scholarly journals Electricity consumption forecast for Tarkwa using autoregressive integrated moving average and adaptive neuro fuzzy inference system

2021 ◽  
Vol 18 (1) ◽  
pp. 75-94
Author(s):  
Robert Ofosu ◽  
Benjamin Odoi ◽  
Mercy Asamoah
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Moving Average ◽  
Electricity Consumption ◽  
Electrical Energy ◽  
Inference System ◽  
Autoregressive Integrated Moving Average ◽  
Independent Variables ◽  
Testing Dataset ◽  
Neuro Fuzzy

Electricity has become one of the inelastic goods in our world today. The proper functioning of most equipment today relies on electricity. Taking Tarkwa which is a mining community into consideration, the various mines, schools, shops, banks and other companies in the municipality massively rely on electricity for their day to day running. Therefore, knowing the exact amount of electricity to produce and distribute for the smooth running of businesses and basic living is of great necessity. This study compared and formulated a model to forecast and predict the daily electrical energy consumption in Tarkwa for the year 2019. The data used was a monthly dataset for the year 2018 and it comprised of the temperature, wind speed, population and electricity consumption for Tarkwa. The methods used were Artificial Neuro-Fuzzy Inference System (ANFIS) and Autoregressive Integrated Moving Average (ARIMA). The ANFIS was used as a predictor to predict the electricity consumption based on the training and testing of the dependent and independent variables. The ARIMA was used to forecast the dependent and independent variables for 2019. These simulations were done using MATLand Minitab. The results of the analysis revealed that the training and testing dataset allowed ANFIS to learn and understand the system but the ANFIS could only forecast the 2019 electricity consumption after the input data to the system was changed to the ARIMA forecasted 2019 independent variables. It was observed that the amount of electricity consumed in 2019 increased by 14%.

scholarly journals Modeling flood discharge at ungauged sites across Turkey using neuro-fuzzy and neural networks

2010 ◽  
Vol 13 (4) ◽  
pp. 842-849 ◽  
Author(s):  
Neslihan Seckin
Keyword(s):  
Neural Network ◽  
Return Period ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Independent Variables ◽  
Maximum Discharge ◽  
Neuro Fuzzy ◽  
Regression Techniques ◽  
Mean Square Errors

One of the most important problems in hydrology is the reliable forecasting of maximum discharge at an ungauged site of interest. Statistical techniques are commonly used for finding the maximum discharge and return period relationship. However, these techniques are generally considered to be inadequate because of the complexity of the problem. Hence, neural network techniques are preferred. In this study, two different neural network models developed based on the following techniques – a multi-layer perceptron neural network with Levenberg–Marquardt algorithm and a radial basis neural network behind an adaptive neuro-fuzzy inference system – are employed in order to capture the nonlinear relationship between discharge and five independent variables – drainage area (km2), elevation (m), latitude, longitude, return period (year) and maximum discharge (m3/s). For a modeling study, watershed data from 543 catchments across Turkey were used. Statistical models with regression techniques were also applied to the same data, providing a wider comparison. The results of the models were then compared and assessed with respect to mean square errors, mean absolute error, mean absolute relative error and determination coefficient. Based on these results, it was found that the neural network techniques demonstrated better performance in predicting the maximum discharge based on five independent variables than the regression techniques, and were comparable to the adaptive neuro-fuzzy inference system.

scholarly journals Estimating daily reference evapotranspiration using available and estimated climatic data by adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN)

2012 ◽  
Vol 44 (1) ◽  
pp. 131-146 ◽  
Author(s):  
Ana Pour-Ali Baba ◽  
Jalal Shiri ◽  
Ozgur Kisi ◽  
Ahmad Fakheri Fard ◽  
Sungwon Kim ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Reference Evapotranspiration ◽  
Climatic Data ◽  
Inference System ◽  
Independent Variables ◽  
Radiation Data ◽  
Neuro Fuzzy ◽  
Artificial Neural

Daily reference evapotranspiration (ET0), as a dependent variable, was estimated for two weather stations in South Korea, using 8 years (1985–1992) of measurements of independent variables of air temperature, sunshine hours, wind speed and relative humidity. The model uses the adaptive neuro-fuzzy inference system (ANFIS) and artificial neural networks (ANNs) for estimating daily ET0. In the first part of the study, the applied models were trained, tested and validated using various combinations of the recorded independent variables, which corresponded to the Hargreaves–Samani, Priestly–Taylor and FAO56-PM equations. The goodness of fit for the models was evaluated in terms of the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE) and Nash–Sutcliffe coefficient (NS). In the second part of the study, the estimated solar radiation data were applied as input parameters (for the same input combinations, as the first part), instead of recorded sunshine values. The results indicated that the both applied ANFIS and ANN models performed quite well in ET processes from the available climatic data. The results also showed that the application of estimated solar radiation data instead of the recorded sunshine values decreases the models’ accuracy.

scholarly journals Comparative Study of Daily Rainfall Forecasting Models Using Adaptive-Neuro Fuzzy Inference System (ANFIS)

2015 ◽  
Vol 10 (2) ◽  
pp. 529-536 ◽  
Author(s):  
M. A Sojitra ◽  
P. A Pandya
Keyword(s):  
Relative Humidity ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Moving Average ◽  
Daily Rainfall ◽  
Rainfall Forecasting ◽  
Inference System ◽  
Forecasting Models ◽  
Mean Temperature ◽  
Neuro Fuzzy

The study was carried out to develop rainfall forecasting Models. Adaptive Neuro-Fuzzy Inference System (ANFIS) was used for developing Models rainfall of Udaipur city. Two data sets were prepared using 35 year of weather parameters i.e. wet bulb temperature, mean temperature, relative humidity and evaporation of previous day and previous moving average week were used to prepare case I and case II respectively. Gaussian and Generalized Bell membership functions were used to prepare models. Statistical and hydrologic performance indices of ANFIS (Gaussian, 5) gave better performance among developed four models. The study showed that sensitivity analysis revealed wet bulb temperature is most sensible parameter followed by mean temperature, relative humidity and evaporation.

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