1, 1, tα) Model with Fractional Order Accumulation

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yumu Lu ◽  
Chong Liu ◽  
Haodan Pang ◽  
Ting Feng ◽  
Zijie Dong
Keyword(s):  
Energy Consumption ◽  
Fractional Order ◽  
Prediction Models ◽  
Grey Model ◽  
Grey Prediction ◽  
Grey System ◽  
Annual Increment ◽  
Model Based ◽  
Proposed Model ◽  
Per Capita

The living energy consumption of residents has become an important technical index to promote the economic and social development strategy. The country’s medium- and short-term living energy consumption is featured with both a certainty of annual increment and an uncertainty of random variation. Thus, it can be seen as a typical grey system and shall be suitable for the grey prediction model. In order to explore the future development trend of China’s per capita living energy consumption, this paper establishes a novel grey model based on the discrete grey model with time power term and the fractional accumulation (FDGM (1, 1, tα) for short) for forecasting China’s per capita living energy consumption, which makes the existing model to adapt to different time series by adjusting fractional order accumulation parameter and power term. In order to verify the feasibility and effectiveness of the novel model, the proposed and eight other existing grey prediction models are applied to the case of China’s per capita living energy consumption. The results show that the proposed model is more suitable for predicting China’s per capita energy consumption than the other eight grey prediction models. Finally, the proposed model based on metabolism mechanism is used to predict China’s per capita living energy consumption from 2018 to 2029, which can provide a reference for energy companies or government decision makers.

scholarly journals Forecasting CO2 Emissions Using a Novel Conformable Fractional Order Discrete Grey Model

2021 ◽  
Author(s):  
Peng Zhu ◽  
Wanli Xie ◽  
Yunshen Shi ◽  
Mingyong Pang ◽  
Yuhui Shi
Keyword(s):  
Carbon Dioxide ◽  
Fractional Order ◽  
Carbon Dioxide Emissions ◽  
Industrial Development ◽  
Grey Model ◽  
Optimal Order ◽  
Low Carbon ◽  
Grey System ◽  
Industrial Carbon ◽  
Proposed Model

Abstract Accurate and scientific forecasting of carbon dioxide emissions will help make better industrial carbon emission planning so as to promote low-carbon industrial development and achieve sustainable economic growth. For depressing the disturbance of various elements, grey system-based models play an important role in forecasting science. In this paper, we extend the cumulative order from integer order to fractional order based on the discrete gray model, which we call CFDGM (1,1). After introducing the free quantity of the model order, the accuracy of the prevenient grey-based models can be further enhanced. We selected the data for carbon dioxide production by Germany, Japan, and Thailand for modeling. To obtain the optimal order of our grey model, we selected four optimizers to search for the order. The results show that although the search history of the four types of optimizers is different, the search results are the same, which proves that the four types of optimizers are stable and reliable, and the order for which we searched is reliable. By substituting the optimal order into CFDGM (1,1), we obtained the fitting and prediction error of the proposed model. The final results show that a satisfactory fitting effect and forecasting effect is obtained by our proposed model.

Prediction of Technical Reserves Based on Grey Model — GM(1,1): Evidence from Non-life Egyptian Insurance Market

2019 ◽  
Vol 10 (9) ◽  
pp. 852-860
Author(s):  
Mahmoud Elsayed ◽  
◽  
Amr Soliman ◽  
Keyword(s):  
System Theory ◽  
Grey System Theory ◽  
Insurance Market ◽  
Grey Model ◽  
Loss Ratio ◽  
Grey System ◽  
Mathematical Technique ◽  
Supervisory Authority ◽  
Error Ratio ◽  
Using Data

Grey system theory is a mathematical technique used to predict data with known and unknown characteristics. The aim of our research is to forecast the future amount of technical reserves (outstanding claims reserve, loss ratio fluctuations reserve and unearned premiums reserve) up to 2029/2030. This study applies the Grey Model GM(1,1) using data obtained from the Egyptian Financial Supervisory Authority (EFSA) over the period from 2005/2006 to 2015/2016 for non-life Egyptian insurance market. We found that the predicted amounts of outstanding claims reserve and loss ratio fluctuations reserve are highly significant than the unearned premiums reserve according to the value of Posterior Error Ratio (PER).

Human Activity Recognition using Fourier Transform Inspired Deep Learning Combination Model

2019 ◽  
Vol 9 (1) ◽  
pp. 16-31
Author(s):  
Kyungkoo Jun
Keyword(s):  
Fourier Transform ◽  
Deep Learning ◽  
Short Term Memory ◽  
Window Size ◽  
Sensor Data ◽  
Data Sets ◽  
Data Set ◽  
Proposed Model ◽  
Testing Data ◽  
Labeling Scheme

Background & Objective: This paper proposes a Fourier transform inspired method to classify human activities from time series sensor data. Methods: Our method begins by decomposing 1D input signal into 2D patterns, which is motivated by the Fourier conversion. The decomposition is helped by Long Short-Term Memory (LSTM) which captures the temporal dependency from the signal and then produces encoded sequences. The sequences, once arranged into the 2D array, can represent the fingerprints of the signals. The benefit of such transformation is that we can exploit the recent advances of the deep learning models for the image classification such as Convolutional Neural Network (CNN). Results: The proposed model, as a result, is the combination of LSTM and CNN. We evaluate the model over two data sets. For the first data set, which is more standardized than the other, our model outperforms previous works or at least equal. In the case of the second data set, we devise the schemes to generate training and testing data by changing the parameters of the window size, the sliding size, and the labeling scheme. Conclusion: The evaluation results show that the accuracy is over 95% for some cases. We also analyze the effect of the parameters on the performance.

scholarly journals Hfinger: Malware HTTP Request Fingerprinting

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 507
Author(s):  
Piotr Białczak ◽  
Wojciech Mazurczyk
Keyword(s):  
Real World ◽  
Network Traffic ◽  
Experimental Evaluation ◽  
Data Sets ◽  
Real World Data ◽  
Malicious Software ◽  
Default Mode ◽  
World Data ◽  
Effectiveness Analysis ◽  
Http Protocol

Malicious software utilizes HTTP protocol for communication purposes, creating network traffic that is hard to identify as it blends into the traffic generated by benign applications. To this aim, fingerprinting tools have been developed to help track and identify such traffic by providing a short representation of malicious HTTP requests. However, currently existing tools do not analyze all information included in the HTTP message or analyze it insufficiently. To address these issues, we propose Hfinger, a novel malware HTTP request fingerprinting tool. It extracts information from the parts of the request such as URI, protocol information, headers, and payload, providing a concise request representation that preserves the extracted information in a form interpretable by a human analyst. For the developed solution, we have performed an extensive experimental evaluation using real-world data sets and we also compared Hfinger with the most related and popular existing tools such as FATT, Mercury, and p0f. The conducted effectiveness analysis reveals that on average only 1.85% of requests fingerprinted by Hfinger collide between malware families, what is 8–34 times lower than existing tools. Moreover, unlike these tools, in default mode, Hfinger does not introduce collisions between malware and benign applications and achieves it by increasing the number of fingerprints by at most 3 times. As a result, Hfinger can effectively track and hunt malware by providing more unique fingerprints than other standard tools.

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