Projection of future transport energy demand of Jordan using adaptive neuro-fuzzy technique

Al-Ghandoor, A.; Samhouri, Murad; Al‐Hinti, I.; Jaber, J.O.; Al-Rawashdeh, Mohammad

doi:10.1016/j.energy.2011.12.023

Cited by 49 publications

(15 citation statements)

References 18 publications

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“…The results of that study showed that use of ANN involves far less error, in comparison with regression method due to its flexibility and intelligibility, and the model could be used for policy making and forecasting purposes. To model and forecast the transportation sector energy consumption for Jordan for two decades, Al-Ghandoor proposed an adaptive neuro-fuzzy inference system along with the double exponential smoothing techniques [4]. The model was developed according to socioeconomic and transport related indicators based on annual VP, INC (GDP/capita) level, and FE.…”

Section: Introductionmentioning

confidence: 99%

General procedure for long-term energy-environmental planning for transportation sector of developing countries with limited data based on LEAP (long-range energy alternative planning) and EnergyPLAN

Sadri

Ardehali

Amirnekooei

2014

Energy

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Section: Introductionmentioning

confidence: 99%

General procedure for long-term energy-environmental planning for transportation sector of developing countries with limited data based on LEAP (long-range energy alternative planning) and EnergyPLAN

Sadri

Ardehali

Amirnekooei

2014

Energy

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“…Following training, the ANFIS model for forecasting Australia's LCCs enplaned passengers and RPKs was validated by selecting six data points, which are different from the other 36 points used for the ANFIS training (Al-Ghandoor et al 2012). Each validation data point was fed into the system and then the Australia's predicted LCCs enplaned passengers and RPKs values were computed and compared to the actual values.…”

Section: Anfis Modelling Resultsmentioning

confidence: 99%

An Adaptive Neuro-Fuzzy Inference System for Forecasting Australia’s Domestic Low Cost Carrier Passenger Demand

2015

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Abstract. This study has proposed and empirically tested two Adaptive Neuro-Fuzzy Inference System (ANFIS) models for the first time for predicting Australia's domestic low cost carriers' demand, as measured by enplaned passengers (PAX Model) and revenue passenger kilometres performed (RPKs Model). In the ANFIS, both the learning capabilities of an artificial neural network (ANN) and the reasoning capabilities of fuzzy logic are combined to provide enhanced prediction capabilities, as compared to using a single methodology. Sugeno fuzzy rules were used in the ANFIS structure and the Gaussian membership function and linear membership functions were also developed. The hybrid learning algorithm and the subtractive clustering partition method were used to generate the optimum ANFIS models. Data was normalized in order to increase the model's training performance. The results found that the mean absolute percentage error (MAPE) for the overall data set of the PAX and RPKs models was 1.52% and 1.17%, respectively. The highest R 2 -value for the PAX model was 0.9949 and 0.9953 for the RPKs model, demonstrating that the models have high predictive capabilities.

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“…Al-Ghandoor et al [3] illustrated a new approach to model and forecast the transport energy demand of Jordan based on Adaptive Neuro-Fuzzy Inference System (ANFIS) and the double exponential smoothing techniques. The ANFIS model was developed using socio-economic and transport related indicators based on annual number of vehicles, vehicle ownership level, income level, and fuel prices in Jordan.…”

Section: Review Of Literaturementioning

confidence: 99%

Energy’s exports forecasting by a neuro-fuzzy controller

2015

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Since accurate forecasting of energy export is very important for planning potential energy demand and improving the energy production sector, various forecasting methods have been developed. The present work is focused to apply a novel technique, an integrated neuro-fuzzy controller named PATSOS. The forecasting system is based on two Adaptive Neural Fuzzy Inference Systems (ANFIS) that form an inverse controller. An ANFIS model represents the controller and another ANFIS represents the energy export model that is going to be controlled. ANFIS uses a combination of the least-squares method and the backpropagation gradient descent method to estimate the optimal energy export forecast parameters. The ANFIS controller belongs to direct control and is based on inverse learning, also known as general learning. Hourly data sets during the period were used to learn and evaluate the proposed system. The forecast accuracy of the proposed technique was evaluated using out of sample tests. The results of the simulation based on statistical errors and the experimental investigations carried out on the laboratory showed that the model despite the high data volatility, is suitable for forecasting hourly energy exports.

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Projection of future transport energy demand of Jordan using adaptive neuro-fuzzy technique

Cited by 49 publications

References 18 publications

General procedure for long-term energy-environmental planning for transportation sector of developing countries with limited data based on LEAP (long-range energy alternative planning) and EnergyPLAN

General procedure for long-term energy-environmental planning for transportation sector of developing countries with limited data based on LEAP (long-range energy alternative planning) and EnergyPLAN

An Adaptive Neuro-Fuzzy Inference System for Forecasting Australia’s Domestic Low Cost Carrier Passenger Demand

Energy’s exports forecasting by a neuro-fuzzy controller

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