scholarly journals Advance artificial time series forecasting model for oil production using neuro fuzzy-based slime mould algorithm

2021 ◽  
Author(s):  
Ayman Mutahar AlRassas ◽  
Mohammed A. A. Al-qaness ◽  
Ahmed A. Ewees ◽  
Shaoran Ren ◽  
Renyuan Sun ◽  
...  
Keyword(s):  
Time Series ◽  
Fuzzy Inference ◽  
Oil Production ◽  
Time Series Forecasting ◽  
Forecasting Model ◽  
Local Optima ◽  
Inference System ◽  
Slime Mould ◽  
Opposition Based Learning ◽  
Neuro Fuzzy

AbstractOil production forecasting is an important task to manage petroleum reservoirs operations. In this study, a developed time series forecasting model is proposed for oil production using a new improved version of the adaptive neuro-fuzzy inference system (ANFIS). This model is improved by using an optimization algorithm, the slime mould algorithm (SMA). The SMA is a new algorithm that is applied for solving different optimization tasks. However, its search mechanism suffers from some limitations, for example, trapping at local optima. Thus, we modify the SMA using an intelligence search technique called opposition-based learning (OLB). The developed model, ANFIS-SMAOLB, is evaluated with different real-world oil production data collected from two oilfields in two different countries, Masila oilfield (Yemen) and Tahe oilfield (China). Furthermore, the evaluation of this model is considered with extensive comparisons to several methods, using several evaluation measures. The outcomes assessed the high ability of the developed ANFIS-SMAOLB as an efficient time series forecasting model that showed significant performance.

A hybrid neuro-fuzzy inference system-based algorithm for time series forecasting applied to energy consumption prediction

2020 ◽  
Vol 268 ◽  
pp. 114977 ◽  
Author(s):  
Mohammed Ali Jallal ◽  
Aurora González-Vidal ◽  
Antonio F. Skarmeta ◽  
Samira Chabaa ◽  
Abdelouhab Zeroual
Keyword(s):  
Time Series ◽  
Energy Consumption ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Time Series Forecasting ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

Photovoltaic power forecasting using wavelet Neuro-Fuzzy for active solar trackers

2021 ◽  
Vol 40 (1) ◽  
pp. 1083-1096
Author(s):  
Stéfano Frizzo Stefenon ◽  
Christopher Kasburg ◽  
Roberto Zanetti Freire ◽  
Fernanda Cristina Silva Ferreira ◽  
Douglas Wildgrube Bertol ◽  
...  
Keyword(s):  
Time Series ◽  
Fuzzy Inference ◽  
Electric Energy ◽  
Time Series Forecasting ◽  
Angle Of Incidence ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Wide Range ◽  
Generation Capacity ◽  
The Difference

The generation of electric energy by photovoltaic (PV) panels depends on many parameters, one of them is the sun’s angle of incidence. By using solar active trackers, it is possible to maximize generation capacity through real-time positioning. However, if the engines that update the position of the panels use more energy than the difference in efficiency, the solar tracker system becomes ineffective. In this way, a time series forecasting method can be assumed to determine the generation capacity in a pre-established horizon prediction to evaluate if a position update would provide efficient results. Among a wide range of algorithms that can be used in forecasting, this work considered a Neuro-Fuzzy Inference System due to its combined advantages such as smoothness property from Fuzzy systems and adaptability property from neural networks structures. Focusing on time series forecasting, this article presents a model and evaluates the solar prediction capacity using the Wavelet Neuro-Fuzzy algorithm, where Wavelets were included in the model for feature extraction. In this sense, this paper aims to evaluate whether it is possible to obtain reasonable accuracy using a hybrid model for electric power generation forecasting considering solar trackers. The main contributions of this work are related to the efficiency improvement of PV panels. By assuming a hybrid computational model, it is possible to make a forecast and determine if the use of solar tracking is interesting during certain periods. Finally, the proposed model showed promising results when compared to traditional Nonlinear autoregressive model structures.

scholarly journals Optimized ANFIS Model Using Aquila Optimizer for Oil Production Forecasting

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1194
Author(s):  
Ayman Mutahar AlRassas ◽  
Mohammed A. A. Al-qaness ◽  
Ahmed A. Ewees ◽  
Shaoran Ren ◽  
Mohamed Abd Abd Elaziz ◽  
...  
Keyword(s):  
Time Series ◽  
Optimization Algorithm ◽  
Fuzzy Inference ◽  
Oil Production ◽  
Anfis Model ◽  
Inference System ◽  
Production Forecasting ◽  
Neuro Fuzzy ◽  
Hybrid Intelligence ◽  
Real World Datasets

Oil production forecasting is one of the essential processes for organizations and governments to make necessary economic plans. This paper proposes a novel hybrid intelligence time series model to forecast oil production from two different oil fields in China and Yemen. This model is a modified ANFIS (Adaptive Neuro-Fuzzy Inference System), which is developed by applying a new optimization algorithm called the Aquila Optimizer (AO). The AO is a recently proposed optimization algorithm that was inspired by the behavior of Aquila in nature. The developed model, called AO-ANFIS, was evaluated using real-world datasets provided by local partners. In addition, extensive comparisons to the traditional ANFIS model and several modified ANFIS models using different optimization algorithms. Numeric results and statistics have confirmed the superiority of the AO-ANFIS over traditional ANFIS and several modified models. Additionally, the results reveal that AO is significantly improved ANFIS prediction accuracy. Thus, AO-ANFIS can be considered as an efficient time series tool.

scholarly journals PEMILIHAN INPUT MODEL ANFIS UNTUK DATA RUNTUN WAKTU MENGGUNAKAN METODE FORWARD SELECTION DILENGKAPI GUI MATLAB (Studi Kasus: Jumlah Penumpang Kereta Api di Wilayah Jawa Non Jabodetabek)

2019 ◽  
Vol 8 (2) ◽  
pp. 233-243
Author(s):  
Tiara Sukma Valentina ◽  
Tarno Tarno ◽  
Alan Prahutama
Keyword(s):  
Time Series ◽  
Fuzzy Inference ◽  
Selection Method ◽  
Regression Method ◽  
System Model ◽  
Forward Selection ◽  
Simulation Data ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Input Model

One of the methods that is commonly used to identify a time series model and input ANFIS (Adaptive Neuro Fuzzy Inference System) model is PACF plot. The PACF plot shows the correlation between current observations and previous observations visually. Formally there are several methods that are known to effectively identify ANFIS inputs, one of which is the Forward Selection regression method. With the same concept as PACF, the process of selecting ANFIS inputs using the Forward Selection method is based on the order of the correlatiom between the predictors of the response which is indicated by the magnitude of the correlation coefficient. This study discusses the Forward Selection method in simulation data that has stationary characteristics, stationary with outliers, non stationary, non stationary with outliers and implements data on the number of train passengers in the Non Jabodetabek Java region. ANFIS modeling on data of the number of train passengers in the Non Jabodetabek Java region produces AIC of 15,5617, MAPE of 8,5093% and RMSE of 571,33691. The result of this study is equipped with a GUI which is useful as a tool to facilitate users in processing data.Keywords : PACF Plot, Forward Selection, ANFIS, non stasionary, outlier

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