scholarly journals Forecasting of Day-Ahead Natural Gas Consumption Demand in Greece Using Adaptive Neuro-Fuzzy Inference System

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2317 ◽  
Author(s):  
Konstantinos Papageorgiou ◽  
Elpiniki I. Papageorgiou ◽  
Katarzyna Poczeta ◽  
Dionysis Bochtis ◽  
George Stamoulis
Keyword(s):  
Neural Networks ◽  
Natural Gas ◽  
Fuzzy Inference ◽  
Inference System ◽  
Hybrid Combination ◽  
Neuro Fuzzy ◽  
Gas Consumption ◽  
Natural Gas Consumption ◽  
Natural Gas Demand ◽  
Consumption Demand

(1) Background: Forecasting of energy consumption demand is a crucial task linked directly with the economy of every country all over the world. Accurate natural gas consumption forecasting allows policy makers to formulate natural gas supply planning and apply the right strategic policies in this direction. In order to develop a real accurate natural gas (NG) prediction model for Greece, we examine the application of neuro-fuzzy models, which have recently shown significant contribution in the energy domain. (2) Methods: The adaptive neuro-fuzzy inference system (ANFIS) is a flexible and easy to use modeling method in the area of soft computing, integrating both neural networks and fuzzy logic principles. The present study aims to develop a proper ANFIS architecture for time series modeling and prediction of day-ahead natural gas demand. (3) Results: An efficient and fast ANFIS architecture is built based on neuro-fuzzy exploration performance for energy demand prediction using historical data of natural gas consumption, achieving a high prediction accuracy. The best performing ANFIS method is also compared with other well-known artificial neural networks (ANNs), soft computing methods such as fuzzy cognitive map (FCM) and their hybrid combination architectures for natural gas prediction, reported in the literature, to further assess its prediction performance. The conducted analysis reveals that the mean absolute percentage error (MAPE) of the proposed ANFIS architecture results is less than 20% in almost all the examined Greek cities, outperforming ANNs, FCMs and their hybrid combination; and (4) Conclusions: The produced results reveal an improved prediction efficacy of the proposed ANFIS-based approach for the examined natural gas case study in Greece, thus providing a fast and efficient tool for utterly accurate predictions of future short-term natural gas demand.

Deterministic Modeling to Predict the Natural Gas Density Using Artificial Neural Networks

2021 ◽  
Author(s):  
Mariam Shreif ◽  
Shams Kalam ◽  
Mohammad Rasheed Khan ◽  
Rizwan Ahmed Khan
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Natural Gas ◽  
Fuzzy Inference ◽  
Ann Model ◽  
Data Set ◽  
Inference System ◽  
Gas Density ◽  
Neuro Fuzzy ◽  
Artificial Neural

Abstract During the past decades, several research studies have been made to unfold the immense and diversified benefits of the innovative applications of machine learning (ML) techniques in the petroleum industry. For instance, machine learning algorithms were applied to estimate the various physical properties of natural gas. Natural gas density is considered an indispensable metric that influences the determination of several variables necessary for analyzing natural gas systems. In this work, the Artificial neural network (ANN), a machine learning technique, was applied to estimate natural gas density incorporating the influencing factors. The ANN model was also compared with another ML technique, namely the Adaptive Neuro-Fuzzy Inference System (ANFIS). A mathematical form has been also presented using ANN. A real data set was taken from the literature, comprised of about 4500 data points assimilating three influencing input variables, including pseudo-reduced pressure (PPr), pseudo-reduced temperature (TPr), and molecular weight (Mw). The PPr and TPr are obtained by calculating the averages of the sample gas critical pressures and critical temperatures. A complicated nonlinear relationship exists between the three influencing variables and the gas density. The data set was divided into a 70:30 ratio for training and testing the model, respectively. Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANN) were applied to train and test the model. Absolute average percentage error (AAPE), coefficient of determination (R2), and root mean squared error (RMSE) were considered in the error metrics to acquire the best possible model. Levenberg–Marquardt backpropagation algorithm was employed for ANN, while subtractive clustering was used for ANFIS. Results showed that natural gas density can be well correlated with numerous inputs using machine learning tools (ANN and ANFIS). The input parameters include Ppr, Tpr, and Mw, as mentioned above. ANN performed better than ANFIS. The network was adjusted against the training sub-set to set-up weights and biases covering each node. R2 for both testing and training data was more than 99%, while AAPE was around 4% for both cases. Moreover, a detailed mathematical scheme for the ANN model is also provided in this paper.

Comparative Analysis of Artificial Neural Networks and Neuro-Fuzzy Models for Multicriteria Demand Forecasting

2013 ◽  
Vol 3 (1) ◽  
pp. 1-24 ◽  
Author(s):  
Golam Kabir ◽  
M. Ahsan Akhtar Hasin
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fuzzy Systems ◽  
Fuzzy Inference ◽  
Demand Forecasting ◽  
Business Environment ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Model Complex ◽  
Artificial Neural

An organization has to make the right decisions in time depending on demand information to enhance the commercial competitive advantage in a constantly fluctuating business environment. Therefore, estimating the demand quantity for the next period most likely appears to be crucial. The objective of the paper is to propose a new forecasting mechanism which is modeled by artificial intelligence approaches including the comparison of both artificial neural networks (ANN) and adaptive network-based fuzzy inference system (FIS) techniques to manage the fuzzy demand with incomplete information. Artificial neural networks has been applied as it is capable to model complex, nonlinear processes without having to assume the form of the relationship between input and output variables. Neuro-fuzzy systems also utilized to harness the power of the fuzzy logic and ANNs through utilizing the mathematical properties of ANNs in tuning rule-based fuzzy systems that approximate the way human’s process information. The effectiveness of the proposed approach to the demand forecasting issue is demonstrated for a 20/25 MVA Distribution Transformer from Energypac Engineering Limited (EEL), a leading power engineering company of Bangladesh.

scholarly journals Prediction of daily streamflow using artificial neural networks (ANNs), wavelet neural networks (WNNs), and adaptive neuro-fuzzy inference system (ANFIS) models

2020 ◽  
Vol 20 (4) ◽  
pp. 1396-1408
Author(s):  
Hüseyin Yıldırım Dalkiliç ◽  
Said Ali Hashimi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Water Resources ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Hydrological Processes ◽  
Inference System ◽  
Daily Streamflow ◽  
Neuro Fuzzy ◽  
Artificial Neural

Abstract In recent years, the prediction of hydrological processes for the sustainable use of water resources has been a focus of research by scientists in the field of hydrology and water resources. Therefore, in this study, the prediction of daily streamflow using the artificial neural network (ANN), wavelet neural network (WNN) and adaptive neuro-fuzzy inference system (ANFIS) models were taken into account to develop the efficiency and accuracy of the models' performances, compare their results and explain their outcomes for future study or use in hydrological processes. To validate the performance of the models, 70% (1996–2007) of the data were used to train them and 30% (2008–2011) of the data were used to test them. The estimated results of the models were evaluated by the root mean square error (RMSE), determination coefficient (R2), Nash–Sutcliffe (NS), and RMSE-observation standard deviation ratio (RSR) evaluation indexes. Although the outcomes of the models were comparable, the WNN model with RMSE = 0.700, R2 = 0.971, NS = 0.927, and RSR = 0.270 demonstrated the best performance compared to the ANN and ANFIS models.

Prediction of Concrete Strength Using Fuzzy Neural Networks

2011 ◽  
Vol 243-249 ◽  
pp. 6121-6126 ◽  
Author(s):  
Jing Xu ◽  
Xiu Li Wang
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Concrete Strength ◽  
Fuzzy Neural Networks ◽  
Output Data ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Fuzzy Neural

The purpose of this paper is to develop the Ⅰ-PreConS (Intelligent PREdiction system of CONcrete Strength) that predicts the compressive strength of concrete to improve the accuracy of concrete undamaged inspection. For this purpose, the system is developed with adaptive neuro－fuzzy inference system (ANFIS) that can learn cube test results as training patterns. ANFIS does not need a specific equation form differ from traditional prediction models. Instead of that, it needs enough input-output data. Also, it can continuously re-train the new data, so that it can conveniently adapt to new data. In the study, adaptive neuro－fuzzy inference system (ANFIS) based on Takagi-Sugeno rules is built up to prediction concrete strength. According to the expert experience, the relationship between the rebound value and concrete strength tends to power function. So the common logarithms of rebound value and strength value are used as the inputs and outputs of the ANFIS. System parameter sets are iteratively adjusted according to input and output data samples by a hybrid-learning algorithm. In the system, in order to improve of the ANFIS, condition parameter sets can be determined by the back propagation gradient descent method and conclusion parameter sets can be determined by the least squares method. As a result, the concrete strength can be inferred by the fuzzy inference. The method takes full advantage of the characteristics of the abilities of Fuzzy Neural Networks (FNN) including automatic learning, generation and fuzzy logic inference. The experiment shows that the average relative error of the predicted results is 10.316% and relative standard error is 12.895% over all the 508 samples, which are satisfied with the requirements of practical engineering. The ANFIS-based model is very efficient for prediction the compressive strength of in-service concrete.

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